text:
SC19, the annual gathering of the advanced computing cognoscenti, many sporting shirts festooned with corporate and institutional logos and geek jokes, is now underway in Denver, Colorado. After arriving from the outskirts of western Nebraska and Kansas (i.e., Denver International Airport), high-performance computing fans are descending on the Colorado Convention Center in downtown Denver, eager to discuss new server design, interconnect advances, and software, while surfing the artificial intelligence tsunami that now consumes all things computing, whether exascale or edge, bare metal or containerized.
I will soon be at SC19 myself, visiting with all my geeky friends, after being at the National Science Board and having dinner with the new PCAST members, but this essay is about none of that. Rather, I want to highlight two recent reports, both of which outline the need for a rethinking of our approaches. Put another way, Feynman's 1959 "go small" clarion call in There's Plenty of Room at the Bottom has been answered, and there's not much more room at the inn, for CMOS FinFETs are down to small numbers of atoms.
Concurrently, we are encountering fabrication and economic challenges below 7 nanometers, though there is still progress to be made. As I told Nature in 2016, I think we will run out of money before we run out of physics. (See The Chips Are Down for Moore's Law.) Regardless, innovation will continue, albeit along other axes. (See Nothing Lasts Forever for musings on how aircraft innovation has morphed from a focus on speed.)
Last week, the U.S. Department of Energy released the Basic Research Needs for Microelectronics report, which many of us have been editing and writing for the past year. I was pleased to co-chair the effort with Cherry Murray (Harvard/Arizona) and Supratik Guha (Argonne National Laboratory). The report, a collaboration among three Department of Energy offices – Advanced Scientific Computing Research (ASCR), Basic Energy Sciences (BES), and High Energy Physics (HEP) – highlights five critical needs:
Last week, the Fast Track Action Committee on Strategic Computing, part of the U.S. National Science and Technology Council (NSTC), released the National Strategic Computing Initiative Update: Pioneering the Future of Computing. The report's opening sentence echoes many of the points in the Department of Energy study, noting,
The national computing landscape is undergoing rapid evolution along multiple dimensions due to the introduction of new and potentially disruptive technologies and the demands of new classes of data intensive applications. Computer architectures and systems are more heterogeneous and complex, and the challenges associated with the complexity and sustainability of software are significant.
The NSTC strategic computing report recommendations include
There's trouble in River City. The free ride from Dennard scaling is over; the von Neumann architecture is showing its age; and the hierarchical abstractions we have so cherished are now an intellectual box from which we must escape.
It's time for ab initio, integrative, transdisciplinary thinking, as I discussed in Renaissance Teams: Reifying the School at Athens.
SC17, the annual gathering of the supercomputing community, was held in mid-November, the week before U.S. Thanksgiving. As I mentioned in a previous post (Come to the Supercomputing Big Data Edge), we presented the results of the Big Data and Extreme-scale Computing (BDEC) workshops on the technical and social convergence of big data, machine learning, high-performance computing, and computing hardware and software. You can find the report here. At SC17, we had a lively discussion about edge computing, global competitiveness, and the nature of scientific revolutions.
In addition to the BDEC discussion, I also chaired an SC17 panel on the energy efficiency of HPC data centers. The panel was organized with the help of the Energy Efficient High Performance Computing Working Group (EEHPCWG). The panel participants were Sadaf Alam (CSCS/ETH), Bill Gropp (NCSA/Illinois), Satoshi Matsuoka (GSIC/Tokyo Tech), and John Shalf (NERSC/LBL). You can find the slides and a video of the panel here, courtesy of InsideHPC.
Finally, I was recently a guest on Iowa Public Radio's lunchtime conversation, River to River. We had a lively and thoughtful conversation about the potential social consequences of autonomous vehicles. You can listen to that audio here.
SC17, the annual gathering of the supercomputing community is nigh. As always, there will be talk of next-generation technology – hardware, software, algorithms – and new applications. All of this will take place against the backdrop of the race to exascale computing – bigger and bigger, faster and faster. We will all geek out, and we will love it.
While we discuss operations/joule, quantum and neuromorphic computing, and new and exotic memory technologies to enable ever faster systems, there is another computing revolution underway, at the other extreme. As Richard Feynman famously said about nanoscale devices, "There's plenty of room at the bottom." Folks, I'm talking edge devices, the world of streaming data sensors that is changing the nature of computing and increasingly, of science as well. This is the subject of the forthcoming Big Data and Extreme-scale Computing (BDEC) report.
The history of computing is one of punctuated equilibrium, with each pendulum swing, from centralized to distributed, bringing new and unexpected changes. It has also been a story of disruption from below, as successive generations brought order of magnitude shifts in price-performance ratios and sales volumes. Mainframes beget minicomputers, which begat workstations, which begat personal computers, which begat smartphones. The same has been true in scientific computing. Monolithic supercomputers (think the original Cray series) gave way to symmetric multiprocessors (SMPs) and distributed memory supercomputers. These gave way to clusters, now augmented with accelerators. Mind you, I still miss the lights on the Thinking Machines CM-5.
In both technical and "mainstream" computing, our largest systems are of unprecedented scale, whether globe spanning networks of massive cloud data centers or petascale and soon-to-be exascale HPC systems. As the Internet of Things sweeps across the computing landscape like a tsunami, one should ask about its analog in technical computing – the burgeoning networks of sensors that now capture science, engineering, environment, health and safety, and social data. (See Mobility, Transduction, and Garages)
Today's smartphone-cloud ecosystem places most of the intelligence and data in the cloud. Indeed, all of the intelligent assistants – Siri, Cortina, and others – run in the cloud, dependent on precious wireless network bandwidth and relying on both behavioral history and deep learning algorithms. Limited by smartphone processing speeds, memory capacity, power budgets, and battery lifetime, and human expectations and physiology, there are good economic and technical reasons for this design.
There are also disadvantages, particularly in science. First, transmitting raw, time sensitive data to central sites places major demands on national and international networks. When the Large Hadron Collider (LHC) was under construction, this bandwidth and data processing demand was a major concern, triggering an international collaboration to build a distributed data storage hierarchy; those concerns remain as luminosity upgrades to the HLC are being planned.
The planned data pipeline for the Large Synoptic Survey Telescope (LSST) will send the raw data from the telescope in Chile to a dedicated supercomputer at NCSA, with a sixty second (maximum) delay before global alerts of transient phenomena must be distributed. Although this centralized approach can and will work when the daily data volume is measured in terabytes, the data deluge of the Square Kilometer Array (SKA) will require in situ reduction.
My friends in Chicago's Array of Things project grappled with another aspect of this local/remote tradeoff when designing urban sensors to capture and analyze human interactions. Communities were happy to support social dynamics analysis, but they did not want Big Brother video to leave the sensors. The privacy and social constraint requires low power, high-performance machine learning solutions on the sensors themselves.
Centralization also brings operational and security risks. My former Microsoft colleague, James Hamilton (now at Amazon), when asked why cloud data centers are not even bigger, said, "… the cost savings from scaling a single facility without bound are logarithmic, whereas the negative impact of blast radius is linear." We have all experienced the blast radius effect, at least metaphorically; when a cloud data center goes down, it affects both social networks and business, and it is international news. There is, perhaps, a salutatory lesson here for the ultimate, maximum size of supercomputers, but that is a discussion for another day.
In addition, the stateless design of the venerable TCP/IP stack includes no notion of data geo-location or intermediate data processing. Everyone who has debugged "last mile" network performance problems knows this all too well. To remediate this, research groups and companies have designed a variety of domain-specific network overlays, data caches, publisher-subscriber systems, and content delivery networks (CDNs).
Dramatic declines in computing costs mean it is now possible to deploy truly large numbers of inexpensive sensors. How dramatic? I have posed the following question on my campus. What would you do with 10,000, disposable ~$10 sensors? I was met with surprise at the scale of my question. For many of us, even those who came of age in the smartphone era, this scaling is a difficult question to answer because it defies our worldview of the possible. It is not a science fiction fantasy; it is a present reality.
A traditional hub and spoke model of data storage and sensor data analysis is increasingly neither scalable nor reliable. Amazon's Greengrass architecture is an early example of a generic, distributed approach, while NVidia's Metropolis system targets video in smart cities. There is an even more general model, where generic services support distributed data caching and processing throughout the network. Micah Beck (Tennessee) has made a thoughtful case for the design of such an approach, which he calls an Exposed Buffer Processing Architecture. Similarly, the Array of Things leaders have argued that edge processing is essential when
Simply put, these requirements imply a much more complex, distributed data storage and processing architecture for data-driven scientific computing than we have developed to date.
This brave new world of streaming data has profound implications for the future of supercomputing. Here are just a few:
As the name implies, the Big Data and Extreme-scale Computing (BDEC) project has been examining this nexus and the changing nature of scientific discovery enabled by the fusion of big data – from sensors large and small – machine learning, and computational modeling. It is a rapidly shifting environment, with new hardware and software ecosystems evolving rapidly.
Much of it remains largely unknown to the traditional HPC community, just as software containerization and machine learning were just a few years ago. That will change very soon. If you are attending SC17, come to the Birds of a Feather (BOF) discussion, late Wednesday, November 15.
In his book, Hell's Angels, the inimitable gonzo journalist Hunter S. Thompson once wrote about living on the edge – riding flat out – in the dark – on a motorcycle:
The Edge…There is no honest way to explain it because the only people who really know where it is are ones who have gone over. The others–the living–are those who pushed their control as far as they felt they could handle it, and then pulled back, or slowed down, or did whatever they had to when it came time to choose between Now and Later.
In supercomputing, we have always felt the need for speed, exploiting every new technology in the endless search for higher performance and greater capacity. It's time for the supercomputing community to come to the edge, where sensors and streaming data meet HPC, computational models, and machine learning.
There's lots of room at the edge for innovation and discovery. Let's not crash in the dark.
When Larry Smarr, the founding director of the National Center for Supercomputing Applications (NCSA), quietly confided to me in the fall of 1999 that he was moving the UCSD, I was Head of the Department of Computer Science during the midst of the first dot.com boom, itself enabled by NCSA Mosaic and the first web revolution. Netscape and its Illinois alumni were remaking Silicon Valley; Max Levchin had just founded PayPal while a student in the department; alumnus Tom Siebel was flying high with new CRM technology at Siebel Systems; and we had just celebrated HAL's birthday (from 2001: A Space Odyssey) with a massive Cyberfest event that attracted worldwide attention. ("I am a HAL 9000 computer, production number 3; I became operational at the HAL plant in Urbana Illinois on January 12, 1997.")
Students were beating down the doors, begging to get into computer science; we were hiring faculty at a frantic pace, unable to keep up with demand; venture capitalists were calling, and virtual reality (VR) was all the rage. In many ways, it was much like today's deep learning and VR boom, powered by cloud services and big data. As Yogi Berra might say, it's like déjà vu all over again, as we consistently underestimate the power of computing to reshape our society.
When Larry and I talked about the future and the NCSA directorship in the fall of 1999, I already had had a long association with NCSA. I had worked on joint research projects, and I had done some of the early visualization of web traffic when NCSA's web server had been the world's busiest. I was also the leader of the data management team – what we would now call big data – for the National Computational Science Alliance (the Alliance), which was anchored by NCSA as part of the NSF Partnerships for Advanced Computational Infrastructure (PACI) program. Based on that conversation with Larry, it was clear that both NCSA and my life were about to change in some profound ways. However, despite the universal sense that computing was a revolutionary force, none of us could have predicted just how profound those changes would be. I am very proud of the NCSA team and humbled by what they accomplished.
In the space of four years, we broke ground on three new buildings, deployed the first Linux clusters in national allocation by NSF, negotiated creation of a 40 gigabit per second transcontinental optical network for research, developed an early GPU cluster based on Sony PlayStation 2's, launched the NSF TeraGrid, and committed to support the Large Synoptic Survey Telescope (LSST). In between all that, I joined the President's Information Technology Advisory Committee (PITAC), there were national meetings (the High-end Computing Revitalization Task Force (HECRTF)) and Congressional hearings about the future of supercomputing, and plans for petascale systems set stage for what would become Blue Waters. It was, in a very real way, an inflection point for the nature of computational science and scientific computing that continues today.
At the end of the 1990s, custom-built, parallel systems (MPPs) such as the Thinking Machines CM5 and SGI Origin 2000 dominated high-performance computing, having displaced vector machines such as the Cray Y-MP. As commodity PCs increased in performance, NCSA and the Alliance began experimenting with home-built Linux clusters, and in 2001, we deployed the first two of these – a one teraflop IA-32 cluster and one teraflop IA-64 Itanium cluster.
As strange as it may seem now, with Linux clusters the unquestioned standard for scientific computing, this was then viewed as a radical and highly risky decision. People often asked me, "Who do you call for support when something fails?" and "Who is responsible for the software?" The answers, of course, were that we were galvanizing a community and building a new support model, as we reinvented the very nature of scientific computing. NCSA led the way, as it has throughout its history.
What did not happen is not as well known. We had an opportunity to launch a big data initiative, in partnership with Microsoft. In 2000, secured a contingent $40M gift from Microsoft for a Windows-based data analytics system that would complement the Linux computational infrastructure, bringing database technology to high-performance computing. Alas, we did not receive the necessary federal funding. I will always wonder if we might have kickstarted the big data revolution a decade earlier. The irony is not lost on me that a few years later I would find myself leading the eXtreme Computing Group at Microsoft to design next-generation cloud computing infrastructure in support of deep learning and cloud services, using lessons and ideas from high-performance computing.
Despite the data analytics setback, the success of two Linux clusters provided the evidence needed for us to propose and deploy the NSF TeraGrid, connecting NCSA, SDSC, Argonne and Caltech in the world's largest open computing infrastructure for scientific discovery. Anchored by NCSA and SDSC, and in partnership with Intel and IBM, the TeraGrid brought distributed Grid services into national production and the subsequent expansion of the Extended Terascale Facility (ETF) created what became the NSF XSEDE program.
The TeraGrid also bought a dramatic increase in national bandwidth, as Charlie Catlett and I negotiated with Qwest to create a 40 gigabit per second wide area network that connected the Illinois and California TeraGrid sites. Our goal was to catalyze a new way of thinking about big data pipes. Ironically, Charlie and I almost nixed this deal, as we had been seeking an even faster 160 gigabits per second connection, only to settle for 40. To put this in perspective, at the time, the Internet2 transcontinental backbone operated at only 2.5 gigabits per second.
While system building, we also had great fun, made possible by the truly wonderful people that were and are the NCSA faculty, staff and students. We built (display) walls and played with smart badges for the SC02 conference. Donna Cox and her team created incredible video of severe storms and galactic evolution that illuminated the beauty of scientific discovery. Rob Pennington and his staff bought Playstation2 systems on eBay and built a GPU-accelerated Linux cluster that caught the attention of the NY Times and helped usher in today's GPU clusters.
In the midst of all this, we ran out of space – offices and computing facilities. The University of Illinois committed to an expansion of the Advanced Computation Building (ACB) that allowed us to deploy the terascale Linux clusters and the TeraGrid. ACB had originally been built to house ILLIAC IV and served as the machine room for NCSA, but the new clusters required more power, more cooling and more space. We designed the ACB expansion with plans for yet another expansion, one rendered unnecessary by the construction of the National Petascale Computing Facility.
I must give enormous credit to the university leadership team, because they built the ACB expansion before we had secured the NSF funding for new machines. I made a handshake, $100M deal with then Provost Richard Herman that if he built the ACB expansion, that NCSA would fill it with hardware. Both of us took a huge political risk, but we honored that commitment. It was just one event in a long history that reflects the repeated willingness of the University of Illinois to invest in the future.
Collectively, the team also secured funding for the NCSA office building and the new Siebel Center for Computer Science, creating an informatics quadrangle that continues to support student, faculty and staff discovery. During this time, I distinctly remember talking to Tom Siebel about the old Digital Computer Laboratory (DCL), which he described as looking like a prison. I remarked that he could help fix that, which began a long series of campus conversations that culminated in his remarkable $32M gift. Likewise, I was pleased that the NCSA building finally came to fruition, after many years of delays. It is not well known, but the two buildings were designed to allow them to connected via an extension to the NCSA building and an arch extending from the northwest corner of Siebel Center.
It was an exhilarating and exciting time. None of it would have happened without an incredible team. We built on Larry Smarr's groundbreaking vision for a supercomputing center. We created machines, software and tools; we engaged researchers, scholars and artists; we invented the future. That is – and always will be – NCSA's mission. Congratulations on 30 great years!
N.B. A version of this essay appeared as a guest blog post for the EU Cloud Lightning project
What's a cloud? The word means many things to many people. In true Airplane! movie parody style, one is tempted to say, "It's a visible mass of condensed water vapor floating in the atmosphere. But, that's not important right now."
Some might say that our cloud confusion is indicative of a fog, a low hanging cloud. Indeed, in public perception, the cloud is a vaguely understood and amorphous capability that lurks behind our smartphones, enabling electronic communications, web searches, social networks, e-commerce, and 24x7 news and streaming media. It is a mysterious "something, somewhere" that is only noticed when it is inaccessible.
For those of us in computing, the cloud is more than a buzzword or meme, it is a set of technologies that are transforming the provisioning and delivery of computing cycles, storage and services, with profound implications for government services, research discovery and innovation, and businesses large and small. Given the public confusion and sometimes overhyped marketing, it seems worthwhile to dispel the fog and illuminate a few compelling features of clouds.
In a very real sense, public clouds – those hosted by cloud providers for on-demand access – have much in common with the timesharing systems of the 1960s and 1970s, albeit at unprecedented scale. They shift the capital costs of provisioning, along with the expertise required for operation, from users to providers, allowing users to focus on their core expertise (e.g., research, business, government) and pay for only what computing they need, when they need it.
In turn, cloud elasticity allows servers to be provisioned and de-provisioned dynamically in response to changing interest and demand. One of the few things worse than having your brilliant idea ignored is having it fail publicly from too much attention. Many an organization has first been thrilled to see their product or service be reported in the press and go viral, only to be horrified when on-premise servers collapse under the load of success; this is known as the slashdot effect, named after one of the popular "news for nerds" web sites. Without cloud elasticity, the global phenomenon of Pokémon Go would not be possible.
Although not required, many clouds also virtualize the underlying computing, allowing multiple services and software stacks to be co-resident on the same physical hardware, further reducing costs. With Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) and containerization, developers can choose the level of abstraction, from low level to high level, needed to support existing and new applications.
By allowing innovators to focus on their ideas rather than computing infrastructure, the pay-as-you-go cloud model has made it easier, faster and cheaper to bring ideas to life, unleashing a second dot.com boom of cloud-based services, unprecedented citizen access to government data, and new approaches to scientific discovery via big data.
The latter has sometimes been called the fourth paradigm, where new sensors, data analytics and deep learning have shifted the research approach from "what experiment should I conduct" to "what insights can I extract from available data." It has also stimulated interest in convergence architectures for high-performance computing (HPC) and data analytics that combine the best elements of cloud and HPC capabilities. (See my essay, Exascale Computing and Big Data: Time to Reunite.)
Implicit in this client-cloud model is ubiquitous, reliable and inexpensive wired and wireless broadband services. Without broadband access, clouds are simply isolated sets of computers and data, unconnected to the world of mobile devices and the nascent Internet of Things. To further ensure reliability, most public cloud providers geo-distribute their pools of servers. Even private clouds (i.e., those operated by governments and companies for their own use) often geo-distribute their servers at multiple data centers. Should disaster strike at one site, services can be shifted automatically to another site, perhaps even in another country or continent.
Finally, the unprecedented scale of cloud deployments, with commercial data centers each costing over $1B, has stimulated new approaches to energy efficiency and cooling, component failures and reliability, customized and open hardware, software defined networks (SDNs) and software defined storage (SDS), and system provisioning and management. In short, we are in a time of radical change in the computing ecosystem, all against a backdrop of Moore's law limitations.
What's a cloud? It's an elastic, on-demand, often geo-distributed, frequently virtualized, flexible hardware, software and services infrastructure that powers the 21st century knowledge economy. And that is really important right now.
N.B. An abridged version of this essay was submitted as a white paper to the BDEC Frankfurt workshop.
I am going to be a bit radical and suggest we must reason in new ways about the need to integrate high-performance computing (HPC) and big data analytics. That must begin with our thinking about how we label and discuss them. Any expert in journalism, communications or psychology will immediately and unhesitatingly affirm that names have extraordinary power. If you doubt that, look at the philosophy of proper names and the cultural history associated with true names. And if that's not convincing, ask any child who was taunted because his or her name rhymed with something unfortunate.
Names shape our thinking, define our discourse and reinforce our biases and perspectives. We use names and descriptors to categorize people, organizations and objects; we use names to define the positive and the negative; and we use names to market, reward and punish. Brand equity has real value. Sadly, as many studies have shown, names also play on stereotypes and our implicit bias. Take Harvard's Project Implicit bias test; you will be humbled and chastened as it exposes your own bias.
High-performance computing (HPC) and big data/machine learning are not exempt from the power of names and a "they could learn from us if they'd only listen" dichotomy. Done any low performance computing (LPC) lately? Analyzed any small data? Of course, you did, when you checked your email and text messages on your smartphone a few minutes ago, but you did not think about it in those terms. In fact, you ran a trans-petascale computation and staged data across a global set of fiber optically connected data caches (a content delivery network) when you searched for a coffee shop yesterday. But, none of us call it that – it's just a quick web search in the ill-defined and amorphous cloud to which we are connected via a global broadband (wired and wireless) infrastructure.
I suspect we are trapped in a sematic cul-de-sac when we talk about high-performance computing and computational science. They are code words for physics-inspired numerical solution of partial differential questions; all too frequently, everything else is potentially suspect and inferior. The denotation of the word exascale says nothing about computing at all, but the connotation of FLOPS and batch-oriented HPC platforms is there in all of our minds. In turn, deep learning and big data conjure images of computer science types tuning neural nets and recommender systems in cloud data centers for targeted product marketing.
It's time to stop categorizing and name calling, time to end cultural and disciplinary silos. There is no hierarchy of needs or intellectual purity. There are just ideas and people who can learn from one other. Dare I say it – death to HPC; death to big data. It's time to stop the religious wars over HPC and big data cultures and technologies and focus on informatics-mediated discovery and innovation. The goal of an HPC center should not be self-perpetuation, either of infrastructure or organization. Nor should a cloud service be restricted to commercial domains. That means starting with intellectual outcomes and deriving approaches rather than defining approaches and searching for feasible intellectual outcomes.
There are many technical ways to envision and realize the continuum and fusion of traditional HPC and computational science and big data and machine learning. These include integration of steam-based workflows and just-in-time schedulers, containerization and software stack packaging for application-specific configuration, fine-grained parallelization of learning packages, learning for algorithm and software adaptation and tuning, custom ASIC designs (see Google's recent TPU announcement), software defined networks and storage (SDN and SDS), and renewable and energy efficient hardware design and configuration. There are an equally large number of social and economic approaches, including denominating informatics costs in currency to illuminate investment priorities, spot pricing for priority access, economics-driven infrastructure selection and deployment, and institutional and funding agency policies for data management.
What we cannot do is allow ourselves to be bound by names, labels and cultures. Richard Hamming wisely noted, "The purpose of computing is insight, not numbers." HPC and machine learning are not goals, they are enablers. And that's the major consensus narrative.
The Raspberry Pi, a credit card sized, $35 computer, is a runaway success, exceeding even the wildest expectations of anyone in the hobbyist or educational communities. The latest version contains a 900 MHz quadcore ARM processor, 1 GB of DRAM, multiple USB ports, a 10/100 Ethernet port, an HDMI interface and 40 GPIO pins (for external device interfaces). Permanent storage is via a Micro SD card slot.
Assuming one has either an HDMI-capable digital television or desktop computer display, the only additional requirements are a USB keyboard/mouse and a Micro SD card of at least 8 GB capacity. Chances are most of us have all of these; if not, another $30 suffices for all but the display. Thus configured, the Raspberry Pi can run several variants of Linux and, as well as the forthcoming embedded version of Windows10.
All of this takes me back to a time in the early 1980s, when I walked to graduate school every day (in the snow, uphill both ways), Pac-Man was a state of the art video game, and the Rubik's Cube was bringing recreational group theory to the masses (though they didn't know it). A DEC VAX 11/780 with 2-8 MB of DRAM running the Berkeley Software Distribution (BSD) of UNIX was the de facto standard research environment for academic computer science.
At a time when batch computing and timeshared university mainframes defined academic research, these BSD VAX systems, along with the National Science Foundation (NSF) Coordinated Experimental Research (CER) program and the CSNET academic network, were transformative. It is no exaggeration to say that modern, experimental computer science can be traced directly to this environment. All of which is why Bill Joy once called the 1 MIPS of the VAX 11/780 the "electron volt of computing."
The Raspberry Pi is a more capable system in almost every way than that departmental VAX – faster, with a larger memory and bigger secondary storage system – as well having a richer base of open source software and access to the global Internet. It is a very tangible and inexpensive manifestation of Moore's Law. However, to quote from the movie Airplane!, that's not important right now. (I am serious. And don't call me Shirley.)
What is important is the transformative effect of the Raspberry Pi and related, inexpensive computing devices on both education and innovation. (The Kickstarter-funded Adapteva Parallela multicore processor is another.) Raspberry Pi's now serve as robot controllers, research sensors, web servers, and software development platforms; they are the darling of hobbyists and the maker movement.
However, the creation nearest and dearest to my heart has been the widespread development of Raspberry Pi Beowulf clusters for parallel computing education. These systems use inexpensive Ethernet switches to create systems similar to the Caltech Cosmic Cube, a seminal design for message-based parallel computing created by Chuck Seitz and Geoffrey Fox in the early 1980s.
Although not fast by today's parallel computing standards, these Raspberry Pi clusters allow students to construct complete parallel systems, ranging from small, development clusters (4-8 nodes) to larger (40-120+ nodes) research systems. More importantly, they expose all aspects of hardware and software configuration, bringing experiential parallel computing to a broad new class of students. (The construction recipe for ORNL's Tiny Titan is one of many such examples.) Even more importantly, the clusters let students run real-world parallel applications.
I would have dearly loved to have one of these clusters for parallel computing research back in the early 1980s. At that time, Richard Fujimoto and I were writing Ph.D. theses at UC-Berkeley and Purdue, respectively, about various aspects of message passing machines. We then collaborated on a book about what we then called multicomputer networks. We have come a long, long way since then.
Yes, I will have some Raspberry Pi. In fact, I'll have a cluster.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog. In addition, you can find the video of an interview and the full text of the CACM article, co-authored with Jack Dongarra.
In other contexts, I have written at length about the cultural and technical divergence of the data analytics (aka machine learning and big data) and high-performance computing (aka big iron) communities. I have euphemistically called them "twins separated at both." (See HPC, Big Data and the Peloponnesian War and Scientific Clouds: Blowin' in the Wind.) Like all twins, they share technical DNA and innate behaviors, despite their superficial differences. After all, in a time long, long ago, they were once united by their use of BSD UNIX and SUN workstations for software development.
Since then, both communities have successfully built scalable infrastructures using high-performance, low cost x86 hardware and a rich suite of (mostly) open source software tools. Both have addressed ecosystem deficiencies by developing special-purpose software libraries and tools (e.g., SLURM and Zookeeper for resource management and MPI and Hadoop for parallelism), and both have optimized hardware for their problem domains (e.g., Open Compute hardware building block standardization, FPGAs for search and machine learning, and GPU accelerators for computational science).
Like many of you, I have seen this evolution firsthand, as a card-carrying geek in both the HPC and cloud computing worlds. One of the reasons I went to Microsoft was to bring HPC ideas and applications to the nascent world of cloud computing. While at Microsoft, I led a research team to explore energy-efficient cloud hardware designs and new programming models, and I launched a public-private partnership between Microsoft and the National Science Foundation on cloud applications. Now that I am back in academia, I am seeking to bring cloud computing ideas back to HPC.
In that spirit, Jack Dongarra and I recently co-authored an article for the Communications of the ACM on the twin ecosystems of HPC and big data and the challenges facing both. Entitled, Exascale Computing and Big Data, the article examines the commonalities and differences, and discusses many of the unresolved issues associated with resilience, programmability, scalability, and post-Dennard hardware futures. Most importantly, the article makes an impassioned plea for hardware and software integration and cultural convergence.
The possibilities for this convergence are legion. The algorithms underlying deep machine learning would benefit from the parallelization and data movement minimization techniques commonly used in HPC applications and libraries. Similarly, the approaches to failure tolerance and systemic resilience common in cloud software have broad applicability to high-performance computing. Both domains face growing energy constraints on the maximum size of feasible systems, necessitating shared focus on domain-specific architectural optimizations that maximize operations per joule.
Perhaps most important of all, there is increasing overlap of application domains. New generations of scientific instruments and sensors are producing unprecedented volumes of observational data, and intelligent, in situ algorithms are increasingly required to reduce raw data and identify important phenomena in real time. To see this, one need look no further than applications of machine learning to astronomy, which now include automated object identification. Conversely, client plus cloud services are increasingly model-based, with rich physics, imaging processing and context that depend on parallel algorithms to meet real-time needs; augmented reality applications are one such exemplar.
The explosive growth of Docker and containerized software management speaks to the need for lightweight, flexible software configuration management for increasingly complex and rich software environments. My hope is that we can develop a unified hardware/software ecosystem that leverages the technical and social strengths of each community. Each would benefit from the experiences and insights of the other. It is past time for the twins to have a family reunion.
I am seeking a post-doctoral research associate to join a National Science Foundation (NSF) project on high-performance computing system reliability and energy efficiency modeling. If you are interested, or you know someone who might be interested, the details of the position are below.
Post-Doctoral Research Associate
Computer System Reliability and Energy Efficiency Modeling Research Project Description
As node counts for high-performance computing systems grow to tens of thousands and with proposed exascale systems likely to contain hundreds of thousands of nodes, overall system reliability and energy consumption are increasingly critical issues. New approaches to balance hardware, software and support costs are needed to address systemic resilience Likewise, the rising energy requirements of ever-larger high-performance computing systems now pose limits on the practicality of their deployment, due to both energy availability and cost.
To make larger systems useable and cost effective, we must develop and adopt new design and operational models that embody two important realities of large-scale systems: (a) frequent hardware component failures are part of normal operation and (b) system and application optimization must be multivariate, including energy cost and efficiency as complements to performance and scalability. New design ideas drawn from commercial cloud computing, including adaptable designs for hardware failure and energy efficiency, are needed if proposed exascale designs are to be feasible, much less practical.
The project focuses on development of (a) scalable, analytic and simulation models for hardware performability (performance plus reliability) based on the principle of near-complete decomposability, (b) assessment and sizing of zero-touch field replaceable hardware modules (FRMs) to reduce hardware repair errors and total cost of ownership (TCO) models, (c) energy-aware batch scheduling models that incorporate bounds on energy availability and energy costs and (d) user resource allocation cost models with energy as a cost proxy.
Desired Skills
Candidates should have a PhD in computer science, electrical and computer engineering or an allied discipline. Experience in computer system simulation, analytic modeling, high-performance computing systems and parallel applications is highly desirable. The successful candidate will be expected to work independently on original research problems related to the project and help coordinate the activities of PhD students.
Desired Start Date
Fall 2014
For more information, please contact
Professor Daniel A. Reed firstname.lastname@example.com
2660 University Capitol Centre +1 319-335-2132
Iowa City, Iowa 52242 USA www.hpcdan.org
In addition to this blog, I also write for the Communications of the ACM (CACM). I recently posted an essay on the future of exascale computing there.As I noted in the CACM blog, we need a catastrophe – in the mathematical sense – a discontinuity triggered by a sustained research program and development program that combines academia, industry and government expertise that leaps the chasm of technical challenges we now face.


text:
Login >
Password Reissue
> End
Password Reissue
Return to Login
Please enter your Team ID in the field below and click 'Reissue Password'.
A new temporary password will be sent to your team leader's e-mail address.
Team ID
E-mail address of team leader
Copyright © Student Formula Japan All Rights Reserved.

subset: commoncrawl


text:
Dev Math 62 - Reciprocal of a Mixed Number مخلوط عدد کا معکوس
Urdu Translation of Reciprocal of a Mixed Number.
If you're seeing this message, it means we're having trouble loading external resources on our website.
If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.


text:
Vivacious is her attitude
Adds sparks with her gratitude
Appears nimble footed
When she rocks the floor
Ignores ones who are boor.
Nags for cute smile
But carries glimmer
Sometimes at grimmer
Desires to roar on thwarts
Aspires to soar on winds.
Brings charm with her positivity
Brings radiance with her creativity
Believes in authenticity
Her syllable includes proficiency
But Adores efficiency
Prolific by nature
Specific with goals.
True motivator when needed
True listener when needed
True friend to rely upon.
Some call her extrovert
Some call her an introvert
She is just an ambivert….


text:
Cardless Credit? Who would have even thought that's even possible? Read on to find out more about how Flipkart Cardless Credit makes your shopping more affordable.
Have you been putting off buying that new double-door refrigerator for mom because you're still waiting on your next month's salary? Do you need that new laptop for work but don't want to borrow money from friends again? You need not worry about the price tags or budgets any more!
Here's how Flipkart Cardless Credit works
With Flipkart Cardless Credit, you can avail easy credit up to ₹1 Lakh to shop for anything that has caught your eye or you really need. Simply choose this as a payment option when you proceed to checkout and don't worry about making a down payment either! When it comes to payment, you can simply choose to pay the amount next month at zero interest, pay 3 months EMI at zero interest or pay in Easy EMIs upto 12 months. Apart from the credit, the simple digital KYC process, easy EMI options and zero processing fee will ensure that your shopping is even more affordable.
Cardless Credit offers credit to Indians with limited or no access to it
If you've kept your distance from applying for a credit card or a personal loan because it is cumbersome, time-consuming, expensive and quite honestly, because you're not quite sure whether you qualify, Flipkart Cardless Credit is just what you need. With Flipkart Cardless Credit, you can shop confidently.
How to apply for Cardless Credit
All you have to do is follow these easy steps:
- Enter your PAN and other details
- Check your Credit Limit
- Complete your simple and quick digital KYC process
- Shop on Flipkart using the Cardless Credit payment option
- Repay by the 15th of next month on the Flipkart app
You can also check your Credit Limit anytime by going to My Accounts > Cardless Credit on the Flipkart app.
This innovative solution offers a smart, stress-free way to shop for Flipkart customers who don't have access to credit or aren't applying for credit. Now, armed with Cardless Credit, you can shop for everything you want or need without any niggling worries of budget.
Click here to get started now!
Also read: Affordable Shopping For All: What You Need To Know About The Flipkart Axis Bank Credit Card


text:
Did you know that the air we breathe is only around 21% oxygen? Now, imagine what it would be like to inhale oxygen that's 95% pure… and scented!
Oxygen bars are becoming incredibly popular around the world for many wellness-related reasons (did someone say hangover cure?!) But is a breath of fresh air really worth it?
Need some pure O2 in your life? Check out The Elixir Clinic for all your wellness needs.


text:
A new governance code for UK private companies will say nothing on executive pay, it has been revealed.
The businessman leading the project to define a code for private companies has said that the pay of executives in private businesses is between them and their bosses.
James Wates, who chairs the construction company Wates, told the Financial Times: "I believe, personally, that what we pay our people in private business is between us and them."
He said that to publicise pay rates would be to put a "for sale" sign on top management talent.
Wates was appointed in January to head a group developing a new governance code for private business.
The move came in response to high-profile business failures such as BHS. Wates is working with other bodies such as the Financial Reporting Council, the Institute of Directors, and the Trades Union Congress (TUC).
At the time of his appointment, Wates said: "I hope that the standards produced through this effort will help promote and enable ever-stronger and more consistent corporate governance amongst large private companies, which generate significant value for the UK economy and society."
The new code will apply to companies with 2,000 employees or with a turnover of more than £200m, or a balance sheet of more than £2bn.


text:
All Matrimonial Services are - 100%, Completely & Totally FREE!!
Contact Members - Free!!
View Contact Details - Free!!
Send Unlimited Messages - Free!!
Photo Album (up to 8 photos) - Free!!
Complete Profile Privacy - Free!!
Register Now !! to contact VID7921
Good partner and small family no income and
About Life Partner Preferences:
Normal family any cast


text:
Statutorily Mandated Designation of Difficult Development Areas and Qualified Census Tracts for 2012, 66741-66747 [2011-27817]Download as PDF
[Federal Register Volume 76, Number 208 (Thursday, October 27, 2011)] [Notices] [Pages 66741-66747] From the Federal Register Online via the Government Printing Office [www.gpo.gov] [FR Doc No: 2011-27817] ======================================================================= ----------------------------------------------------------------------- DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT [Docket No. FR-5575-N-01] Statutorily Mandated Designation of Difficult Development Areas and Qualified Census Tracts for 2012 AGENCY: Office of the Assistant Secretary for Policy Development and Research, HUD. ACTION: Notice. ----------------------------------------------------------------------- SUMMARY: This document designates ``Difficult Development Areas'' (DDAs) for purposes of the Low-Income Housing Tax Credit (LIHTC) under Section 42 of the Internal Revenue Code [[Page 66742]] of 1986 (IRC) (26 U.S.C. 42). The United States Department of Housing and Urban Development (HUD) makes new DDA designations annually. The designations of ``Qualified Census Tracts'' (QCTs) under IRC Section 42 published October 6, 2009, remain in effect. In addition to announcing the 2012 DDA designations, HUD seeks public comment on whether it should use Small Area Fair Market Rents (FMRs), rather than metropolitan-area FMRs, in future designations of metropolitan DDAs. DATES: Comment Due Date: December 27, 2011. ADDRESSES: Interested persons are invited to submit comments regarding this rule to the Regulations Division, Office of General Counsel, 451 7th Street, SW., Room 10276, Department of Housing and Urban Development, Washington, DC 20410-0500. Communications must refer to the above docket number and title. There are two methods for submitting public comments. All submissions must refer to the above docket number and title. 1. Submission of Comments by Mail. Comments may be submitted by mail to the Regulations Division, Office of General Counsel, Department of Housing and Urban Development, 451 7th Street, SW., Room 10276, Washington, DC 20410-0500. 2. Electronic Submission of Comments. Interested persons may submit comments electronically through the Federal eRulemaking Portal at http://www.regulations.gov. HUD strongly encourages commenters to submit comments electronically. Electronic submission of comments allows the commenter maximum time to prepare and submit comments, ensures timely receipt by HUD, and enables HUD to make them immediately available to the public. Comments submitted electronically through the http://www.regulations.gov Web site can be viewed by other commenters and interested members of the public. Commenters should follow the instructions provided on that site to submit comments electronically. Note: To receive consideration as public comments, comments must be submitted through one of the two methods specified above. Again, all submissions must refer to the docket number and title of the rule. No Facsimile Comments. Facsimile (FAX) comments are not acceptable. Public Inspection of Public Comments. All properly submitted comments and communications submitted to HUD will be available for public inspection and copying between 8 a.m. and 5 p.m. eastern time weekdays at the above address. Due to security measures at the HUD Headquarters building, an advance appointment to review the public comments must be scheduled by calling the Regulations Division at 202- 708-3055 (this is not a toll-free number). Individuals with speech or hearing impairments may access this number through TTY by calling the Federal Relay Service at 800-877-8339. Copies of all comments submitted are available for inspection and downloading at http://www.regulations.gov. FOR FURTHER INFORMATION CONTACT: For questions on how areas are designated and on geographic definitions, contact Michael K. Hollar, Senior Economist, Economic Development and Public Finance Division, Office of Policy Development and Research, Department of Housing and Urban Development, 451 Seventh Street, SW., Room 8234, Washington, DC 20410-6000; telephone number 202-402-5878, or send an email to firstname.lastname@example.com. For specific legal questions pertaining to Section 42, contact Branch 5, Office of the Associate Chief Counsel, Passthroughs and Special Industries, Internal Revenue Service, 1111 Constitution Avenue, NW., Washington, DC 20224; telephone number 202- 622-3040, fax number 202-622-4753. For questions about the ``HUB Zones'' program, contact Mariana Pardo, Assistant Administrator for Procurement Policy, Office of Government Contracting, Small Business Administration, 409 Third Street, SW., Suite 8800, Washington, DC 20416; telephone number 202-205-8885, fax number 202-205-7167, or send an email to firstname.lastname@example.com. A text telephone is available for persons with hearing or speech impairments at 202-708-8339. (These are not toll-free telephone numbers.) Additional copies of this notice are available through HUD User at 800-245-2691 for a small fee to cover duplication and mailing costs. Copies Available Electronically: This notice and additional information about DDAs and QCTs are available electronically on the Internet at http://www.huduser.org/datasets/qct.html. SUPPLEMENTARY INFORMATION: This Document This notice designates DDAs for each of the 50 states, the District of Columbia, Puerto Rico, American Samoa, Guam, the Northern Mariana Islands, and the U.S. Virgin Islands. The designations of DDAs in this notice are based on final Fiscal Year (FY) 2011 Fair Market Rents (FMRs), FY 2011 income limits, 2000 Decennial Census population counts for nonmetropolitan areas, and 2010 Decennial Census population counts for metropolitan areas, as explained below. This notice also seeks public comment on whether HUD should change the methodology for determining metropolitan DDAs to use Small Area FRMs (SAFMRS), estimated at the ZIP-Code level based on the relationship of ZIP-Code rents to metropolitan area rents, as the housing cost component of the DDA formula rather than metropolitan-area FMRs. Such a change would more widely distribute DDAs to metropolitan areas around the country than the current methodology, and encourage the development of LIHTC and tax-exempt bond-financed housing in neighborhoods with potentially greater opportunities for resident employment and education. 2000 and 2010 Census Data from the 2010 census on total population of metropolitan areas and from the 2000 census for nonmetropolitan areas are used in the designation of DDAs. Population totals from the 2000 census are used for the designation of nonmetropolitan areas because 2010 population totals are not uniformly available for all nonmetropolitan areas, specifically Guam and the Virgin Islands. The Office of Management and Budget (OMB) first published new metropolitan area definitions incorporating 2000 census data in OMB Bulletin No. 03-04 on June 6, 2003, and updated them periodically through OMB Bulletin No. 09-01 on November 20, 2008. The FY 2011 FMRs and FY 2011 income limits used to designate DDAs are based on these metropolitan statistical area (MSA) definitions, with modifications to account for substantial differences in rental housing markets (and, in some cases, median income levels) within MSAs. Background The U.S. Department of the Treasury (Treasury) and its Internal Revenue Service (IRS) are authorized to interpret and enforce the provisions of the IRC, including the LIHTC found at Section 42. The Secretary of HUD is required to designate DDAs and QCTs by IRC Section 42(d)(5)(B). In order to assist in understanding HUD's mandated designation of DDAs and QCTs for use in administering IRC Section 42, a summary of the section is provided. The following summary does not purport to bind Treasury or the IRS in any way, nor does it purport to bind HUD, since HUD has authority to interpret or administer the IRC only in instances [[Page 66743]] where it receives explicit statutory delegation. Summary of the Low-Income Housing Tax Credit The LIHTC is a tax incentive intended to increase the availability of low-income housing. IRC Section 42 provides an income tax credit to owners of newly constructed or substantially rehabilitated low-income rental housing projects. The dollar amount of the LIHTC available for allocation by each state (credit ceiling) is limited by population. Each state is allowed a credit ceiling based on a statutory formula indicated at IRC Section 42(h)(3). States may carry forward unallocated credits derived from the credit ceiling for one year; however, to the extent such unallocated credits are not used by then, the credits go into a national pool to be redistributed to states as additional credit. State and local housing agencies allocate the state's credit ceiling among low-income housing buildings whose owners have applied for the credit. Besides IRC Section 42 credits derived from the credit ceiling, states may also provide IRC Section 42 credits to owners of buildings based on the percentage of certain building costs financed by tax-exempt bond proceeds. Credits provided under the tax-exempt bond ``volume cap'' do not reduce the credits available from the credit ceiling. The credits allocated to a building are based on the cost of units placed in service as low-income units under particular minimum occupancy and maximum rent criteria. In general, a building must meet one of two thresholds to be eligible for the LIHTC: (1) 20 percent of the units must be rent-restricted and occupied by tenants with incomes no higher than 50 percent of the Area Median Gross Income (AMGI), or (2) 40 percent of the units must be rent-restricted and occupied by tenants with incomes no higher than 60 percent of AMGI. The term ``rent-restricted'' means that gross rent, including an allowance for tenant-paid utilities, cannot exceed 30 percent of the tenant's imputed income limitation (i.e., 50 percent or 60 percent of AMGI). The rent and occupancy thresholds remain in effect for at least 15 years, and building owners are required to enter into agreements to maintain the low-income character of the building for at least an additional 15 years. The LIHTC reduces income tax liability dollar-for-dollar. It is taken annually for a term of 10 years and is intended to yield a present value of either: (1) 70 percent of the ``qualified basis'' for new construction or substantial rehabilitation expenditures that are not federally subsidized (as defined in Section 42(i)(2)), or (2) 30 percent of the qualified basis for the cost of acquiring certain existing buildings or projects that are federally subsidized. The actual credit rates are adjusted monthly for projects placed in service after 1987 under procedures specified in IRC Section 42. Individuals can use the credits up to a deduction equivalent of $25,000 (the actual maximum amount of credit that an individual can claim depends on the individual's marginal tax rate). For buildings placed in service after December 31, 2007, individuals can use the credits against the alternative minimum tax. Corporations, other than S or personal service corporations, can use the credits against ordinary income tax, and, for buildings placed in service after December 31, 2007, against the alternative minimum tax. These corporations also can deduct losses from the project. The qualified basis represents the product of the building's ``applicable fraction'' and its ``eligible basis.'' The applicable fraction is based on the number of low-income units in the building as a percentage of the total number of units, or based on the floor space of low-income units as a percentage of the total floor space of residential units in the building. The eligible basis is the adjusted basis attributable to acquisition, rehabilitation, or new construction costs (depending on the type of LIHTC involved). These costs include amounts chargeable to a capital account that are incurred prior to the end of the first taxable year in which the qualified low-income building is placed in service or, at the election of the taxpayer, the end of the succeeding taxable year. In the case of buildings located in designated DDAs or designated QCTs, eligible basis can be increased up to 130 percent from what it would otherwise be. This means that the available credits also can be increased by up to 30 percent. For example, if a 70-percent credit is available, it effectively could be increased to as much as 91 percent. IRC Section 42 defines a DDA as any area designated by the Secretary of HUD as an area that has high construction, land, and utility costs relative to the AMGI. All designated DDAs in metropolitan areas (taken together) may not contain more than 20 percent of the aggregate population of all metropolitan areas, and all designated areas not in metropolitan areas may not contain more than 20 percent of the aggregate population of all nonmetropolitan areas. IRC Section 42(d)(5)(B)(v) allows states to award an increase in basis up to 30 percent to buildings located outside of federally designated DDAs and QCTs if the increase is necessary to make the building financially feasible. This state discretion applies only to buildings allocated credits under the state housing credit ceiling and is not permitted for buildings receiving credits in connection with tax-exempt bonds. Rules for such designations shall be set forth in the LIHTC-allocating agencies' qualified allocation plans (QAPs). Explanation of HUD Designation Methodology A. Difficult Development Areas In developing the list of DDAs, HUD compared housing costs with incomes. HUD used 2010 census population for metropolitan areas, 2000 census population data for nonmetropolitan areas, and the MSA definitions, as published in OMB Bulletin No. 09-01 on November 20, 2008, with modifications, as described below. In keeping with past practice of basing the coming year's DDA designations on data from the preceding year, the basis for these comparisons is the FY 2011 HUD income limits for very low-income households (very low-income limits, or VLILs), which are based on 50 percent of AMGI, and final FY 2011 FMRs used for the Housing Choice Voucher (HCV) program. In formulating the FY 2011 FMRs and VLILs, HUD modified the current OMB definitions of MSAs to account for substantial differences in rents among areas within each new MSA that were in different FMR areas under definitions used in prior years. HUD formed these ``HUD Metro FMR Areas'' (HMFAs) in cases where one or more of the parts of newly defined MSAs that previously were in separate FMR areas had 2000 census-based 40th-percentile recent-mover rents that differed, by 5 percent or more, from the same statistic calculated at the MSA level. In addition, a few HMFAs were formed on the basis of very large differences in AMGIs among the MSA parts. All HMFAs are contained entirely within MSAs. All nonmetropolitan counties are outside of MSAs and are not broken up by HUD for purposes of setting FMRs and VLILs. (Complete details on HUD's process for determining FY 2011 FMR areas and FMRs are available at http://www.huduser.org/portal/datasets/fmr/fmrs/docsys.html&data=fmr11. Complete details on HUD's process for determining FY2011 income limits are available at http://www.huduser.org/portal/datasets/il/il11/index.html.) HUD's unit of analysis for designating metropolitan DDAs, therefore, consists [[Page 66744]] of: entire MSAs, in cases where these were not broken up into HMFAs for purposes of computing FMRs and VLILs; and HMFAs within the MSAs that were broken up for such purposes. Hereafter in this notice, the unit of analysis for designating metropolitan DDAs will be called the HMFA, and the unit of analysis for nonmetropolitan DDAs will be the nonmetropolitan county or county equivalent area. The procedure used in making the DDA calculations follows: 1. For each HMFA and each nonmetropolitan county, a ratio was calculated. This calculation used the final FY 2011 two-bedroom FMR and the FY 2011 four-person VLIL. a. The numerator of the ratio, representing the development cost of housing, was the area's final FY 2011 FMR. In general, the FMR is based on the 40th-percentile gross rent paid by recent movers to live in a two-bedroom apartment. In metropolitan areas granted a FMR based on the 50th-percentile rent for purposes of improving the administration of HUD's HCV program (see 76 FR 52058), the 40th-percentile rent was used to ensure nationwide consistency of comparisons. b. The denominator of the ratio, representing the maximum income of eligible tenants, was the monthly LIHTC income-based rent limit, which was calculated as 1/12 of 30 percent of 120 percent of the area's VLIL (where the VLIL was rounded to the nearest $50 and not allowed to exceed 80 percent of the AMGI in areas where the VLIL is adjusted upward from its 50 percent-of-AMGI base). 2. The ratios of the FMR to the LIHTC income-based rent limit were arrayed in descending order, separately, for HMFAs and for nonmetropolitan counties. 3. The DDAs are those with the highest ratios cumulative to 20 percent of the 2010 population of all metropolitan areas and 2000 population of all nonmetropolitan areas. Population totals from the 2000 census are used for the designation of nonmetropolitan areas because 2010 population totals are not uniformly available for all nonmetropolitan areas, specifically Guam and the Virgin Islands. B. Application of Population Caps to DDA Determinations IRC Section 42 requires the application of caps, or limitations, as noted above. The cumulative population of metropolitan DDAs cannot exceed 20 percent of the cumulative population of all metropolitan areas, and the cumulative population of nonmetropolitan DDAs cannot exceed 20 percent of the cumulative population of all nonmetropolitan areas. In applying caps, HUD established procedures to deal with how to treat small overruns of the caps. The remainder of this section explains those procedures. In general, HUD stops selecting areas when it is impossible to choose another area without exceeding the applicable cap. The only exceptions to this policy are when the next eligible excluded area contains either a large absolute population or a large percentage of the total population, or the next excluded area's ranking ratio, as described above, was identical (to four decimal places) to the last area selected, and its inclusion resulted in only a minor overrun of the cap. Thus, for both the designated metropolitan and nonmetropolitan DDAs, there may be minimal overruns of the cap. HUD believes the designation of additional areas in the above examples of minimal overruns is consistent with the intent of the IRC. As long as the apparent excess is small due to measurement errors, some latitude is justifiable, because it is impossible to determine whether the 20 percent cap has been exceeded. Despite the care and effort involved in a Decennial Census, the Bureau of the Census and all users of the data recognize that the population counts for a given area and for the entire country are not precise. Therefore, the extent of the measurement error is unknown. There can be errors in both the numerator and denominator of the ratio of populations used in applying a 20 percent cap. In circumstances where a strict application of a 20 percent cap results in an anomalous situation, recognition of the unavoidable imprecision in the census data justifies accepting small variances above the 20 percent limit. C. Exceptions to OMB Definitions of MSAs and Other Geographic Matters As stated in OMB Bulletin 09-01, defining metropolitan areas: OMB establishes and maintains the definitions of Metropolitan * * * Statistical Areas, * * * solely for statistical purposes. * * * OMB does not take into account or attempt to anticipate any non- statistical uses that may be made of the definitions[.] In cases where * * * an agency elects to use the Metropolitan * * * Area definitions in nonstatistical programs, it is the sponsoring agency's responsibility to ensure that the definitions are appropriate for such use. An agency using the statistical definitions in a nonstatistical program may modify the definitions, but only for the purposes of that program. In such cases, any modifications should be clearly identified as deviations from the OMB statistical area definitions in order to avoid confusion with OMB's official definitions of Metropolitan * * * Statistical Areas. Following OMB guidance, the estimation procedure for the FY 2011 FMRs incorporates the current OMB definitions of metropolitan areas based on the Core-Based Statistical Area (CBSA) standards, as implemented with 2000 Census data, but makes adjustments to the definitions, in order to separate subparts of these areas in cases where FMRs (and in a few cases, VLILs) would otherwise change significantly if the new area definitions were used without modification. In CBSAs where subareas are established, it is HUD's view that the geographic extent of the housing markets are not yet the same as the geographic extent of the CBSAs, but may approach becoming so as the social and economic integration of the CBSA component areas increases. The geographic baseline for the new estimation procedure is the CBSA Metropolitan Areas (referred to as Metropolitan Statistical Areas or MSAs) and CBSA NonMetropolitan Counties (nonmetropolitan counties include the county components of Micropolitan CBSAs where the counties are generally assigned separate FMRs). The HUD-modified CBSA definitions allow for subarea FMRs within MSAs based on the boundaries of ``Old FMR Areas'' (OFAs) within the boundaries of new MSAs. (OFAs are the FMR areas defined for the FY 2005 FMRs. Collectively, they include the June 30, 1999, OMB definitions of MSAs and primary MSAs (old definition MSAs/primary metropolitan statistical areas (PMSAs), metropolitan counties deleted from old definition MSAs/PMSAs by HUD for FMR-setting purposes, and counties and county parts outside of old definition MSAs/PMSAs referred to as nonmetropolitan counties). Subareas of MSAs are assigned their own FMRs when the subarea 2000 census base FMR differs significantly from the MSA 2000 census base FMR (or, in some cases, where the 2000 census base AMGI differs significantly from the MSA 2000 census base AMGI). MSA subareas, and the remaining portions of MSAs after subareas have been determined, are referred to as ``HUD Metro FMR Areas (HMFAs),'' to distinguish such areas from OMB's official definition of MSAs. In the New England states (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont), HMFAs are defined according to county subdivisions or minor civil divisions (MCDs), rather than county boundaries. However, since no part of an HMFA is outside an OMB- defined, [[Page 66745]] county-based MSA, all New England nonmetropolitan counties are kept intact for purposes of designating Nonmetropolitan DDAs. For the convenience of readers of this notice, the geographical definitions of designated Metropolitan DDAs are included in the list of DDAs. Future Designations DDAs are designated annually as updated income and FMR data are made public. QCTs are designated periodically as new data become available, or as metropolitan area definitions change. QCTs are not redesignated for 2012 because household income distribution and poverty data is not available for 2010 census tract boundaries. The most recent data for which household income by tract is available is from the 2005- 2009, 5-year American Community Survey (ACS). This data, however, was released using the 2000 census tract boundaries, while the 2010 decennial census population counts were released using the 2010 census tract boundaries. The geography of the population counts does not match the geography of the income and poverty rate information. This makes the most recent data incompatible for QCT designation, meaning HUD cannot designate QCTs in accordance with statute. The next release of census tract-level data from the ACS, which will be the 2006-2010, 5-year data using 2010 Decennial Census boundaries, is scheduled for December 2011. At this point, all data needed to designate QCTs in accordance with statute will be tabulated to compatible geographies. Since the LIHTC program, for which QCTs are designated, operates on a calendar-year annual allocation cycle, HUD's standing practice is to designate QCTs in the fall prior to the effective date, which coincides with the calendar year. This provides lead time for the LIHTC developers and administrators to adjust plans in accordance with the revised designations. Thus, the next scheduled designation of QCTs using data released in December 2011 is the fall of 2012, for an effective date of January 1, 2013. Effective Date The 2012 lists of DDAs are effective: (1) For allocations of credit after December 31, 2011; or (2) For purposes of IRC Section 42(h)(4), if the bonds are issued and the building is placed in service after December 31, 2011. If an area is not on a subsequent list of DDAs, the 2012 lists are effective for the area if: (1) The allocation of credit to an applicant is made no later than the end of the 365-day period after the applicant submits a complete application to the LIHTC-allocating agency, and the submission is made before the effective date of the subsequent lists; or (2) For purposes of IRC Section 42(h)(4), if: (a) The bonds are issued or the building is placed in service no later than the end of the 365-day period after the applicant submits a complete application to the bond-issuing agency, and (b) The submission is made before the effective date of the subsequent lists, provided that both the issuance of the bonds and the placement in service of the building occur after the application is submitted. An application is deemed to be submitted on the date it is filed if the application is determined to be complete by the credit-allocating or bond-issuing agency. A ``complete application'' means that no more than de minimis clarification of the application is required for the agency to make a decision about the allocation of tax credits or issuance of bonds requested in the application. In the case of a ``multiphase project,'' the DDA or QCT status of the site of the project that applies for all phases of the project is that which applied when the project received its first allocation of LIHTC. For purposes of IRC Section 42(h)(4), the DDA or QCT status of the site of the project that applies for all phases of the project is that which applied when the first of the following occurred: (a) The building(s) in the first phase were placed in service, or (b) the bonds were issued. For purposes of this notice, a ``multiphase project'' is defined as a set of buildings to be constructed or rehabilitated under the rules of the LIHTC and meeting the following criteria: (1) The multiphase composition of the project (i.e., total number of buildings and phases in project, with a description of how many buildings are to be built in each phase and when each phase is to be completed, and any other information required by the agency) is made known by the applicant in the first application of credit for any building in the project, and that applicant identifies the buildings in the project for which credit is (or will be) sought; (2) The aggregate amount of LIHTC applied for on behalf of, or that would eventually be allocated to, the buildings on the site exceeds the one-year limitation on credits per applicant, as defined in the Qualified Allocation Plan (QAP) of the LIHTC-allocating agency, or the annual per-capita credit authority of the LIHTC allocating agency, and is the reason the applicant must request multiple allocations over 2 or more years; and (3) All applications for LIHTC for buildings on the site are made in immediately consecutive years. Members of the public are hereby reminded that the Secretary of Housing and Urban Development, or the Secretary's designee, has sole legal authority to designate DDAs and QCTs, by publishing lists of geographic entities as defined by, in the case of DDAs, the several states and the governments of the insular areas of the United States and, in the case of QCTs, by the Census Bureau; and to establish the effective dates of such lists. The Secretary of the Treasury, through the IRS thereof, has sole legal authority to interpret, and to determine and enforce compliance with the IRC and associated regulations, including Federal Register notices published by HUD for purposes of designating DDAs and QCTs. Representations made by any other entity as to the content of HUD notices designating DDAs and QCTs that do not precisely match the language published by HUD should not be relied upon by taxpayers in determining what actions are necessary to comply with HUD notices. The designations of ``Qualified Census Tracts'' under IRC Section 42, published October 6, 2009 (74 FR 51304), remain in effect. The above language regarding 2012 and subsequent designations of DDAs also applies to the designations of QCTs published October 6, 2009 (74 FR 51304) and to subsequent designations of QCTs. Interpretive Examples of Effective Date For the convenience of readers of this notice, interpretive examples are provided below to illustrate the consequences of the effective date in areas that gain or lose DDA status. The examples covering DDAs are equally applicable to QCT designations. (Case A) Project A is located in a 2012 DDA that is NOT a designated DDA in 2013. A complete application for tax credits for Project A is filed with the allocating agency on November 15, 2012. Credits are allocated to Project A on October 30, 2013. Project A is eligible for the increase in basis accorded a project in a 2012 DDA because the application was filed BEFORE January 1, 2013 (the assumed effective date for the 2013 DDA lists), and because tax credits were [[Page 66746]] allocated no later than the end of the 365-day period after the filing of the complete application for an allocation of tax credits. (Case B) Project B is located in a 2012 DDA that is NOT a designated DDA in 2013 or 2014. A complete application for tax credits for Project B is filed with the allocating agency on December 1, 2012. Credits are allocated to Project B on March 30, 2014. Project B is not eligible for the increase in basis accorded a project in a 2012 DDA because, although the application for an allocation of tax credits was filed before January 1, 2013 (the assumed effective date of the 2013 DDA lists), the tax credits were allocated later than the end of the 365- day period after the filing of the complete application. (Case C) Project C is located in a 2012 DDA that was not a DDA in 2011. Project C was placed in service on November 15, 2011. A complete application for tax-exempt bond financing for Project C is filed with the bond-issuing agency on January 15, 2012. The bonds that will support the permanent financing of Project C are issued on September 30, 2012. Project C is not eligible for the increase in basis otherwise accorded a project in a 2012 DDA, because the project was placed in service before January 1, 2012. (Case D) Project D is located in an area that is a DDA in 2012, but is not a DDA in 2013. A complete application for tax-exempt bond financing for Project D is filed with the bond-issuing agency on October 30, 2012. Bonds are issued for Project D on April 30, 2013, but Project D is not placed in service until January 30, 2014. Project D is eligible for the increase in basis available to projects located in 2012 DDAs because: (1) One of the two events necessary for triggering the effective date for buildings described in Section 42(h)(4)(B) of the IRC (the two events being bonds issued and buildings placed in service) took place on April 30, 2013, within the 365-day period after a complete application for tax-exempt bond financing was filed, (2) the application was filed during a time when the location of Project D was in a DDA, and (3) both the issuance of the bonds and placement in service of Project D occurred after the application was submitted. (Case E) Project E is a multiphase project located in a 2012 DDA that is not a designated DDA in 2013. The first phase of Project E received an allocation of credits in 2012, pursuant to an application filed March 15, 2012, which describes the multiphase composition of the project. An application for tax credits for the second phase Project E is filed with the allocating agency by the same entity on March 15, 2013. The second phase of Project E is located on a contiguous site. Credits are allocated to the second phase of Project E on October 30, 2013. The aggregate amount of credits allocated to the two phases of Project E exceeds the amount of credits that may be allocated to an applicant in one year under the allocating agency's QAP and is the reason that applications were made in multiple phases. The second phase of Project E is, therefore, eligible for the increase in basis accorded a project in a 2012 DDA, because it meets all of the conditions to be a part of a multiphase project. (Case F) Project F is a multiphase project located in a 2012 DDA that is not a designated DDA in 2013. The first phase of Project F received an allocation of credits in 2012, pursuant to an application filed March 15, 2012, which does not describe the multiphase composition of the project. An application for tax credits for the second phase of Project F is filed with the allocating agency by the same entity on March 15, 2014. Credits are allocated to the second phase of Project F on October 30, 2014. The aggregate amount of credits allocated to the two phases of Project F exceeds the amount of credits that may be allocated to an applicant in one year under the allocating agency's QAP. The second phase of Project F is, therefore, not eligible for the increase in basis accorded a project in a 2012 DDA, since it does not meet all of the conditions for a multiphase project, as defined in this notice. The original application for credits for the first phase did not describe the multiphase composition of the project. Also, the application for credits for the second phase of Project F was not made in the year immediately following the first phase application year. Request for Public Comment on Designating DDAs Using Small Area FMRs in Metropolitan Areas HUD is considering a major policy change in the method of designating metropolitan DDAs beginning with the 2013 designations. Rather than using FMRs established for HUD Metropolitan FMR Areas as the measure of ``construction, land, and utility costs relative to area median gross income,'' HUD would use ``Small Area FMRs'' (SAFMRs) defined at the ZIP Code level within metropolitan areas. In general, HUD estimates SAFMRs by multiplying the ratio of ZIP-Code area to metropolitan-area median gross rent by the metropolitan-area FMRs (a complete description of how SAFMRs are estimated was published in a Federal Register notice at 75 FR 27808-12 (May 18, 2010) and is available at: http://www.huduser.org/portal/datasets/_fmr/fmr2010f/Small_Area_FMRs.pdf). HUD would use the same income measure as used in the current metropolitan DDA designation method, the HUD income limits for very low-income households, or VLILs, estimated at the HUD Metropolitan FMR Area level, which are used to determine LIHTC and tax- exempt bond-financed project maximum rents and tenant income limits. HUD would otherwise designate Small Area Difficult Development Areas (SADDAs) in the same way as it designates metropolitan DDAs as described above in this notice, except that the unit of analysis is the metropolitan ZIP Code instead of the HUD Metropolitan FMR Area. Thus, the population-weighted 20 percent of ZIP Codes with the highest ratios of SAFMR to metropolitan VLIL would be designated as DDAs. HUD has available an evaluative list of the 2,118 metropolitan ZIP Codes that would be designated Small Area DDAs based on the data available to HUD at the time of this publication. The main piece of currently missing data that HUD would have for a 2013 designation of SADDAs is the 2010 Decennial Census population counts for ZIP Codes. Thus, HUD used the ZIP Code-to-metropolitan area rent relationships and ZIP Code populations from the 2000 Decennial Census to create the evaluative list of SADDAs. In general, the metropolitan areas designated DDAs in this notice have many, but not all, ZIP Codes designated as SADDAs, while a number of metropolitan areas that have never been DDAs in the history of the program get one or more SADDAs. Under SADDAs, the additional subsidy available under section 42 would be limited to the higher opportunity areas of high-cost rental markets, and to the highest opportunity areas of otherwise lower-cost rental markets. HUD seeks comments on the relative merits of SADDAs versus existing metropolitan DDA policy in advancing HUD's goals of meeting the need for quality affordable rental homes and utilizing housing as a platform for improving quality of life. [[Page 66747]] Findings and Certifications Environmental Impact In accordance with 40 CFR 1508.4 of the regulations of the Council on Environmental Quality and 24 CFR 50.19(c)(6) of HUD's regulations, the policies and procedures contained in this notice provide for the establishment of fiscal requirements or procedures that do not constitute a development decision affecting the physical condition of specific project areas or building sites. Therefore, they are categorically excluded from the requirements of the National Environmental Policy Act, except for extraordinary circumstances, and no Finding of No Significant Impact is required. Federalism Impact Executive Order 13132 (entitled ``Federalism'') prohibits an agency from publishing any policy document that has federalism implications if the document either imposes substantial direct compliance costs on state and local governments and is not required by statute, or the document preempts state law, unless the agency meets the consultation and funding requirements of section 6 of the executive order. This notice merely designates DDAs as required under Section 42 of the IRC, as amended, for the use by political subdivisions of the states in allocating the LIHTC. This notice also details the technical methodology used in making such designations. As a result, this notice is not subject to review under the order. Dated: October 20, 2011. Raphael W. Bostic, Assistant Secretary for Policy Development and Research. [FR Doc. 2011-27817 Filed 10-26-11; 8:45 am] BILLING CODE 4210-67-P


text:
When a loved one dies, families must deal with not only the loss but also planning a funeral that allows far-flung relatives and friends enough time to attend services.
In central Ohio, there's a solution to that logistical nightmare.
For the past 10 years, area funeral homes have had the ability to livestream almost all their funeral services.Get the news delivered to your inbox: Sign up for our morning, afternoon and evening newsletters
However, there are still few takers for the service, according to local funeral homes.
Devin Woodyard, vice president of O R Woodyard Funeral and Cremation Services, estimates that the number of funeral services that are livestreamed in a year barely cracks double-digits.
Michael Schoedinger, president and sixth-generation owner of Schoedinger Funeral and Cremation Services, said he doesn't know the number of the company's funerals that have been livestreamed, but he thinks the option is underused.
"We've been doing it for years, but it took a while to gain interest," he said. "In the beginning, it was a one-way stream."
The first time Schoedinger livestreamed a funeral was in 2007 for a member of the military who was overseas and couldn't make it home for his grandmother's funeral. That remote connection to an intimate family moment is one of the main draws for families.
"Our typical (livestream) experience is when there's a child or grandchild who's overseas," Woodyard said. "There are some correctional facilities that allow for inmates to view a screening of the funeral services if they can't visit."
Woodyard and Schoedinger use funeralOne, a Detroit-based company that provides livestream services to more than 5,000 funeral homes across the country. Although the livestreams can be shared openly on the funeral homes' websites, most people elect to have the streams shared privately via email.
"Most people are satisfied," Woodyard said of the service. "The camera isn't right up to the casket; it's usually mounted in the ceiling, and it's more of a wide view of the room. There are other times where we put it on a tripod."
In addition to streaming, both funeral homes also offer video-recording services. Woodyard offers a separate, higher-quality camera that's used to record the funeral so the footage can be viewed anytime, while Schoedinger records the footage for a tribute page that's posted on the company's website.
Although livestreaming is still relatively new in the funeral industry, the natural advances of technology might make it obsolete.
"It's probably being quickly outdated by how much movement there's been on FaceTime and Skype," Woodyard said. " I think two-way communication is going to phase out the web-based livestreams."
No matter what the future of livestreaming is in funerals, the idea of recording a personal family moment won't go away.
"I think funeral rites are part of our life," Schoedinger said, "so even if you have to use your TV to show your child how to say goodbye to grandma, it's better than not doing it at all."


text:
Yield and combining ability stability in different oat populations
The association of stability indexes and diallel analyses can provide a strong tool for the predictability of selecting quantitative traits. Therefore, the present work had as objective to test the stability of different oat populations, originating from the combination of fve parental genotypes (UPF 16, UPF 18, UFRGS 7, UFRGS 17 and URPel 15) as assessed by their general and specifc combining abilities. The experimental design consisted of random blocks with three replications. The experiments were performed in the Centro Agropecuário da Palma, in the experimental feld belonging to the Centro de Genômica e Fitomelhoramento, in Capão do Leão county, Rio Grande do Sul state. The essays were performed in 2002 and 2003, as one and two experiments, respectively, adding up to three environments. The evaluation in different generations and years was essential to discriminate well the genotypes for the stability of their specifc combining ability effects. The crosses UPF 16 x UPF 18, UPF 16 x UFRGS 7 and UPF 16 x UFRGS 17 were found to be promising regarding the increase grain yield by indirect selecting number of panicles per plant, number of grains per panicle and panicle weight, respectively.
Full Text:PDF (Português (Brasil))
DOI: http://dx.doi.org/10.5433/1679-0359.2009v30n2p331
Londrina - PR
E-mail: email@example.com


text:
When I wrote my article on Botox Mistakes and How to Fix Them, I had no idea how popular it would be. So I thought I would cover dermal filler problems, too.
It makes sense to do a piece on Juvederm and Restylane mistakes or problems, just in case some of you have had filler results that you're not that happy with. Some of these tips are things you can do to help ensure good results, and some are things your dermatologist may need to do.
Dermal Filler Problems
Here are five problems you may encounter or hear from your friends:
- Too much bruising or bruises;
- Asymmetry (the face or parts of it look lop-sided);
- Over-filled or puffy looking;
- Under-filled (you don't look any different than before);
- Lumps or bumps
- The one serious complication – a blocked blood vessel (very rare).
Too Much Bruising
The injection of any face fillers will often cause at least a little bruising or some red spots. With a little knowledge on your part and a good doctor or nurse injector, bruising can be minimized.
Bruising after Juvederm or Restylane cannot be cured or fixed, other than to cover it with make-up. So the goal with bruising is to prevent it in the first place. So here are the tips on how to avoid Juvederm bruises that we provide to our patients at Madison Skin & Laser Center, our dermatology practice in Seattle:
- No aspirin, Aleve, or Ibuprofin 1 week prior to your appointment (Tylenol is fine)
- Do not take Vitamin E 1 week prior to injections. Your multivitamin is okay.
- Stop alcohol 2 days prior to injections.
- No garlic 2 days prior to injections.
- Eat pineapple 2-3 times 1-2 days prior to injections. Bromelain supplements can be taken but can have some serious side effects.
- Take Arnica one day prior or morning of injections. You can find it at certain drugstores and often at Whole Foods.
Asymmetry, or a Lop-Sided Face
We all have some normal and natural asymmetry to our faces. I think that's part of what makes us individual and beautiful. Some of us have a lot and some have very little asymmetry. If a filler like Juvederm is done well, the uneven face should be slightly corrected or left as is. But it should definitely not be worse. So, if you're Juvederm or Restylane filler looks lop-sided, look at a recent photo first!
Then, take a deep breath or two, and if it's not too noticeable, just wait a week. Bruises can cause a temporary bump or asymmetry and will go away in a week or 10 days. Sometimes that bruise is deep and not visible on the surface of the skin.
Now, if the asymmetry is still there after a week or so, or is excessive, you may need to fix it. For this, please call your doctor. They should get you in within a day or two. Make an appointment and explain your concern. Depending on your goals, the doctor can either inject more filler (needed if one side is under-filled) or use an enzyme (hyluronadase), to reduce or dissolve the bump or fullness.
Over-Filled or Too Puffy
The same principles above apply to this problem. Wait a week or so because often the problem will resolve on its own. There is often some puffiness right after a treatment that goes down within 7-10 days. Remember sometimes bruises from Juvederm can be deep and you may not see them on the surface. They might just feel a little tender and cause some puffiness.
If you still feel over-filled, call your dermatologist. You may need a little enzyme (hyluronadase) injected to reduce the fullness.
Under-filled or Not Enough Result
Feeling under-filled or that the result is not enough aren't really dermal filler problems or mistakes in the true sense of the word. In my experience, most patients prefer to start with a conservative treatment to see how they will react to a new product. It's a good idea to go slowly in the beginning. Many doctors will under-fill just slightly on that first treatment but tell you to please wait a week or two and come back for 1 more syringe if it's not quite enough.
Lump or Bumps
There have been some rare reports of small lumps or bumps with Juvederm and Restylane. These are usually small bruises underneath the filler. But the bruises can feel like a small lump or bump.
The best way to fix these lumps or bumps is wait ten days. If the lump or bump is still noticeable, see your doctor. The most common fix for this is, again, hyluronadase to remove enough filler so that the lump or bump goes away.
The one serious complication – a blocked blood vessel (very rare)
This is fortunately a very, very rare side effect of Juvederm or Restylane. How rare? Estimates are 1 out of 10,000+ patients.
But here's what to know just in case. Small bruises are common; even an occasional large bruise is fairly common. The two areas at risk are around the base of the nose and the area where your frown lines are. If those turn dark purple or there's blotchy red rash that extends out from the purple area onto the nose, upper lip or forehead, call your doctor right away, and don't wait until Monday if it's the weekend. This needs to be treated right away to avoid a scar.
As with all cosmetic treatments, you should find an experienced injector in a reputable medical practice.
For more information, see Dr. Irwin's other filler resources:
Guide to Juvederm, Restlyane, and Fillers
See Dr. Irwin's expert answers to other reader's questions on dermal filler problems or mistakes and how to fix them:
If I had a problem with a Juvederm injection, is it okay to use hyaluronidase to fix it?
I have a lump in my lip from Juvederm!
Help! A Restylane problem!
I had injections of Restylane in my chin and now have white bumps inside my mouth. Should I worry – it has been 4 weeks?
More problems with fillers under the eye area.
Is there a good makeup that can cover up bruises from injection?
A serious problem after Radiesse.


text:
Should I post snippets from my upcoming books: Yes or No.
There were strong feelings both sides. YES--and a lot enthusistic yeses, and some ... "I don't want to know a thing about the book until it's time to read it."
Well, there were more yesses than nos. I think the "no" crowd might think I'm going to post scenes. Nope. Just snappy lines of narrative like....
Friend with benefits? That wasn't how Tricia thought of her relationship with Marshall Cambridge. Okay, they were friends, and they did sometimes have the occasional sleepover, but that didn't mean…
Good grief, Tricia realized. She and Marshall were friends with benefits!*
So, what do you think about snippets? Yay or Nay?
*This snippit is from my upcoming (Booktown #13) A KILLER EDITION, which will be available in July of 2019.


text:
Fudraising concert in support of the World Zionist Congress.
Featuring Star for Israel violinist – virtuoso Sanya Kroitor, "Fiery Violin"
and his friends musicians from Israel and USA
Alex Gold,Piano
Klezmer-Jazz Band, Lead by Yaacov Mayman, Golden saxophone of New York, NY
Donation $10
More info and tickets at www.centermakor.oeg or Center Makor Facebook
Free Parking
Event Location: Center Makor
1845 Commonwealth Ave, Boston, Massachusetts, 02135
CJP provides the above links concerning third-party events for your convenience only. CJP has no control over the content of the linked-to websites or events they describe, and accepts no responsibility for the websites, including any advertising or products or services on or available from such sites, or for any loss or damage that may arise from your attending, or registering to attend, the described events. If you decide to access any of the third-party websites linked to below, you do so entirely at your own risk and subject to the terms and conditions of use for such websites and event attendance. CJP is not responsible or liable to you or any third party for the content or accuracy of any materials provided by any third parties. All statements and/or opinions expressed in the linked-to materials or at the described events, and all commentary, articles and other content provided at the third-party websites or at the events, are solely the opinions and the responsibility of the persons or entities operating the linked-to websites and events. The inclusion of any link on this website does not imply that CJP endorses the described event, or the linked-to website or its operator.


text:
Use the search filters below to browse our projects:
Currently displaying:
...or search by project name: nominate Project
You searched for:
New England Carpenters Training Center
|Owner: NECTF
|Location: Millbury, MA
|Contract Value: $1.4 M
|Construction Manager: New England Carpenters Training Fund (NECTF)
|Size: 80,000 Sq. Ft.
The design of the building's exterior showcases the work of union carpenters. Features include metal panels, Hardie cement board, and an architectural louver system. Work included window replacements, dorm room upgrades, and reconfiguration of shop space.