text:
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.
Warning: This is a long post, reflecting the complexity and nuance of Internet governance.
Just before the 2012 holidays, the World Conference on International Telecommunications (WCIT), pronounced as "wicket" by the cognoscenti, concluded in Dubai. In the buildup to the WCIT, there was much handwaving and fearmongering, political position jockeying, and backroom negotiations by multinational companies, governments, non-governmental organizations (NGOs), technology policy wonks and political pundits. There were frequent stories in the trade and policy rags and the mainstream press, including the New York Times. How, you might ask, could such a seemingly obscure conference engender such an international frenzy?
In the spirit of full disclosure, I should reveal that I spent a good portion of the last three years working on this issue, while heading Microsoft's Technology Policy Group. I traveled the world, meeting telecommunications policy leaders, trade associations, companies and senior representatives of international governments, including ones from the United States Departments of State, Commerce and Federal Communications Commission (FCC). With that disclosure, as well as noting that what follows are my personal opinions, a bit of background and history is in order.
The International Telecommunications Union (ITU), which is a United Nations organization, organizes the WCIT. The ITU began as the International Telegraph Union, which suggests some its origins and history. Originally responsible for coordinating global use of the radio spectrum and, more recently, assisting in international assignment of satellite orbits, the ITU now operates under three units, the ITU-R (radiocommunications), ITU-T (standardization) and ITU-D (development).
As a UN organization, the ITU's members are countries, rather than individuals, companies or NGOs. Despite the one country, one vote governance model, there is multistakeholder participation in some informal aspects of the ITU. I was a frequent visitor to the ITU, and I participated in ITU retreats and CTO roundtables. I know the ITU leadership well.
Because major ITU policies and approaches, including tariffs, are codified as international treaties – the International Telecommunication Regulations (ITRs) – policy changes are fraught with all the complexities that accompanies any international treaty. Indeed, before the 2012 WCIT, the ITRs were last updated in 1988. The languid pace of treaty change is but one of many challenges posed by the ITRs. It has also brought into question the relevance of the ITU and its policies and approaches. The country voting model and UN culture do not match the freewheeling Internet world.
Looking back twenty four years to the previous WCIT takes one almost to prehistory in Internet time. The TCP/IP protocols were in place, but international networking was largely a research or private network curiosity and dialup modems defined the consumer network experience. The World Wide Web was still a gleam in the eye of Tim Berners-Lee; the Mosaic web browser had not burst on the scene; the dot.com frenzy was yet to come; mobile phones were rare, expensive and bulky; and plummeting international telephony charges due to IP-based audio and video calling were still over the horizon. In short, the telecommunications world of 2012-2013 is radically different from the staid landscape of 1988.
The technological and economic changes wrought by the Internet in those fourteen years are equaled by the social and cultural changes. Geographically and politically separated regions are now digitally interconnected in ways unthinkable just a few years ago. The global flow of information has challenged governments, companies, NGOs and individuals to adapt legal frameworks, regulations, technical operations, social expectations and national security operations. (See Globally Connected, Globally Worried and Information Privacy: Changing Norms and Expectations for a bit of perspective.)
One can parse Internet governance challenges in several ways: technical, legal and economic, social and ethical and national security. Let's begin with the technical issues, which are perhaps the simplest. Operating the global Internet effectively requires de facto adherence to an evolving set of technical standards. The Internet Corporation for Assigned Names and Numbers (ICANN) manages the domain name system (DNS) and IP address blocks on behalf of the international community.
Although not without controversy, ICANN and its sister organization, the Internet Society's Internet Engineering Task Force (IETF), have generally been effective in creating a multistakeholder governance model for Internet operations. However, the tizzy over generic top-level domain names (gTLDs) and intellectual property protection has heightened government desires to control ICANN.
Shifting to economics, companies, countries, NGOs and privacy advocates all worry about the transnational flow of data, differing laws and intellectual property protections, jurisdictional constraints and safe harbors. If you are a Kenyan national working for a German company with operations in Brazil, and you travel to India, just whose laws govern you and your company? The answer is murky, but generally all of the above, plus some others.
Nevertheless, Internet service operators must respond to civil and criminal legal requests for data every day. For those subject to multiple legal jurisdictions, it is sometimes impossible to satisfy the laws of countries involved. For those interested and suffering from insomnia, I highly recommend reading the Bank of Nova Scotia case (United States v. Bank of Nova Scotia, 740 F.2d 817 (11th Circuit, 1984).
Mixed with all of these legal and economic issues are elements of international trade, protectionism and economic development. The rise of cloud computing, whose economics have driven massive scale and data consolidation, has exacerbated these concerns about extraterritorial jurisdiction and control worldwide, particularly when U.S. companies dominate cloud computing and many fear the U.S. PATRIOT Act.
Then there are the crucial issues of human rights and free speech. How does one reconcile global communication, which brings differing international norms and expectations for freedom of speech into direct and day-to-day conflict? It is more than an abstract question for millions of Internet users, and one with no simple answers.
One can debate the ethics, human rights records and legitimacy of certain governments, but the sovereignty of nations and their right to establish laws within their territory is an unquestioned aspect of international law. I have seen senior representatives of governments with widely divergent views on freedom of expression all agree that their governments have a critical role to play in Internet governance. Those same representatives differed markedly in their delineation of appropriate governmental roles, their definitions of free speech and its appropriate limitations.
Finally, there are issues of national security, information security and cyberwarfare that are beyond the scope of this essay. In a knowledge economy, information is advantage, and economic or technical disruption via the Internet itself can be a form of low-grade warfare. Likewise, with military capabilities themselves increasingly dependent on smart, network-connected weapons, they are themselves objects of defense and attack.
This interplay of technology, international law, economics and trade, social norms and human rights, and national security is a witch's brew of complexity, with diverse stakeholders and perspectives. Many of them encamped in Dubai at last December's WCIT.
Thus, it is not surprising there was acrimony and controversy, with claims and counterclaims; one could hardly expect otherwise. Central to these debates were concerns, some say unfounded, though others disagree vehemently, that the U.N., via the ITU, might assume greater control of Internet governance, shifting from the multistakeholder model toward greater centralization and government control.Some of this was also tied up in the ITU's own search for new relevance.
What emerged from two weeks of painful negotiations was what can best be described as an uneasy peace. After much debate, 89 of 152 countries signed an amended version of the ITRs. The U.S., Japan, Canada, Germany, India, and the U.K. were not among the signatories, objecting to attempts to curtail multistakeholder governance. In short, it was an ugly mess.
What's next? The global community is strongly divided along ideological lines. Thus, we are likely to see even greater dichotomy in government intervention, more uncertainty in international law, and limits on global information flow. Despite this, I believe the Internet will continue to grow and evolve organically. Too many stakeholders want that to happen, and their voices must be heard.
Thus, I am a strong proponent of the multistakeholder model. I do recognize, though, that governments have an important role to play, just as they do in other domains. This is a messy process, and it will undoubtedly continue that way. Such is the nature of debate.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.
A substantial fraction of my professional life has been devoted to analyzing and optimizing the dynamic behavior of some of the world's largest high-performance computing (HPC) systems. During the past thirty years, spanning my time at Illinois, North Carolina, Microsoft and now Iowa, my students, post-doctoral associates, research staff and I have used and developed many performance, energy consumption, and reliability instrumentation and measurement tools and techniques, embodied them in scalable data capture and reduction software, and created correlation tools that connect measurements to potential remediation opportunities. Multiple instantiations of these tools have been released as part of the Pablo performance analysis toolkit and Autopilot toolkit for adaptive performance control.
Most of this work originated in a challenge from my Ph.D. thesis supervisor, Herb Schwetman, issued while we were both at Purdue. After listening to me expound excitedly about the virtues of yet another interconnection network for parallel systems, he challenged me to prove that the object of my excitement would deliver higher performance than other alternatives. I dutifully promised him that I would spend a few weeks on the performance analysis problem, then return to the core parallel systems design question.
What ensued changed my thesis direction dramatically, as the two-week project took three years to complete. Over thirty years later, I find myself still working to answer generalizations my advisor's basic question: "How fast is system A compared to system B and what are the bottlenecks in each?" These are beguilingly simply questions, but they cut to the heart of system design and optimization. They are also especially pertinent as we consider the design of putative exascale systems with billion-way parallelism, and millions of components that must operate reliability, all while consuming tens of megawatts of power.
Beneath all this complexity is a simple, yet subtle law that that relates performance (throughput), delay (response time) and number of interacting units (customers). It has been my friend and nemesis throughout my research career.
Generally known as Little's Law, it relates throughput (X), response time (R) and customers (n) in a stable system in a linear way: n=XR. This general relationship holds for computers, airline reservation systems, store checkout lines, or any other system where entities (customers or computer jobs) compete for service from a resource-limited system.
Little's Law also says there is no free lunch. Optimizing for high system utilization leads to large wait times for customers, as anyone who has waited on a busy telephone reservation line knows all too well. Conversely, ensuring short waiting lines requires system utilization to be low. Simply put, one must choose between high system efficiency and short customer wait times. One cannot (in general) have both.
As simple as this may seem, in reality, the best optimization strategy depends on many other constraints and the broader environment. For many systems, the relative costs of the components providing and receiving service determine the appropriate optimization axis. When humans are involved, their psychology and expectations must be managed appropriately. That is why many telephone response lines provide waiting time estimates and periodic feedback during the wait. Those of us who have managed batch-scheduled HPC systems also know that users and system operators constantly balance system utilization against the product of queue wait time and parallelism.
Exascale computing brings many of these issues into focus in new ways. When a multiple megawatt energy spike is imposed on the electrical grid, utility operators notice. Thus, exascale system energy requirements may constrain when parallel job execute, as jobs with high floating-point computation intensity draw more energy those dominated by pointer chasing. Likewise, component reliability may limit maximum parallelism within exascale jobs; the mean-time-to-failure (MTBF) of processors, memory and storage systems, and interconnect elements really matters. Although more job parallelism normally reduces execution time, component reliability bounds effective parallelism and maximum computation time without either resilience or recovery mechanisms.
Performance, delay and parallelism at large scale: Little's Law speaks to all of these issues and more. When performance optimization, reliability requirements, and energy management are convolved with component costs, device physics, system software services and application characteristics, the constraint-based optimization problems become dauntingly complex. Their solution is a bit more than a two-week project, especially at exascale.
N.B. I also write for the Communications of the ACM (CACM). The following essay recently appeared on the CACM blog.
Computing research and advanced computing infrastructure – each is dependent on the other in a myriad of subtle and complex ways, yet each is profoundly different in culture, process, skills and metrics. As a computer scientist who has often conducted research on, toward and with large-scale infrastructure, both for computational science and for big data analytics in clouds, and as someone who has overseen large-scale, production infrastructure, both as director of a national facility and as a university CIO, I have seen this creative tension and intellectual dichotomy firsthand.
Convolving the two inappropriately can be frustrating at best and disastrous at worst. Conversely, when each of the constituent communities acts appropriately, there are enormous benefits to both. As we think about next-generation capabilities and the balance of investment in basic research and deployed infrastructure for data analytics and computational science, it is salutatory to consider some insights from our experiences, reflected, sometimes unthinkingly, in the language we use. The prepositions, such simple words, connecting the two, make all the difference. About, on, before, beyond, for, toward, with: they are all different, and we confuse them at our peril.
First and perhaps most obviously, the metrics of success in research and infrastructure differ markedly, though both operate in a highly competitive world. The research culture rewards novelty and new ideas, intellectual risk taking, rapid prototyping and experimentation, and early publication. At its best, research pursues basic questions and long-term outcomes. Students and post-doctoral associates learn as they are doing, and the resulting prototypes are often buggy and usable only by the researchers themselves. Often, though not always, the experimental scale is small, limited by budgets and staff, with localized consequences for failure and little expectation that others will depend on the outcomes.
Conversely, the production infrastructure culture, whether for big data analytics or computational science, rewards operational stability; quality of service, both technical and human; and long-term compatibility, continuity and interoperability. It also prizes consultation with others, operational experience, planning, process, testing and documentation. Operationally, the production culture is inherently conservative, for failure at scale can affect thousands to millions of users. Thus, change for change's sake is rare. Rather, adoption and deployment are driven by shifting costs and technological capabilities, user expectations and needs and by competitor behavior. These competitors can be in the business world or in the world of international scientific instrumentation and facilities.
Computing researchers rarely build and successfully operate production infrastructure; they lack the skills and production mindset. Likewise, infrastructure operators rarely ask the fundamental questions; they rightly focus on operational priorities and their demanding user community. Yet each can benefit from the other. Lest these characterizations seem unnecessarily pedantic or abstract, consider again the prepositions and their implications.
Conducting research on or with production infrastructure requires collaboration and deep partnership with the infrastructure operators. Moreover, under no circumstances can it compromise infrastructure security, reliability or quality of the service. Thus, there must be a clear value proposition for the infrastructure operator and its user community, other than just the goodwill of the computing research community. The payoff might be optimized production infrastructure based on experimental insights, data to shape next-generation vendor capabilities, or better algorithms for data analysis, but it must be weighed against the risks.
As a personal example, my research group spent years exploring performance measurement and analysis techniques for large-scale HPC systems, using systems at the NSF and DOE supercomputing centers for testing and measurement. Because these centers supported a national base of computational scientists, it was critical that my team's prototype tools not adversely affect either the HPC system or its users. Because these tools typically captured data at many levels, from hardware, system software, runtime libraries and applications, this was no small task. It required careful planning, testing and scheduling. The rewards, however, were substantial – data and insights at a scale simply unattainable in the laboratory and influence on next generation capabilities.
When I became director of the National Center for Supercomputing Applications (NCSA), one of those NSF-funded national facilities, I found myself on the other side of the equation, responsible for deploying and operating a portion of the national research infrastructure. This sharpened my awareness of fiduciary responsibility and the risk asymmetry. Make no mistake; in research collaborations, the infrastructure operator bears the majority of the risk. These span a wide range, but include compromised functionality from faulty experiments and security or privacy breaches due to data analysis.
There is a reason firms with web search logs and other social network data rarely release even sanitized and anonymized data sets for academic research. Even inside a company, production infrastructure and data are accessible to only a few.
What are the best ways to transfer innovative ideas and form mutually beneficial partnerships? It is neither easy nor simple. (See Technology Transfer: A Contact Sport for a few thoughts on this topic.) Crossing the "valley of death" from idea to realization requires understanding the entire set of optimization constraints and considerable patience. Indeed, repeated U.S. National Academies studies have shown that the time lag to translate basic research ideas in computing into a billion dollar industry is over a decade. (See the famous "tire tracks" diagram.) From supply chains and availability through competitive pressures and time to market to ecosystem economics and labor force, these constraints may seem prosaic or mundane to the research community, but they are very real.
As a researcher, this often leads to a difficult lesson, that of idea valuation. Simply put, an intellectually interesting idea is not necessarily a helpful idea or even an economically valuable one. This is an often-painful realization. I have seen many cases where a new and improved algorithm or hardware design was rejected because it was not backward compatible with existing infrastructure; it solved a problem but not the most pressing one; it was too costly to implement; it would have required retraining and a cultural shift; or it was simply the right idea at the wrong time.
As the scale, scope and influence of computing grow ever larger, the stakes are high, for both computing researchers and those who build and deploy large-scale computing infrastructure. Each needs to understand the constraints and needs of the other to find common ground.
What does all of this mean for today's world of HPC and big data? First, we must continue to support the basic research needed to advance the state of the art. There are deep and unresolved research questions regarding resilience and reliability, energy efficiency and performance, programmability and expressivity, privacy and security, among many others, which span algorithms, software, hardware and architecture.
Second, advanced HPC and big data infrastructure enable discovery across all scientific and engineering domains. There are important problems in biology and medicine, physics and materials sciences, weather prediction and climate modeling, and business and consumer services that depend on continued advances in our large-scale infrastructure. It is crucial that we continue to fund and deploy such capabilities at the largest scales possible.
Third and critically, we should not confuse the two. The two communities, computing research and research infrastructure for science and engineering, can and should learn from the other, but they are different, with divergent constituencies and cultures; disjoint timescales and needs; and complementary outcomes and rewards. One cannot be sacrificed for the other.
Remember the prepositions. They really matter.
Today, I testified to the Subcommittee on Technology and Innovation of the U.S. House of Representatives Committee on Space, Science and Technology. The topic of the hearing was "The Next IT Revolution?: Cloud Computing Opportunities and Challenges." At the hearing, I was joined by Mike Capellas (CEO, VCE), Nick Combs (Federal CTO, EMC), and David McClure (Associate Administrator, GSA).
This hearing drew on some of the content from the TechAmerica Commission on the Leadership Opportunity in U.S. Deployment of the Cloud (CLOUD2), on which I had the privilege to serve as the commercial vice-chair. In addition, my own remarks were augmented by Microsoft's cloud engagement experiences with the U.S. National Science Foundation and other research funding agencies. (See Innovation Via Client Plus Cloud: Microsoft-NSF Partnership for background.)
My complete written testimony will ultimately appear on the Subcommittee web site here, and the hearing charter, the subcommittee press release, and the hearing video capture most of context. However, there are a few points from my testimony that seem appropriate to repeat here. These concern research empowerment via the cloud, support for basic research and education and broadband expansion.
Throughout the history of science, data has been scarce and precious. Indeed, the modern scientific method is defined by a careful cycle of hypothesis and experiment, which gathers experimental data to test the hypothesis. In a few short years, scientists and engineers have gone from scarcity to an incredible richness, necessitating a significant change in how they manage and extract insight from all this data. In a parallel shift, many of our scientific, engineering and societal questions increasingly lie at the intersections of traditional disciplines.
Increasing data volumes and the complexity of collaboration on interdisciplinary problems are challenging our historical approaches to discovery and innovation via computing. Most researchers and research institutions are ill-prepared for the large-scale computing infrastructure management challenges posed by large data sets and complex models. The cloud and associated applications and tools offer a possible solution to this challenge by letting scientists be scientists.
The U.S. government can accelerate this transition by encouraging the purchase of cloud services, in addition to the acquisition of local IT infrastructure, and by supporting new tools that facilitate distributed collaboration and simplify access to multidisciplinary scientific data. As I have noted before, Microsoft is acting on this belief, working in partnership with the National Science Foundation.
Today's cloud technology is derived from basic computing research conducted over the past four decades. To ensure that the U.S. continues to remain at the forefront of cloud technology, continued investment in basic research is critical. There are deep and open questions in areas as diverse as the future of silicon scaling and system-on-a-chip design, energy-efficient systems, primary and secondary storage, data mining and analytics, wired and wireless networks, system resilience and reliability, privacy and security, and user interfaces and accessibility, to name just a few. Insights and innovations from this research will spawn new companies, create jobs and reshape our future.
In addition to continued research investment, it is critical to support the pipeline that produces researchers, and others who will able to invent new uses of the cloud and information technology. The U.S. Bureau of Labor Statistics estimates that the computing sector will have 1.5 million job openings over the next 10 years, yet the number of graduates receiving Bachelors, Masters or Ph.D. computer science degrees is far short of that. In addition, we must strengthen the quality of and access to computing education at all levels. Consistent with these concerns about the IT workforce and computing education, Microsoft is a founding member of the Computing in the Core coalition, which supports computer science education, particularly at the K-12 level.
The web and cloud services depend on broadband communications. Without them, service and information sharing are impossible. Concomitantly, ensuring reliable wired and wireless connectivity, with adequate bandwidth and latency, is critical to ensuring successful adoption of the cloud and realization of its benefits. The phenomenal growth of digital data, the rise of streaming media services, and the explosive growth of Internet-connected devices are all straining our nation's broadband infrastructure.
It is critical that we continue to design and deploy new backbone networks that support higher data rates, develop and deploy new protocols and infrastructure for the next generation of wireless networks and define the global standards that will shape the future of the globe-encircling cloud. We must also remember that digital access to information and services is increasingly the enabler of economic competitiveness, of lifelong education in a rapidly changing world, and of government efficiency and service delivery.
This is an exciting time, when the future becomes the present. Computing can be a great equalizer, providing access to the world's knowledge base to individuals, anywhere, anytime; empowering entrepreneurs and companies large and small to sell their products and ideas globally; and enabling scientists and engineers to discover and innovate in ways that will define the future.
As a boy, I remember reading all the superhero comic books, from the standard Superman and Batman to the Green Lantern, Thor, The Flash and Spider-Man. Some superhero stories and backstories were better than others, but all were escapism from small town life. For me, Thor was always a favorite, though I am not quite sure why. Maybe I just liked the head gear! The summer "action movie" season is upon us, and several of these superheros will grace the screen in new Hollywood fantasies, battling the forces of evil.
All of this made me think about the resident evil now facing us in computing – the rise of dark silicon. The very phrase sounds ominious and it is, for I believe it will profoundly reshape how we think about computing in the next decade. But before we talk about the grand denouement where our Justice League of silicon process experts, computer architects and software gurus do battle with dark silicon, perhaps we should begin with a bit of backstory.
We are all familiar with Moore's Law, namely that the number of transistors in a given area doubles roughly every two years. It is worth noting that Gordon Moore never really claimed this was a law, merely that it was a statistical truth based on observational data and technology trends. It is also interesting how a rather informal 1965 Electronics paper, entitled "Cramming More Components onto Integrated Circuits," had such impact. Over the past thirty years, the "law" has held true due to the hard work and creativity of a great many people, as well as many billions of dollars of investment in process technology and silicon foundries.
For a detailed understanding of why reducing transistor feature sizes really has been possible, I highly recommend reading the original, seminal paper by Robert Dennard, Fritz Gaensslen, Hwa-Nien Yu, V. Leo Rideout, Ernest Bassous and Andre LeBlanc, entitled "Design of ion-implanted MOSFET's with Very Small Physical Dimensions." It appeared in the October 1974 issue of the IEEE Journal of Solid-State Circuits, and it defined a scaling process that has driven the semiconductor industry ever since.
Simply put, Dennard scaling says that reducing the length, width and gate oxide thickness of transistor features by a constant k results in transistors that are k2 times smaller, k times faster, and dissipate k2 less power. This was Moore's Law made manifest and has let us move from feature sizes measured in micrometers to nanometers. Since the elucidation of Dennard scaling, all has been well in Metropolis, at least until very recently.
As transistor sizes continued to shrink and clock rates continued to increase, we have seen the rise of high-k dielectrics and silicon on insulator (SOI) to address static current leakage. In that spirit, one of my colleagues recently remarked that transistors are now less like solid doors that keep out the cold (current flow) unless open and more like screen doors that always have some air flow (current leakage) even when closed.
Transistor density, clock rates and chip sizes have also made heat dissipation an ever bigger problem. While at Intel, Pat Gelsinger once remarked that unless things changed, chip temperatures would approach that of the sun's surface, an obvious absurdity only possible in the comic books. All of this has led to multicore designs, bounds on clock rates for most microprocessors and decreases in operating voltages.
We have also seen new semiconductor processes and transistor design options to reduce leakage current. (Intel's recent FinFET announcement is one such example.) Although I expect to see transistor feature sizes continue to shrink, with sub-11 nanometer feature sizes achievable, it will not be easy, nor will it be cheap. Lest there be any doubt, the cheap part really matters, for it is the economics that will determine if scaling continues.
These semiconductor challenges have been mirrored by an increasingly empty bag of general purpose architectural tricks. Almost all of the techniques once found in supercomputer processor designs – superpipelining, scoreboarding, and vectorization – are now in mainstream microprocessors. It is not obvious that we can extract more parallelism while maintaining the illusion of sequential execution. In fact, the evidence is substantial that we cannot.
That's the backstory. Now let's turn to the true evil lurking among us, dark silicon. If one considers process scaling trends and limits on heat dissipation, the conclusion is rather obvious. We soon will have (and in many cases already do have) chips with more transistors than can be concurrently activated. The practical implication is that most of the chip silicon will be "dark" (unpowered) most of the time.
If it's dark most of the time, is it of any value? Yes, but only if we embrace our fears. This is the true end of the hegemony of von Neumann computing and the birth of a new era, one based on chips with a diverse set of specialized functional units, accelerators and tailored architectures that each target specific tasks. The mobile world has already learned this lesson, for functional specialization increases energy efficiency (more operations/joule) and allows designers to mix and match the set of functional units to target domains. Arguably, heterogeneous multicore is one, very tentative step in this direction for client and server computing.
This transition to diverse functional units, accelerators and tailored architectures will also challenge many of our assumptions about programming models and system software, as well as the economics of chip design. In this new world, hardware/software co-design becomes de rigeur, with devices and software systems deeply interdependent.
It is a different ecosystem, with different cultures and different processes. It means far less general-purpose performance increases, much more diversity in the hardware, elevation of multivariate optimization – power, performance, reliability – in programming models, new system software resource management challenges and a plethora of new applications.
More generally, with lots of silicon real estate (dark silicon), chip designers and architects have the space to place functional blocks that are used only part of the time. However, at some point (and we are arguably close to that point now), we will have deployed those functional units that cover the common tasks and adding more will have possibly deleterious effects due to larger chip size. That potentially drives the economics to smaller chip sizes, which, given foundry capacity, leads to lots of small chips. That is the collision of the big chip server world with the small chip mobile and Internet of Things world, a battle already raging.
To address these challenges and battle dark silicon, we need new ideas in computer architecture, system software, programming models and end-to-end user experiences. It's an epic struggle for the future of computing. Giants are battling, and not all will survive, nor will the survivors emerge unscathed. Heroes will also emerge, sometimes from unexpected places.
I think I left my hammer, Mjölnir, around here somewhere. Do you feel your spider sense tingling?
We all remember some variant of the fairy tale of Goldilocks and the Three Bears. It is a tale of the search for a good meal and a nap, though it also involves breaking and entering, culinary theft and accidental vandalism. If, perchance, you feel a worrisome gap in your erudition and erstwhile encyclopedic knowledge of this element of the Western cultural lexicon, fear not, for I will summarize the key elements of the tale. And please bear with me, this does relate to science! (Pun intended.)
The young girl, Goldilocks, visits the home of the bears and samples the porridge of Papa, Mama and Baby Bear, pronouncing each, in turn, too hot, too cold and just right. Sated with porridge, she then seeks a place to sit, finding the chairs of Papa and Mama Bear too large. As she settles comfortably into Baby Bear's chair, it collapses. She then explores each of the beds before falling asleep in Baby Bear's bed, where she is found by the returning bear family. On discovery, she flees.
This tale, of course, raises worrisome questions. Do bears really like porridge? Could they sit in chairs and would they sleep in beds? More tellingly, are they underwater on their cottage mortage? Though all worthy of whimsical rumination, let us defer literal scrutiny of the fairy tale's purported ursine facts and the anthromophrized bears and focus on the porridge bowls and their metaphorical implications for science.
Like porridge bowls, science comes in many sizes, from research conducted by a single investigator (e.g., a theoretical computer scientist studying computational complexity) to experimental projects that involve thousands of people (e.g., the ATLAS and CMS detectors at CERN). More to the point, there is no "right size" for science, other than matching the resources to the nature of the problem and the approach, albeit with the usual assessment of investment versus potential payoff. Nor is there, a priori, differential value between large and small science. Groundbreaking discoveries with broad and transformative effects have occurred across the entire spectrum of project sizes.
Scientific culture does vary widely with discipline and scale, however. Just as the ethos and metrics of biology differ markedly from those in physics, so too do the approaches and politics of small and large science. Large-scale experimental science typically involves long-term planning, geo-political negotiations, and infrastructure construction that may span multiple years before data can be captured and analyzed.
Historically, these two worlds – small-scale science (both theoretical and experimental) and large-scale science – have largely depended on separate and distinct infrastructure. That historical separation is now disappearing, mediated by the tsunami of data now being produced by new generations of scientific instruments and computational models. (See The Zeros Matter: Bigger Is Different and Language Shapes Behavior: Our Poor Cousin, Data.)
Historically, the researcher who had unique experimental infrastructure also maintained a competitive advantage, for he or she could conduct experiments and capture unique data. With the rise of large-scale, shared instrumentation in a host of disciplines, most notably in biology, astronomy and physics, and open access to the resulting research data, advantage instead accrues to the researcher who can ask and answer more interesting questions. This is a profound shift in scientific culture with deep implications.
It is now incumbent upon us to rethink how we facilitate discovery and innovation in this brave new world of large data, for practitioners of both small and large science. Simply put, we must reconsider how we fund, construct, manage and operate scientific data repositories.
As I have learned at Microsoft, there are lessons that can be drawn from the construction of cloud data centers, web search engines and tools for analyzing ill-structured data that could both accelerate and simplify scientific discovery. These tools and practices are not only applicable to individual scientific domains, they are also especially relevant to the problems that lie at the intersection of multiple disciplines, where scientific cultural divides and divergent terminologies often inhibit collaboration and exploration. We will also need new models of public/private partnership to realize this vision, something we are pursuing with Microsoft's worldwide engagements on client plus cloud infrastructure.
Remember the porridge, the bears and Goldilocks. We can match the needs of all scientists, working together. Whether you like it hot or cold, large or small, it can be "just right" for everyone.
If you have ever designed a house or been part of the design process for a building, you know that successful designs are the gestalt of many ideas and often conflicting objectives. Collaboration spaces, meeting rooms, window offices, LEED certification, storage and loading dock access, utility routing, computer spaces, telecom closets, and environmental safety – the list is seemingly endless and desires always exceed budgets. However, when the conflicting objectives are harmonized, the result is both functional and elegant, satisfying both esthetic and practical expectations.
In each design, a senior architect is ultimately responsible for eliciting harmony from the cacophony of competing objectives. Although he or she inevitably works with a cadre of designers and subsystem experts, each senior architect brings a certain panache and philosophy to each project. The great ones and their work are instantly recognizable. Look no further than I. M. Pei and the Louvre pyramid, Frank Gehry and MIT's Stata Center, or Mies van der Rohe and the Seagram building to see this elemental truth embodied in steel, masonry and glass.
It is worth pausing to consider the exegesis and etymology of the phrase "computer architect" and the phrase's broader relationship to architecture in the grand sense of style and design. In this context, a computer architect is far more than a system integrator, but someone who combines disparate elements to create something compelling and new that elegantly and efficiently solves an important problem.
Simply put, computer architects create something of functional beauty, just as do their cousins who work in the more tangible media of rock and stone. Consider John Cocke and the IBM 801, Ken Batcher and the Goodyear MPP, Tadashi Watanabe and the NEC SX. Similarly, Seymour Cray's designs balanced many aspects of power engineering, packaging and cooling, memory bank design and functional unit architecture, and his success arguably rested as much on his prowess as a packaging and cooling designer as it did on his acumen as a processor and memory system designer.
The facts differ, but the story is the same. The sum has always been more than the parts.
As we debate possible exascale designs, evaluate eco-friendly mega data center approaches, ponder the implications of heterogeneous multicore processors, struggle to overcome the memory wall and explore alternatives to rotating magnetic storage, we should remember the broad meaning of architect and its implication for multidisciplinary design. In an era of hyper-specialization and local optimization, we often forget the importance of global optimization. Sometimes it really is better to know something about everything than everything about almost nothing.
Whether building or machine, effective design incorporates and balances diverse elements and capabilities. The sum has always been more than the parts.
If you have ever been sent to the henhouse to fetch eggs, you know the practical truth of the old aphorism, "Don't put all your eggs in one basket." With just one stumble – wait for it – you can have egg on your face. (Sorry, I just could not avoid the pun.) All of which is to say, distributing one's assets is normally a successful risk mitigation strategy. At least that is what we all thought about our retirement mutual funds until the world financial markets collectively declined.
I hear rustling among my faithful readers, collectively wondering how these colorful, though meandering, introductory paragraphs will segue to more salient topics, such as clouds or high-performance computing. Fear not, the answer is here, manifest as a few observations on massive data centers, wide area networking, and techniques for geo-distribution and resilience.
Every operator of massive cloud data centers chooses the locations of those facilities after careful, qualitative and quantitative analysis of several factors, including the tax climate, availability and cost of electricity, the natural stability of the site (i.e., probability of earthquakes, tsunamis, hurricanes and other natural disasters), the region's political stability and physical security, access to optical fiber and broadband networks and a host of other issues. Given an objective scoring of these criteria, it is not surprising that multiple operators and competitors often site their facilities in the same region.
Regardless of location, a substantial fraction of the infrastructure associated with a data center has historically been devoted to electrical power and resilience. This has typically included multiple diesel generators, collectively capable of generating many megawatts of electricity, and large, lead acid battery banks to ensure an uninterruptable power supply (UPS). Not only are these generators and battery banks expensive, their deployment and acceptance are time consuming and often difficult. Indeed, before the economic downturn, demand for generators was so high that sites competed to secure a place in line for delivery, further delaying data center construction.
The reason we strive to protect data centers is obvious, their failure often has catastrophic business implications. Without doubt, though, some workloads are less affected by failure than others. If your web search for basket weaving supplies times out, you are probably content to retry the query a few seconds later. If your attempt to file a tax return electronically fails, that is more problematic.
This focus on resilience raises one rather obvious question, is there a better way? Returning to my initial metaphor, rather than protecting the one big basket of eggs, might one distribute the eggs in several, smaller baskets?
If it were possible to replicate data and computation across multiple, geographically distributed data centers, one could reduce or eliminate UPS costs, and the failure of a single data center would not disrupt the cloud service or unduly affect its customers. Rather, requests to the service would simply be handled by one of the service replicas at another data center, perhaps with slightly greater latency due to time of flight delays. This is, of course, more easily imagined than implemented, but its viability is assessable on both economic and technical grounds.
In this spirit, let me begin by suggesting that we may need to rethink our definition of broadband WANs. Today, we happily talk of deploying 10 Gb/s lambdas, and some of our fastest transcontinental and international networks provision a small number of lambdas (i.e., 10, 40 or 100 Gb/s). However, a single mode optical fiber has much higher total capacity with current dense wave division multiplexing (DWDM) technology, and typical multistrand cables contain many fibers. Thus, the cable has an aggregate bandwidth of many terabits, even with current DWDM.
Despite the aggregate potential bandwidth of the cables, we are really provisioning many narrowband WANs across a single fiber. Rarely, if ever, do we consider bonding all of those lambdas to provision a single logical network. What might one do with terabits of bandwidth between data centers? If one has the indefeasible right to use (IRU) or owns the dark fiber, one need only provision the equipment to exploit multiple fibers for a single purpose.
Of course, exploiting this WAN bandwidth would necessitate dramatic change in the bipartite separation of local area networks (LANs) and WANs in cloud data centers. Melding these would also expose the full bisection bandwidth of the cloud data center to the WAN and its interfaces, simplifying data and workload replication and moving us closer to true geo-dispersion and geo-resilience. There are deep technical issues related to on-chip photonics, VCSELs and ROADMs, among others, to make this a reality.
In the end, these technical questions devolve to risk assessment and economics. First, the cost of replicated, smaller data centers without UPS must be less than that of a larger, non-replicated data center with UPS. Second, the wide area network (WAN) bandwidth, its fusion with data center LANs and their cost must be included in the economic assessment.
These are interesting technical and economic questions, and I invite economic analyses and risk assessments. I suspect, though, that it is time we embraced the true meeting of high-speed networking and put our eggs in multiple baskets.
Over the past year, there has been much debate on the energy efficiency of large data centers and how best to measure that efficiency. Power Usage Effectiveness (PUE) has emerged as the de facto standard, where PUE is defined as the ratio of power entering the facility to power drawn by the computing equipment. Intuitively, the ideal PUE is unity (i.e., all incoming power is used by the computing equipment, with no overhead for cooling or energy distribution). For details on PUE and data center efficiency, I encourage you to read the documents from the The Green Grid, which has promulgated and evangelized these measurement standards. (Truth in advertizing: Microsoft is a leading member of the The Green Grid.)
Many legacy data centers -- those built more than a few years ago – have PUEs in excess of two, or even three. This is largely due to inefficient computer room air-conditioning (CRAC) units, lack of hot and cold aisles, energy losses due to multiple (unnecessary) voltage conversions and aging or inappropriate building designs. If your "data center" is located in the nearest available space to your laboratory -- a retrofitted janitor's closet – and cooled by two box fans from Walmart, odds are you are not a paragon of PUE virtue, even if your aggregate computing power is small.
Today, state of the art data centers have PUEs below 1.5, and there are new designs that could approach a PUE of one by reducing UPS support where appropriate, operating at substantially higher temperatures and exploiting ambient cooling. Many people do not realize that computing hardware is much more resilient to high temperature than history and practice would suggest. It need not be chilled to temperatures suitable for polar bears.
Last year, Microsoft's Christian Belady illustrated this point by operating a group of servers in a tent for over six months, with zero failures. Although not a statistically valid sample, the experiment did illustrate why ASHRAE and hardware vendors have broadened the temperature and relative humidity envelope of acceptable operation.
More generally, efficient data center design and analysis are much like Matryoshka dolls, because the PUE one obtains depends on where and what one measures. Ideally, the bounding box for measurement should be the entire data center ecosystem, from power distribution and cooling for servers to data center networks to operations and support. Equally importantly, a PUE near one does not mean the computing system itself is efficient and well-matched to the offered load or that the computations are either necessary are useful. Choose wisely and measure thoughtfully and carefully.
Finally, I would be remiss if I did not opine on the most obvious, visual difference between cloud data centers and high-performance computing (HPC) facilities. The former are designed for function, not appearance. They are usually nondescript facilities optimized for efficient hardware operation at large scale, not for human accessibility or for comfort. Indeed, container-based data centers look more like a warehouse and distribution center with parking and utility connections than Hollywood's idea of a computing center. Conversely, HPC facilities are usually showpieces with signs, elegant packaging and lighted spaces suitable for tours by visiting dignitaries.
At large scale, efficient trumps pretty. It's all about what one measures.


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Fullmetal Alchemist Blind Alchemist Anime Online
Anime Fullmetal Alchemist Blind Alchemist
4.29 (206 Votes)
Summary:The DVD/Blu-ray Disc of "Fullmetal Alchemist: Brotherhood" volume 1 was announced to be released on August 26th. Along with episode 1 and 2, an extra episode "Moumoku no Renkinjutsushi (Blind Alchemist)" will be included in the volume.The extra episode is based on a side story of "Fullmetal Alchemist" manga published in "Fullmetal Alchemist Perfect Guidebook" (2003).It's about a blind alchemist Judau, who serves the Humbergang family. He once violated the taboo of the Alchemy. More
Genre: Adventure, Drama, Fantasy, Magic, Military, Shounen

subset: commoncrawl


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IMF Executive Board Concludes 2008 Article IV Consultation with IndonesiaPublic Information Notice (PIN) No. 08/106
August 12, 2008
Public Information Notices (PINs) form part of the IMF's efforts to promote transparency of the IMF's views and analysis of economic developments and policies. With the consent of the country (or countries) concerned, PINs are issued after Executive Board discussions of Article IV consultations with member countries, of its surveillance of developments at the regional level, of post-program monitoring, and of ex post assessments of member countries with longer-term program engagements. PINs are also issued after Executive Board discussions of general policy matters, unless otherwise decided by the Executive Board in a particular case.
On August 1, 2008, the Executive Board of the International Monetary Fund (IMF) concluded the 2008 Article IV consultation with Indonesia.1
Since the conclusion of the last Article IV consultation in mid-2007, Indonesia's growth performance has remained strong despite the deteriorating global environment. Growth of 6.3 percent in 2007, the highest growth rate in a decade, was driven by domestic demand, both robust private consumption and a rebound in investment that included a strong rise in foreign direct investment. While exports performed strongly, supported by continuing demand for Indonesia's commodities, the recovery of imports in the second half of the year reduced the contribution of net exports to growth. The growth momentum was maintained in the first quarter of 2008. As a result, poverty and unemployment have declined significantly, though they remain high at 15.4 percent and 8.5 percent, respectively.
Nevertheless, new challenges have emanated from rising commodity prices. Government bond yields have risen by about 250 basis points since end-February as investor sentiment deteriorated due to surging fuel subsidies and inflation concerns. Domestic fuel prices were subsequently increased by about 29 percent. Headline inflation has accelerated to 11.0 percent at end-June, while core inflation reached 8.7 percent at end-May (latest available data), well above Bank Indonesia's (BI) 4-6 percent target range for end-2008. After lowering interest rates to 8 percent and then holding them unchanged since late 2007, BI hiked rates three times since May by a total of 75 basis points in response to rising inflation. Nevertheless, the real policy interest rate calculated on the basis of core inflation has been declining over the year and is currently around zero. Meanwhile, uncertainty remains as international oil and food prices have continued to rise, global growth is expected to slow in 2008.
The external position remains generally strong. Staff estimates that the real exchange rate is moderately undervalued. Nevertheless, the nominal effective exchange rate (NEER) depreciated further (by 10 percent) in 2007, which likely contributed to inflationary pressures, while Indonesia accumulated US$14 billion of reserves. However, in 2008 BI has slowed the accumulation of reserves and the NEER has remained stable. While the current account recorded a surplus of 2.5 percent of GDP in 2007 supported by gains in the price of major commodity exports, there have been some episodes of capital outflows and external financing conditions have tightened somewhat as a result of the global financial market turmoil.
Fiscal performance has been strong despite rising fuel subsidies. Energy subsidies rose to 3 percent of GDP in 2007 (almost a third of total current spending). Nevertheless, the fiscal deficit was contained to 1.2 percent of GDP, well below the government's target. Revenue performance was buoyant, with an across the board surge in non-oil and gas revenue being a testament to both the underlying strength of the economy and payoff from ongoing reforms in tax administration. Budget execution has also improved noticeably, in particular in the area of development expenditure.
Indonesia's financial sector has displayed resilience in the face of the global credit market turmoil. Several factors have limited the transmission of global financial shocks, including (i) reduced external vulnerabilities, (ii) low exposure to structured credit and derivative products, (iii) ample domestic liquidity, and (iv) relatively low reliance on external financing. Financial soundness indicators have improved, with banking sector profitability increasing, and gross non-performing loans (NPLs) declining to 4 percent amid strong private credit growth. The stock market index remains about 6 percent above the level at end-June 2007. Nevertheless, volatility has increased, and since end-February, Emerging Markets Bond Index (EMBI) and Credit Default Swaps (CDS) spreads have widened more than in other emerging market countries and the government bond market came under stress in March, reflecting largely investors' concerns about inflation and fuel subsidies.
Executive Board Assessment
Executive Directors welcomed the resilience of the Indonesian economy to the global slowdown and financial market turmoil, underpinned by strong macroeconomic fundamentals and the highly liquid and well-capitalized banking system. Robust domestic demand and buoyant exports supported by high commodity prices have contributed to this commendable performance, and are expected to continue to sustain the growth momentum. Downside risks are associated with a less favorable external environment, a possible slump in commodity prices, and remaining vulnerability to spikes in global risk aversion and contagion from other emerging markets.
Directors saw as a key immediate challenge the need to rein in the inflationary pressures stemming from the large increases in food and fuel prices and strong domestic demand. They welcomed the recent increases in interest rates, and considered that further monetary policy tightening will be needed to put inflation back on a firmly declining path. Directors encouraged the authorities to continue to strengthen the inflation targeting framework, through effective communication, publication of inflation forecasts, and a strong commitment to the medium-term targets. They agreed that the shift of the operational target to the interbank overnight rate has been managed well and will enhance the effectiveness of monetary policy.
Directors considered Indonesia's flexible exchange rate policy appropriate. Noting the staff's assessment that the real effective exchange rate may be somewhat undervalued, Directors welcomed the new policy of increased reselling of oil receipts, which should help strengthen the currency and support monetary policy in dampening inflationary pressures. They underscored that such a policy should be pursued with care to avoid creating perceptions of specific exchange rate targets. At the same time, efforts should continue on the structural front to strengthen external competitiveness. While reserve levels are broadly adequate, some additional accumulation over the medium term could further reduce vulnerabilities.
Directors welcomed Indonesia's strong fiscal performance. They considered the fiscal stance appropriate and consistent with a substantial reduction of the public debt burden. Directors commended the authorities for addressing the challenge of energy subsidies through the recent fuel price increase, accompanied by cash transfers to the poor. Going forward, Directors encouraged the authorities to move toward an automatic fuel price adjustment mechanism, which would be the preferred solution to ensure fiscal space for capital and social expenditures and guard against emerging concerns about the sustainability of subsidies. In the interim, further ad-hoc adjustments would help reduce the cost of fuel subsidies. Directors welcomed the progress made in public finance reforms, and looked forward to the completion of the pending reforms of the tax system and tax administration. To further enhance fiscal transparency, the authorities were encouraged to consider participating in the Extractive Industries Transparency Initiative.
Directors observed that Indonesia's banking system remains relatively resilient to macroeconomic shocks and exchange rate risks. They agreed that the rapid credit growth calls for close monitoring and strict adherence to prudential regulations in line with international standards. Directors supported the planned gradual transition to Basel II, and welcomed the review of the financial safety net, with a view to ensuring timely responses in case of a crisis. They looked forward to Indonesia's early participation in the Financial Sector Assessment Program (FSAP).
Directors welcomed progress toward implementing structural reforms over the past year. Further efforts will nevertheless be needed to achieve higher sustainable GDP growth, aimed particularly at improving the business climate and accelerating infrastructure development. In that regard, Directors viewed the recently announced policy packages as a welcome signal of the authorities' intention to press ahead with the structural reform agenda.


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Prouty, playing his first competitive round of the season, carded a nine-under 62 to take the individual lead. With play suspended, he leads the field with at 12-under par. His 62 was a career low and equaled the
In addition to Prouty, Nathan Tyler (-1), Josh Esler (E) and Travis Esway (+2) were all in the top-25 places at the time play was suspended.
Thirty-six holes were originally scheduled for Monday, but play was suspended due to darkness at 6:15 p.m. MST. Teams still on the course in round two have one hole remaining in order to complete the round. Play will resume Tuesday at 8 a.m. MST.
At the time play was suspended, UNLV led the field with a total of 17-under par. Brigham Young (-14),
The Arizona Junior Varsity also got off to a solid start Monday, as the squad tallied a two-under 282 to open play. Trey
Sophomore Ben Fox was in 87th place to round out the UA varsity competitors.
The 54-hole PING-Arizona Intercollegiate concludes tomorrow at Arizona National Golf Club, a par 71, 6,793-yard layout. For live tournament scoring, please go to www.golfstat.com.
Top-Five Teams (with play suspended)
2. Brigham Young -14
4. UC-Irvine -5


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Making Your Real Estate Transaction Virtually Simple!
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Your Virtual TC is doing our part to help save the environment. We are a "paperless" office. A Real Estate transaction can have up to 100 documents per file. We utilize email as much as possible and have a fax server to receive your incoming faxes by PDF to alleviate the 'paper'work. Your Virtual TC also utilizes DocuSign.com for your electronic signing needs.Using DocuSign's electronic signature process it makes it easy and convenient for your customers, giving you the competitive advantage buyers and sellers are talking about. Two copies of the file on CD are delivered to the Agent after closing (agent copy & client copy) for no additional fee upon completion of your file. You can also choose to download your file by secure link after closing.
Access Your Files Anytime
We now use Google Drive for our online file-sharing for 24/7 access to view and download documents anytime, anywhere.
It would be our pleasure to service your Real Estate Transaction Coordinating needs.


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Want to comment on school bond issue?
Published: Monday, July 14, 2014 at 11:28 a.m.
Last Modified: Monday, July 14, 2014 at 11:28 a.m.
Pender County | The Pender County Board of Education will hold three public input sessions about the bond referendum on the November ballot.
Board members will hear comments from the community on three separate dates:
July 28 at Trask High School;
July 29 at Topsail High School;
July 31 at Pender High School.
All meetings begin at 6 p.m.
The Pender County Board of Commissioners approved a resolution earlier this month to put a $75 million school construction bond on the ballot. The bond includes $21.5 million to renovate Penderlea and keep it as a kindergarten through eighth-grade school, as well as funding to renovate Burgaw Middle, West Pender Middle, Cape Fear Elementary, Cape Fear Middle and Pender High and funding to build a new elementary/middle school in the Surf City area.
– Pressley Baird
Reader comments posted to this article may be published in our print edition. All rights reserved. This copyrighted material may not be re-published without permission. Links are encouraged.


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Superman/Shazam! The Return of Black Adam (2010)
Superman finds a foe who may be too powerful even for him to defeat in the murderous Black Adam. Fortunately, Black Adam's real target is the Earth's Mightiest Mortal--Captain Marvel.
Directed By: Joaquim Dos Santos
Rated: PG
Running Time:
Genre: Action & Adventure, Animation, Special Interest
Release Date: November 9, 2010
DVD Release Date: December 7, 2010


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Printable View
Welcome to the forum from SW Ohio.:wave:
Originally Posted by Foxy
Welcome neighbor, I'm in Henderson nv....
Btw, it took longer than 60 days for my permit to arrive and that was just a couple months ago, the paperwork say up to 120 day...but between 60-90 seems average and just depends how busy they are. :)
Oh I hope not! This wait is killing me! haha. But yeah I am aware it can take up to 120 days. Guess I'm just quite optimistic since a few friends got theirs in a little over 60 days this past year. <crosses fingers>
Welcome from a fellow Vegas resident!
Welcome from Ohio.
Greetings and welcome from central Florida.


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OverDrive announced today that they have have temporarily disabled the Kindle book format from your OverDrive-powered website. Users will not be able to borrow, download, or place a hold on Kindle books during this time. However, Kindle books currently checked out will not be affected.*
This is the result of an issue that developed earlier in the morning when some Kindle users were prompted to 'Buy now with 1-Click' when attempting to download a Kindle book from your OverDrive-powered website, rather than being taken to a page to redeem their checked out eBook title.
Further updates are pending.
Additionally, 3M Cloud users are experiencing slower than usual load times since the iOS 7 upgrade. We are expecting an app update to resolve this issue.
*Kindle access has been restored.
I did not have a problem at this time but am grateful you were able to fix it.
I'm not sure what I would do for books were it not for Overdrive (I spend a lot of time out of country) and my public library -- but I'm glad I don't have to find out. :)


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4 simple steps to get MSP coins.
- Sign Up for My Rewards
- Click 'Go To Store'
- Purchase the item from the store
- MSP coins transferred can be redeemed for free gifts
Return Policy
4 simple steps to get MSP coins.
Design and Specifications
The Lava Xolo Play T1000 is a gaming phone from Lava. Xolo Play T1000 comes with a gorgeous 4.7-inch HD OGS touchscreen display for immersive visuals. A 1.5 GHz Core Nvidia Tegra 3 processor with ULP GeForce Graphics ensures that gaming is a cinch. Also, the 1 GB RAM should be able to handle multitasking effectively. The phone runs Android v4.1.1 (Jelly Bean) Operating system.
Camera and Multimedia
The phone features an 8 MP rear camera with autofocus. The phone has an LED flash too. The camera takes pictures of a decent resolution and the camera can record 720p HD videos as well. Xolo Play T1000 has a 2 MP front-facing camera for video calling and close up shots. The phone has one speaker and also has a 3.5 mm audio jack for connectivity. The phone supports all the regular audio and video formats such as MP3, AAC, WMA, WAV, MP4, WMV, H.264 and H.263.
Connectivity and Features
The Xolo Play T1000 runs on 3G and 2G networks. It has all the regular connectivity features amid smartphones and can connect to the Internet using 3G, EDGE/GPRS and Wi-Fi 802.11 b/g/n. The phone can also be used as Wi-Fi hotspot. The phone has Bluetooth v3.0 and a micro-USB v2.0.
Battery and Storage
The Xolo Play T1000 is powered by a 2000 mAh Li-Ion battery that offers a decent battery life. The phone may come with 8 GB of internal storage and it will also feature a microSD card slot that can extend memory to 32 GB of storage.
Good value for money. Only small issue I found is that the phone's stock video camera app doesn't work well. It becomes dim when record button is pressed and the video captured is quite dark. I downloaded a separate app and it works fine, although there are some frame skips for hd recording. Battery isn't great if you are a heavy user and could barely last for a day. Otherwise the screen is pretty good, using it is a smooth experience with no lags while playing most of the games. Network reception is superb. Call quality is good, could be a little better in terms of volume (I find it a little low). Music playback is superb on speaker, headphone is cheap quality and you need a better piece to enjoy good music.