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Walla Walla repaired, returns to Kingston route Saturday
SATURDAY UPDATE: Repairs were made to the ferry Walla Walla on Friday night and the Kingston route was returned to two-boat service Saturday morning, according to a bulletin from Washington State Ferries.
5:30 p.m. UPDATE: More work is needed on the Walla Walla's steering system and the Kingston route will operate with one boat Friday evening, according to a Washington State Ferries bulletin. The 9:10 p.m. and 10:30 p.m. departures from Kingston, and the 9:50 p.m. and 11:10 p.m. departures from Edmonds have been cancelled.
12:50 p.m. UPDATE: Engineers have repaired the Walla Walla, according to a bulletin from Washington State Ferries. The Walla Walla will resume service with a 1:30 p.m. departure from Kingston.
The Kingston to Edmonds ferry route was reduced to one-boat service Friday morning, while Washington State Ferries crews examined problems with the steering system on the Walla Walla.
The Puyallup is operating a limited schedule on the route, according to a bulletin from Washington State Ferries.
State Ferries reported a two-hour wait time for motorists at the Edmonds terminal 9:50 a.m.
If the repairs sideline the Walla Walla for longer than 24 hours, State Ferries could replace it with a boat from the San Juan Island, Fauntleroy or Bremerton routes. Ferries spokeswoman Joy Goldenberg said the agency is waiting on information from its engineers before deciding how to proceed.
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Latest content added for UNT Digital Library Partner: UNT Librarieshttp://digital.library.unt.edu/explore/partners/UNT/browse/?fq=str_degree_department:Department+of+Sociology&fq=untl_collection:UNTETD2016-06-28T16:28:55-05:00UNT LibrariesThis is a custom feed for browsing UNT Digital Library Partner: UNT LibrariesLiving Arrangements of Elderly Widows in India: Family Convention, Bad Luck and Abandonment2016-06-28T16:28:55-05:00http://digital.library.unt.edu/ark:/67531/metadc849664/

In India, issues of gender discrimination and female empowerment have become more prominent in the last several years. Elderly women, specifically widows, are often abandoned or not well cared for by family members and are typically marginalized within Indian society, vulnerable, and susceptible to poverty. This is an exploratory analysis with a research hypothesis asking, who are the caregivers of elderly widows? Statistics indicate that women may be taking on more of a care giving role with elderly widows which in turn may exacerbate the already existing issues of poverty and neglect for this population. The purpose of this study was to examine in more depth the factors related to living arrangements of elderly Indian widows using the NFHS-3 (2005-2006) data set. Quantitative methods of secondary data analysis and systematic literature review are employed in this research. sociological factors related to family self-reported living arrangement, age of widow, education, caste, socioeconomic level, religion, and geographic region were analyzed using data from the respondents identified as older widows (N=2,176). Findings indicate 78% report living alone or in non-familial households while 22% reported living in various familial constellations. The odds of living with a relative versus not living with a relative were found to be significant for three variables: age, religion Muslim, and region Northern. Living arrangements for elderly widows in Indian society are determined based on a complex system of logic embedded in a patrilineal descent, family convention, religion, and regional cultural practices. Understanding these complex factors is important in predicting the needs and available services for this population of vulnerable elderly women.
Migration Information Gathering by Mexican-origin Immigrants in the Pre-migration Phase2016-03-20T10:34:12-05:00http://digital.library.unt.edu/ark:/67531/metadc822813/

U.S. immigration procedures are complex and may elude the average individual seeking admission to the United States. Understanding this, the current study investigates how information resources are used by potential migrants to learn about the migratory process. Using a mixed-methods approach, I interviewed 30 Mexican immigrants with unauthorized immigration experience about the process of gathering migration information in the pre-migration phase. Qualitative data were coded using seven themes generated from the primary research questions, including: Information Resources, Resources Used During Migration, Motivation for Migration, Method of Migration, Lack of Information/Misinformation, Types of Help and Types of Information. Findings suggest that the factors motivating migrants to come to the U.S. are combined in complex ways and lack of information about legal alternatives to unauthorized migration is an important factor influencing method of migration. Also, while access to new information resources is increasing, these resources are not being tapped for migration information.
The Role of Family in Alcohol Consumption Among Turkish Adolescents2016-03-20T10:34:12-05:00http://digital.library.unt.edu/ark:/67531/metadc822734/

Alcohol consumption among adolescents is an important issue because of its link to many negative social and health problems, including depression, suicide, and aggression. Drawing from Hirschi's social bonding theory and Agnew's general strain theory, this study examines the effects of family relations on alcohol consumption among Turkish adolescents. Social bonding theory suggests that individuals with stronger social bonds are less likely to use alcohol than individuals with weaker social bonds. General strain theory, on the other hand, proposes that individuals with higher levels of strain due to financial difficulties and/or negative relationships are more likely to consume alcohol compared to individuals with lower levels of strain. In particular, this study proposes to examine how parental attachment, parental monitoring, time spent with family, parents' religiosity, family economic strain, and negative life events in the family predict alcohol consumption among adolescents in Turkey. 2008 Youth in Europe (YIE) project data is used in the study. In general, the results indicate that social bonding and strain factors have significant effects on the adolescents' alcohol consumption patterns. These findings will help to inform prevention programs aimed at reducing adolescent alcohol risk behaviors by explaining the importance of family relationships.
Can Akers' Social Structure and Social Learning Theory Explain Delinquent Behaviors Among Turkish Adolescents?2016-03-04T16:14:01-06:00http://digital.library.unt.edu/ark:/67531/metadc804850/

The aim of this study was to examine to what extent Social Structure and Social Learning Theory (SSSL) explains delinquent behaviors among Turkish adolescents. While Social Structure and Social Learning (SSSL) Theory have been examined quite frequently in the criminology and sociology literature, the present study is unique as it tests the theory in Turkey, a context with a mixed Islamic and Secular cultural structure. The data originates from a survey conducted in Istanbul in 2008 by the Icelandic Centre for Social Research and Analysis (ICSRA) under the auspices of their Youth in Europe project. The sample includes 2,445 Turkish high school students. The dependent variable includes a 13-item delinquency scale, and the independent variables consist of differential association, costs and rewards of differential reinforcement, definitions, imitation, differential location in the social structure, and differential social location of groups. The statistical analyses were conducted using a negative binomial regression approach. Results demonstrated that differential association (peer delinquency) is positively associated with delinquent behaviors among Turkish adolescents. In addition, there is a significant and positive relationship between norms/beliefs that favor delinquency and delinquent behaviors. Moreover, parental reaction, a measure of differential reinforcement, has a negative impact on delinquency. Imitation variables, which include witnessing an argument and witnessing violence in the family, also appear as significant predictors for delinquency. Gender is the only social structure variable significantly related to delinquent behaviors. Furthermore, results indicated that social learning variables mediated the relationship between social structure and delinquent behaviors. Policy implications and suggestions for further studies are also provided at the end of the dissertation.
Health-related Quality of Life and Social Engagement in Assisted Living Facilities2016-03-04T16:14:01-06:00http://digital.library.unt.edu/ark:/67531/metadc804930/

This research project aims to clarify the factors that impact successful aging in Assisted Living facilities (ALFs) in Denton County, Texas. We hypothesize that social disengagement decreases physical and mental components of quality of life. This exploratory research project employed standardized questionnaires to assess residents in the following domains; HRQOL, social engagement status, level of cognition, depression, and the level of functioning. This study collected data from 75 participants living in five ALFs. The average of Physical Component Scale (PCS) and Mental Component Scale (MCS) was 35.33, and 53.62 respectively. None of the participants had five or more social contacts out of facilities, and two-third of them had two or less social contacts. On average, those participants who were more socially engaged had higher score of MCS compared with disengaged counterparts. The level of physical function significantly affects social engagement, when people with more disabilities are more likely to be socially disengaged. Social engagement and depression significantly impact MCS, when depression is a mediating factor between social engagement and mental component of quality of life. Considering the expansion in aging population in the United States within the next three decades, the demand for high quality long-term care will skyrocket consequently. This study reveals that external social engagement can sustain HRQOL of residents in assisted living facilities.
Healthcare Utilization and Health Outcomes: Us-born and Foreign-born Elderly Asian Americans2016-03-04T16:14:01-06:00http://digital.library.unt.edu/ark:/67531/metadc804863/

In order to better understand variations of health behaviors between US-born and foreign-born elderly Asian Americans (65+) in the United States, the research aims to explore relationships among health outcomes, healthcare utilization, and sociodemographic characteristics. Data from the National Health Interview Survey 1998-2012 is used to construct structural equation models for the US born group and for the foreign born group. The results found that there is a reciprocal relationship between health outcomes and healthcare utilization in both groups. Use of healthcare services can positively affect health outcomes, while better health outcomes reduce the need for healthcare utilization. In addition, some sociodemographic characteristics, such as age, sex, and marital status have a direct effect on health outcomes, but some others, such as education, family size and combined family income, have an indirect effect on health outcomes via healthcare utilization. The region of residency has both direct and indirect effects on health outcomes. Regarding the effects of predictors on health outcomes, US-born elderly Asians usually receive more health advantages from using institutional health services than foreign-born elderly Asians. Practitioners, social gerontologists, and policy makers should be cautious about assuming that there is a positive impact of increased healthcare utilization on health outcomes in elderly Asian Americans.
Sociological Applications of Topic Extraction Techniques: Two Case Studies2016-03-04T16:14:01-06:00http://digital.library.unt.edu/ark:/67531/metadc804982/

Limited research has been conducted with regards to the applicability of topic extraction techniques in Sociology. Addressing the modern methodological opportunities, and responding to the skepticism with regards to the absence of theoretical foundations supporting the use of text analytics, I argue that Latent Semantic Analysis (LSA), complemented by other text analysis techniques and multivariate techniques, can constitute a unique hybrid method that can facilitate the sociological interpretations of web-based textual data. To illustrate the applicability of the hybrid technique, I developed two case studies. My first case study is associated with the Sociology of media. It focuses on the topic extraction and sentiment polarization among partisan texts posted on two major news sites. I find evidence of highly polarized opinions on comments posted on the Huffington Post and the Daily Caller. The highest polarizing topic was associated with a commentator's reference on Hoodies in the context of the Trayvon Martin's incident. My findings support contemporary research suggesting that media pundits frequently use tactics of outrage to provoke polarization of public opinion. My second case study contributes to the research domain of the Sociology of knowledge. The hybrid method revealed evidence of topical divides and topical "bridges" in the intellectual landscape of the British and the American sociological journals. My findings confirm the theoretical assertions describing Sociology as a fractured field, and partially support the existence of more globalized topics in the discipline.
Prosperity Belief and Liberal Individualism: A Study of Economic and Social Attitudes in Guatemala2016-02-09T16:37:48-06:00http://digital.library.unt.edu/ark:/67531/metadc801941/

Globalization has facilitated the growth of "market-friendly" religions throughout the world, but especially in developing societies in the global South. A popular belief among these movements is prosperity belief. Prosperity belief has several characteristics which make it compatible with liberal individualism, the dominant value in a globalized society. At the same time, its compatibility with this value may be limited, extending only to economic liberalism, but not to liberal attitudes on social issues. Data from the Pew Forum on Religious and Public Life's 2006 survey Spirit and Power: Survey of Pentecostals in Guatemala is used to conduct a quantitative analysis regarding the economic and social attitudes of prosperity belief adherents in Guatemala in order to examine the potential, as well as the limits, of this belief's compatibility with liberal individualism. Results suggest that support for liberal individualism is bifurcated. On one hand there is some support for the positive influence of prosperity belief on economic liberalism in regards to matters of free trade, but on the other hand, prosperity belief adherents continue to maintain conservative attitudes in regards to social issues. As prosperity belief and liberal individualism continue to grow along global capitalism, these findings have implications for the future of market-friendly religions and for the societies of the global South.
Peer Networks and Health Risk Behaviors Among Adolescents2016-02-09T16:37:48-06:00http://digital.library.unt.edu/ark:/67531/metadc801957/

Adolescence is a time of great exploration and change. During this time, youth are transitioning both biologically and sexually into adults. Adolescents are also testing the boundaries of self-reliance and making choices about their personal relationships. Not surprisingly, aggressive urges are often driven by peers in pursuit of some form of identity (Masten 2004). Peers can have both positive and negative effects on the wellbeing on youth. Peer groups can provide emotional, physical, and social support to youth during a time of immense change (Parker and Asher 1987; Gest, Graham-Berman, and Hartup 2001). Peers can also model delinquent and risk-taking behaviors that have lasting health, social, and economic consequences throughout the life course. In an effort to understand the role of friendships in adolescent health, social scientists have increasingly focused on adolescent network structures within schools and the role various positions and peer group formations influence behaviors such as alcohol and cigarette use, violent and serious delinquency, and sexual risk-taking. While informative, peer networks studies have yet to adequately address how peer network structures based on immigrant generation and types of marginalized social positions influence health risk behavior engagement among adolescents. In three studies, I address the dearth of research in these areas, using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health). The first study investigates the influence of generational peers on alcohol misuse among immigrant youth. Testing hypotheses derived from sociological theories of generations regarding race/ethnicity, gender, and immigrant generation, findings from this study demonstrate generational ties are inversely related to alcohol misuse for immigrants and these effects depend partly on race/ethnicity and gender. The second study investigates the effects of specific network forms of social isolation on heavy episodic drinking and cigarette use among adolescents. The central finding from this study is that different network-based forms of social isolation had varying effects on alcohol and cigarette use when compared to sociable youth. The final study examines the relationship between types of social isolation and violent delinquency when compared to sociable youth. Deriving hypotheses from general strain theory, I test whether the isolation-violence relationship varies across isolation types when compared to sociable youth. I also test whether other negative experiences and circumstances (strains) tied to adolescence moderate the relationship between isolation types and violent delinquency. Finally, studies indicate a consistent gender gap in criminality. Therefore, I test whether the isolation-violence relationship differs by gender. Findings demonstrate that socially disinterested youth show a greater capacity for violent behavior, but other types of marginalized youth showed no difference in violence when compared to sociable youth. Results also suggest that some types of strain moderate the isolation-violence relationship and that these patterns are gendered.
An Application of Marxian and Weberian Theories of Capitalism: the Emergence of Big Businesses in the United States, 1861 to 18902016-02-09T16:37:48-06:00http://digital.library.unt.edu/ark:/67531/metadc801922/

This study was an examination of businesses that became big businesses in the United States during the time period between the years of 1861 and 1890, a period of time frequently referred to as the "big business era." The purpose of the study was to identify actions taken by businesses that enabled them to become and remain big businesses. A secondary purpose of the study was to show that these actions were explained by theories of Karl Marx and Max Weber. The results of the study showed that businesses which took specific actions were able to become and remain big businesses and these actions were explained by the theories of Marx and Weber. The results of the study demonstrate the ability of classical sociological theory to explain macro-level social change.

subset: commoncrawl


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iPhone app to log property keys to estate agents with photo
This project was successfully completed by EMCALLISTER for £550 GBP in 21 days.Get free quotes for a project like this
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Project BudgetN/A
Completed In21 days
Total Bids19
Project Description
I am looking for a freelancer to write an iPhone/ipad compatabile database app to log property keys in and out from estate agents.
The app must use a graphical type user interface rather than a spreadsheet look.
Fields must include:
Company logo
Date taken (auto date & time stamp)
Date returned (auto date & time stamp)
Agent name
Property address
No. Of keys
Key tag number
Photo of keys (to be taken from iPhone and auto inserted), possibly with a double tap to zoom to full screen
Signature (to be signed on screen)
Name of signatory
I've generated a similar app using FileMaker Pro 12 but its too slow and cumbersome.
The app must be quick and easy to use and searchable as we sometimes need to look up keys several months back and look pretty as clients will be looking at it to sign off keys.
Looking to make some money?
- Set your budget and the timeframe
- Outline your proposal
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Trend Qualification and Trading
Techniques To Identify the Best Trends to Trade
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Entering and Exiting Trends for Maximum Profit
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$9.99Humor abounds as life takes form for Linda, growing up with five siblings and a widowed mother, beginning in Connecticut and traveling South to Florida. Linda's ambitions move her along life's path into her nursing career. Humorous stories unfold from eavesdropping on the hooker next door to the short lived revelation that she was proud to be a member of the (SOS) Sisters of the Spinsterhood. As
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Invest by Knowing What Stocks to Buy and What Stocks to Sell"The author introduces an investing methodology with proven results and easily applied unequivocal decision making. Particularly impressive is the way he includes a selling discipline, not just a buying discipline. This book is a must for any concerned investor." Richard Arms, Analyst, Author, and Inventor of The Arms Index "This is one of the...
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TAIL RISK HEDGING: Creating Robust Portfolios for Volatile Markets"TAIL RISKS" originate from the failure of mean reversion and the idealized bell curve of asset returns, which assumes that highly probable outcomes occur near the center of the curve and that unlikely occurrences, good and bad, happen rarely, if at all, at either "tail" of the curve. Ever since the global financial crisis, protecting investments against these severe tail events has become a
How the Markets Really Work (and How They Don't)Bestselling author, Jack Schwager, challenges the assumptions at the core of investment theory and practice and exposes common investor mistakes, missteps, myths, and misreads When it comes to investment models and theories of how markets work, convenience usually trumps reality. The simple fact is that many revered investment theories and market models are flatly wrong—that is, if we insist that
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Dress Your Family in Corduroy and DenimDavid Sedaris plays in the snow with his sisters. He goes on vacation with his family. He gets a job selling drinks. He attends his brother's wedding. He mops his sister's floor. He gives directions to a lost traveler. He eats a hamburger. He has his blood sugar tested. It all sounds so normal, doesn't it? In his newest collection of essays, David Sedaris lifts the corner of ordinary life,
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Getting Started In..... (Book 82)An easy-to-read and updated guide to the dynamic world of options investing During the recent market turmoil, option trading volume actually increased, but many are still unsure of the opportunities that options present, simply because they don't fully understand how this market works. With Getting Started in Options, Eighth Edition, author Michael C. Thomsett looks to change this. In non


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Essays on personal addiction.
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Chocolate Fudge cake with Truffle Filling, Italian Meringue Buttercream, Covered in 'Tiffany"' Blue Fondant. The Bow, Tag and Rings are hand-made from Gumpaste, dusted with Luster Dusts.
Very pretty! Your bow is puurrfect! K'ly
The rings are amazing - as is everything else!
The rings are a really beautiful touch!
Fantastic Cake! Any tips on making the box lid? Many Thanks, Frenchie
CakeCentral.com is the world's largest cake community for cake decorating professionals and enthusiasts.
(if this is a private computer)
Yes please, register now!


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Father's Day isn't too far away and I wanted to do a little replay of a fun project from almost four years ago. Yikes, that's weird to say. Or type. Sometimes I can't believe how long I've been baking and posting treats here. And that I still love every minute of it. This project goes so far back on the site, I thought it would be fun to share again for new readers because it's one of my favorites. And it's one I remember being so giddy over as it all came together in my kitchen.
I think it was the french fry cookies that sent me over the top. I love how they turned out.
Sugar cookies for fries and sugar for salt. So sweet!
But the other part I love about this project is the presentation. I made templates for the paper tray, printed tissue and french fry containers… all for Father's Day. It totally pulls the whole look together. And I promise you'll fool people at first glance.
Here is the original Father's Day Fast Food Fun post [6] with instructions. Make 'em and make dad happy!
And because so many of you loved these the first time around and wanted to make them for the Fourth of July, here are some new templates for the most requested events. Hope they come in handy! And there's a blank french fry template if you want to fill in with your own phrase.
Happy Fourth of July Template [7]
Happy Memorial Day Template [9] … for next year!
Happy Father's Day Template [11]
And just for fun… a glimpse at some of the Happy Meal photos you guys emailed me after making them.
I'm lovin' it.
Thanks for emailing your pics Katja, Rena, Kiki, Bethany, Dena, Heidi, Lisa, Nancy, Carolina, Aly, Lisa, Jennifer, Leah, Tina, Robin, Stephanie, Heidi, Missy, Betty-Ann, Heather, Jenn, Jennifer, Amy, Laura, Anna, Vicki, Alison, Jenn, Hfidanzo, Tina, Sara, Jen [13], Freakgirl [14], Lena [15], Liz [16], Paige [17], The Fegers [18], Leah [19], Camille [20].
Your meals made me happy!
Oh and if you decide to make these for the Fourth, you could go for the "full meal deal" … cupcakes disguised as snow cones would so be an over-the-top way to cap off your cookout.
Snow Cone Cupcakes How-To. [22]


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Neoseeker : Games : Wii : Role-Playing : Fantasy : Animal Crossing: City Folk : Cheats : Can't buy red turnip seeds?
cheats | faqs | screens | forums | friendcodes
Animal Crossing: City Folk
- Released on Nov 16, 2008
- By Nintendo for Wii
Animal Crossing: City Folk Tips
Can't buy red turnip seeds? Does Joan say she "doesn't have any"? This is not random, but a glitch. Go to your Wii menu and change the time TO THE EXACT REAL TIME. Then go on Animal Crossing and CHANGE IT'S TIME TO 1 MINUTE OF WHAT THE WII TIME IS. In one week's time, your game will be ready to sell red turnip seeds to you.
PS: If you Time travel, change your wii system time to avoid this glitch.
PS: If you Time travel, change your wii system time to avoid this glitch.
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text:
We're hours away from seeing the new Garen, and Riot Games has released a League of Legends spotlight video showing off Demacia's new and improved Colossal Shoulders McGee.
We first heard word of his visual rework at the start of the month, when Riot dropped a couple of teaser images showing his new classic splash art and how his skins will look. Now, you have a proper spotlight video showing off Garen's updated appearance. He looks pretty damn good, too.
Heck, even Garen's classic skin looks awesome now. I'm going to miss the old Dreadknight skin, though.
More than ever, Garen deserves his spot on our list of Manliest Manly Men in League of Legends.
Here's hoping the upcoming patch will also hint at what sort of Halloween skins we'll see this year. All that RP isn't going to spend itself.


text:
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- The initiatory system of the Enochian Temples divides the path of initiation into four major stages, distinguished primarily by the types and sources of the influences they are sensitive to. Briefly, these stages are: 1. Lunar-planetary, focused in Yesod and...
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Lily's Florist is an Australian based delivery florist. Order flowers online for same day delivery to any location is Australia. Fresh flowers from just $29.
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Jury Delivers Verdict in Eleven Minutes
It took an Ohio County jury 11 minutes on Thursday to find Heyward Bazar guilty of a misdemeanor battery charge stemming from a Nov. 22 altercation he had with his girlfriend after they watched Wheeling's Christmas parade.
Bazar faces up to one year in jail for the conviction rather a possible two-to-10 years that could have accompanied a conviction on the original felony indictment of malicious assault or one-to-five years for a lesser felony assault charge.
With the absence of the victim in the court, Assistant Ohio County Prosecutor Gail Kahle drew testimony from two of Bazar's neighbors at 716 Main St.; an Ohio Valley Medical Center Emergency Room nurse who treated the victim; and from police officers who responded to the scene.
He said it is not uncommon for victims in domestic assault cases not to attend the trial.
Kahle presented evidence that Bazar meant to hurt his then-girlfriend by punching her and causing facial and head wounds.
The neighbors said they opened the door to the room shared by Bazar after hearing a commotion and saw Bazar holding her by the hair in a manner suggesting he intended to hurt her.
Defense attorney Richard Hollandsworth argued the two were engaged in an argument that escalated to a physical confrontation leading to the victim striking her head on the door frame of their neighbor's house.
Police who responded to the call said they found blood outside of the couple's room and found the victim to be bleeding from the head.
She told the officers that Bazar had beaten her.
Ohio County Circuit Judge James Mazzone set a sentencing date for 1:30 p.m. July 17.


text:
Technical News from The Portland Group
September 2010
Porting the SPEC Benchmark BWAVES to GPUs with CUDA Fortran
The SPEC® CPU2006 benchmarks are designed to compare compute-intensive workloads on different computer systems. They are organized into two groups: a 12 benchmark integer suite, CINT2006 and a 17 benchmark floating point suite, CFP2006. BWAVES a member of the CFP2006 suite is one of the benchmarks used to assess CPUs for compute-intensive workloads.
The CUDA architecture facilitates the porting of compute-intensive applications to the GPU. CUDA C was first introduced by NVIDIA in 2007, and CUDA Fortran was introduced in 2009 in a joint effort between The Portland Group (PGI) and NVIDIA. Both CUDA C and CUDA Fortran are essentially standard-compliant C and Fortran with a handful of extensions that allow portions of the computation to be offloaded to the GPU.
CUDA is a hybrid programming model. Both the host (or CPU) and the device (or GPU) can execute parts of the code stream independently and in some case simultaneously. CUDA specific code modifications are usually relatively small and typically focus on two areas: host/device data transfers and kernels or the code that will run on the device. We need to note here that SPEC run rules forbid testers from modifying the SPEC source code. In this light, the performance data presented here on a CUDA Fortran implementation of BWAVES does not qualify as a rules-compliant run, but can nevertheless give useful comparison data.
We chose to port BWAVES using CUDA because it is not a good fit for the GPU. Neither the grid size (65x65x64) nor the layout of the interleaved dependent variable arrays are structured for optimizing bandwidth utilization. While not a good fit for the GPU, BWAVES is an excellent fit for multi-core CPU systems as evidenced by the performance data available on the SPEC website. It is because BWAVES has been so well optimized to run on CPUs that we chose it for this comparison.
Following are three sections covering 1) detailed breakdown of the BWAVES algorithm including a CPU performance profile. 2) discussion of the CUDA Fortran port considerations, including strategies for porting certain kernels to the device, and 3) results validation and performance comparisons of CPU and CUDA runs.
The BWAVES Benchmark
BWAVES simulates the evolution of a blast wave using a discretization of the compressible Navier-Stokes equations on a uniform, structured, three-dimensional mesh. This mesh is periodic in each direction and contains 65 points in the x and y directions and 64 points in the z direction. There are five dependent variables at each point in the mesh corresponding to density, energy and velocity in the x, y and z directions. The code runs for 100 time steps. The time-step loop is composed of four sections:
- determination of the time step via CFL condition,
- calculation of the left-hand side (LHS) terms including Jacobian evaluations,
- calculation of the right-hand side (RHS) terms, and
- the BiCGSTAB routine
The first component, determining the time step, is a simple maximum reduction of the velocity field and is executed in very little time relative to the other components. It is not included in this analysis. The LHS calculation assembles the components of the A matrix, the RHS calculation similarly calculates the components of the b vector, and the BiCGSTAB routine is an iterative method of solving the system Ax = b for x using the bi-conjugate gradient method. The BiCGSTAB algorithm as implemented in BWAVES is listed in Figure 1. The iterative loop in the BiCGSTAB routine consists of two matrix-vector multiplications, five reductions, several daxpy operations and parameter updates.
|r = Ax
|Matrix-vector multiplication
|r = b − r
|rˆ = r, p = v = t = 0, α = ω0 = ρ0 = 1
|while (∑ r · r − ε > 0):
|ρ = ∑ r · rˆ
|β = ρ/ρ0 ∗ α/ω0
|p = r + β(p − ω0ν)
|ν = Ap
|Matrix-vector multiplication
|α = ρ / ∑ rˆ · ν
|r = r − αv
|t = Ar
|Matrix-vector multiplication
|ω = ∑ t · r / ∑ t · t
|x = x + αp + ωr
|r = r − ωt
|ρ0 = ρ, ω0 = ω
Figure 1: The BiCGSTAB algorithm in BWAVES, where A is a matrix from the LHS calculation, lowercase roman letters are vectors, and greek letters are parameters. b is the vector from the RHS calculation, and x is the solution vector. The iterative loop starting in line (4) consists of two matrix-vector multiplications, five reductions, and several daxpy operations and parameter updates.
CPU Performance Profile
To understand how to implement this algorithm in CUDA Fortran and to determine where to concentrate most of the porting effort we began by profiling the original code.
Table 1 below contains performance data for BWAVES on two systems: a single quad-core, 2.83 GHz Intel Xeon E5440 (Harpertown) system and a dual quad-core, 2.67 GHz Intel Xeon X5550 (Nehalem) system. Both systems were running Red Hat Linux. The benchmark was compiled on these systems using the PGI 10.5 Fortran compiler with flags -fastsse -Mfprelaxed for single-core runs and -fastsse -Mfprelaxed ‑Mconcur=levels:5,allcores for the four- and eight-core runs. The table shows overall wall-clock times for the 100 time-step run, the time spent calculating the left-hand side (LHS) and right-hand side (RHS) terms and the time spent in the BiCGSTAB routine. In all cases, the time spent in the BiCGSTAB routine dominated at around 90% of overall CPU time.
|Number of
CPU Cores
|CPU Overall
Time (s)
Time (s)
Time (s)
Time (s)
(E5440, 2.83 GHz)
(X5550, 2.67 GHz)
Table 1: Overall and component wall-clock times for BWAVES on two CPU platforms. The components are the left-hand side (LHS) calculation including the Jacobian, the right-hand side (RHS) calculation, and the BiCGSTAB routine which includes the matrix-vector multiplication.
Further examination of the performance using PGPROF shows that the matrix-vector multiplication routine mat.times.vec() that is called from the BiCGSTAB routine in steps 1, 8, and 11 of Figure 1 takes approximately 75% of total wall-clock time. The CPU matrix-vector multiply code is listed in Figure 2.
do k=1,nz km1=mod(k+nz-2,nz)+1 kp1=mod(k,nz)+1 do j=1,ny jm1=mod(j+ny-2,ny)+1 jp1=mod(j,ny)+1 do i=1,nx im1=mod(i+nx-2,nx)+1 ip1=mod(i,nx)+1 do l=1,nb y(l,i,j,k)=0.0d0 do m=1,nb y(l,i,j,k)=y(l,i,j,k)+ 1 a(l,m,i,j,k)*x(m,i,j,k)+ 2 axp(l,m,i,j,k)*x(m,ip1,j,k)+ 3 ayp(l,m,i,j,k)*x(m,i,jp1,k)+ 4 azp(l,m,i,j,k)*x(m,i,j,kp1)+ 5 axm(l,m,i,j,k)*x(m,im1,j,k)+ 6 aym(l,m,i,j,k)*x(m,i,jm1,k)+ 7 azm(l,m,i,j,k)*x(m,i,j,km1) enddo enddo enddo enddo enddo
Figure 2: Matrix-vector code used in the CPU BiCGSTAB algorithm of BWAVES. At each point in physical space, the sum of the product of seven 5x5 matrices with five element vectors is calculated. The seven matrices represent terms in the seven-point stencil for each point which includes the centered value and its nearest neighbor in each direction. As a result each component of the a, axp, ... matrices are accessed just once in this loop. The elements of the x vector are accessed multiple times via shifting the indices to get nearest neighbor information.
Several aspects of this loop are worth noting. First, when calculating the result at any point in the mesh, information from the point's nearest neighbors is used by both the matrix and vector terms. However, the neighboring information is represented differently for the two terms. In the case of the matrix quantities, seven different matrices are used; one centered and six neighbors. For the vector, only a single array is used with shifted indices. The result is that each component of each matrix in the entire nested loop is accessed only once, whereas each element of the vector is used 35 times (five times for each component of the seven-point stencil). This access pattern will an important consideration in the CUDA implementation.
CUDA Fortran Implementation
This section starts with a review of the CUDA architecture and programming model, and highlights some of the performance issues to be aware of when porting sections of the CPU code to CUDA. Next is a discussion on determining which sections of the code to run on the device. Included here is a discussion on using asynchronous data transfers to efficiently communicate between the host and device. Finally, the section wraps up with a discussion of porting the BiCGSTAB algorithm and in particular the matrix-vector multiplication routine to CUDA.
CUDA Architecture and Programming Model
The CUDA architecture supports hybrid computing. This means both the host (CPU) and device (GPU) can be used to compute portions of the code simultaneously. A key advantage of this hybrid model is the ability to modify code incrementally. One performance caveat of this model is that data transfers between host and device through the PCI bus tend to be the primary performance bottleneck.
Current CUDA-enabled GPUs contain from tens to hundreds of processor cores and are capable of running tens of thousands of threads concurrently. The processor cores on a GPU are grouped into multiprocessors. The programming model mimics this grouping. A subroutine, or kernel, running on the device is launched with a grid of threads grouped into thread blocks. The threads blocks of the programming model map to multiprocessors in hardware. The number of thread blocks that can reside on a single multiprocessor at any instant in time is limited by the number of shared multiprocessor registers and the amount of shared memory. As a result far more threads can be resident on a multiprocessor than there are thread processors. Because context switches on the GPU are very inexpensive, such oversubscription of processors by threads is the primary means for hiding memory latencies. Threads waiting on data loads and stores can be efficiently swapped out for other threads that are ready to perform computations.
Even with the prospect of hiding latencies described above, one of the primary performance challenges with CUDA, is efficient data movement. With BWAVES the challenge is even more acute because there is relatively little computation performed per element.
Efficient Host to Device Data Transfers
There are two aspects to efficient data movement: data transfer between the host memory and device memory, and onboard data transfer between the device memory and actual GPU chip. We begin with a discussion of how to efficiently perform transfers between the host and device.
The bandwidth of the PCI bus is over an order of magnitude slower than the bandwidth between the device memory and GPU chip. Therefore special emphasis needs to be given to limiting and hiding PCI traffic. The first and most obvious approach is to limit PCI traffic by running as much code on the device as possible. In our case, porting all four components of the time-step loop to CUDA would eliminate all PCI traffic except the initialization and final data retrieval. However, not all code needs to be ported to CUDA to avoid the cost of PCI transfers.
Asynchronous transfers can be used to overlap computation on the device (and host) with PCI memory transfers. To see how we can use the asynchronous mechanism, we'll first need to look at the dependencies between sections of the code. The calculation of the LHS and RHS components are independent of one another. As such they are candidates for one component being calculated on the host while the other is calculated on the device. To decide which component to run where, we need to consider the potential speedup and also the amount of PCI traffic required for each component. Both of these factors suggest that the LHS component is the better candidate for implementing on the device. First, the LHS calculation takes approximately five times as long to run as the RHS. Second, the result of the LHS contains 35 times as much data as the result of the RHS calculation. (Seven 5x5-word matrices per mesh element versus one 5-word vector per mesh element.) Calculating the LHS component on the device presents the opportunity to both greatly improve the LHS performance and hide the host to device PCI transfer of the single vector array generated by the RHS calculation.
Porting the BiCGSTAB Routine
Having looked at how to distributed the code between the host and device, we now turn our attention to the implementation of the kernels to run on the device.
The algorithm for BiCGSTAB depicted in Figure 1 contains several different operations including matrix-vector multiplications in lines 1, 8, and 11, reductions in lines 4, 5, 9, and 12, daxpy-like vector operations in lines 2, 7, 10, and 13-14, as well as scalar parameter updates in lines 6 and 15. We begin with the most time-consuming operation of BiCGSTAB, the matrix-vector multiplication.
Matrix-Vector Multiplication
From our earlier performance analysis, we discovered that the matrix-vector multiplication listed in Figure 2 takes up by far the majority of the CPU time. Therefore, we'll pay special attention to its CUDA implementation.
As described previously, at each point in the physical mesh the routine calculates the sum of a multiplication of seven 5x5 matrices with a five element vector. Each element of the seven a* matrices is used only once and each element of the vector x is used a total of 35 times. The use of on-chip shared memory to store elements of the vector x can reduce the amount of traffic to device memory, but shared memory cannot help with reducing the number of accesses to the seven a* matrices. Thus we need to ensure we are efficiently retrieving the elements of the seven matrices from device memory. Before discussing how this is done we need to talk about the notion of data coalescing.
In terms of data movement on the device, the primary performance issue deals with how memory accesses can be coalesced into a single transaction if certain access requirements are met. The details of how coalescing works can be found in the CUDA Programming and Best Practices Guides, but in a nutshell the optimum memory throughput occurs when contiguous threads access contiguous words in memory. More precisely, pending certain alignment properties, 16 contiguous threads (on the C1060) or 32 contiguous threads (on the C2050) can access an equivalent number of contiguous elements from device memory in as few as a single transaction.
A typical strategy for coalescing mesh-based problems is to have each thread block work on one or more two-dimensional tiles in physical space. To ensure coalesced loads, the leading dimension of the tile is usually a multiple of 16 or 32. Data that are reused, such as components of the x vector, are placed in shared memory so they can be accessed by different threads within the block. This makes stencil calculations easy and efficient because access to noncontiguous data in shared memory does not suffer the performance penalty that noncontiguous accesses to device memory suffer.
While this type of tiling is a plausible approach in general, it is not an appropriate strategy for this matrix-vector multiplication for several reasons. First, is the size of the mesh. At 65x65x64 it is not conducive to tiles with dimensions that are multiples of 16 or 32. The last tile in each horizontal direction would greatly underutilize the threads in the block. Second, there is a limit to the amount of shared memory on a multiprocessor. Multiple two-dimensional shared-memory tiles with 16 or 32 points in the leading direction, where each point contains five 64-bit elements of the vector, would greatly limit the occupancy, or the number of threads that are resident on a multiprocessor at one time. (Recall maximizing occupancy helps to hide memory latency.)
Because the data at a grid point are interleaved, it is not necessary to have 16 or 32 points in the leading dimension of the tile to achieve coalescing. As an example, we can launch a kernel with thread blocks containing 25 threads which map to one point in the physical mesh. The 25 contiguous elements of one of the seven matrices at that point can be accessed in as few as a single transaction. However, there are several problems with this approach. The first is that a group of 32 threads, referred to as a warp, is executed in SIMD fashion on the multiprocessor. This implies that a block of 25 threads is essentially a block of 32 threads were seven of the threads do not perform any work. The second problem is that there are a maximum of eight thread blocks per multiprocessor at any one time, resulting in a maximum of 200 threads per multiprocessor. This once again greatly limits occupancy. The third issue is that in such an arrangement each element of the x vector can only be used within that block five times out of a possible 35 times that they are used overall. So each element of the x vector would still have to be reloaded from device memory seven times.
One way to improve on the above approach is to map a block of 125 threads to five consecutive mesh points. This improves thread block effectiveness from 25 of 32 to 125 of 128. Moreover the five-point tile fits evenly into the 65-point mesh points in the first direction. This eliminates any prospect of dormant threads in the last blocks of the first dimension. This approach also addresses the other two issues identified above. The maximum number of threads per multiprocessor is now 1000 based on the eight block limit. For a block of threads calculating the five components of y at points (i0:i0+4,j,k), the elements of x stored in shared memory are the five components at points (i0:i0+4,j-1:j+1,k-1:k+1) and additionally (i0-1,j,k) and (i0+5,j,k). The components of x at (i0:i0+4,j,k) in shared memory are accessed either 10 or 15 rather than five times within the block, reducing the number of times it is loaded from device memory from seven to either five or six times.
Taking the analysis one step further, we can improve the reuse of the x vector elements by looping in the third direction within the kernel so that each block of threads processes a ribbon of 5 by 64 elements. This is demonstrated in Figure 3. This kernel would launch with (65/5)*65 blocks instead of the (65/5)*65*64 blocks as in the previous case. Shared memory is used in the same manner as shown earlier, however the components at (i0:i0+4,j,k) and (i0:i0+4,j,k+1) are reused when k is incremented rather than reloaded from device memory. This results in the interior points in the ribbon being loaded from device memory just three times overall, once for its vertical ribbon and for the j±1 ribbons.
Figure 3: The tile used in the CUDA Fortran implementation of the matrix-vector multiplication kernel for two consecutive iterations of the loop over the z direction. Each block of threads evaluates components of the output vector at the five interior elements of the tile for each iteration in the z direction. The 10 orange (lighter) elements, five of which are interior in both the k and k+1 steps, represent components of the input vector that get reused from one iteration of the k loop to the next. Upon completion of the loop over the z direction, each block of threads will have calculated results for an xz ribbon of five by 64 elements.
Up to this point we have discussed coalescing and efficient shared memory use for the matrix multiplication input values. Now we'll look briefly at the output vector y. Let's start by comparing the CUDA Fortran code shown on Figure 4 with the equivalent section of CPU code in Figure 2. The 125 threads of a block access contiguous elements of the matrix arrays with indices l=1:5, m=1:5, and i=i0:i0+4. The variable il=i-i0+1 is a local index for i used to access the shared memory arrays. The results of the multiplications by the 125 threads are stored in the 125-word shared memory array sum_s which acts as a partial sum. Following this, syncthreads() is called to ensure all threads in the block have completed the partial sum. Then 25 of the threads in a block perform a final reduction into y. Similarly, the variables p and pl serve an analogous role in mapping the first 25 threads to elements of the vectors as i and il do in mapping all 125 threads of a block to elements of the matrices.
sum_s(l,m,il) = & a(l,m,i,j,k) * x_000_s(m,il )+ & axp(l,m,i,j,k) * x_000_s(m,il+1)+ & ayp(l,m,i,j,k) * x_0p0_s(m,il )+ & azp(l,m,i,j,k) * x_00p_s(m,il )+ & axm(l,m,i,j,k) * x_000_s(m,il-1)+ & aym(l,m,i,j,k) * x_0m0_s(m,il )+ & azm(l,m,i,j,k) * x_00m_s(m,il ) call syncthreads() if (t <= 25) then y(l,p,j,k) = sum_s(l,1,pl)+ sum_s(l,2,pl)+ & sum_s(l,3,pl)+ sum_s(l,4,pl)+ & sum_s(l,5,pl) endif
Figure 4: Matrix-vector multiplication in CUDA Fortran. This section of code is inside a loop over the k index, where each block of threads calculates results in a ribbon of mesh points, five points in i and all 64 points in k. For each iteration in k, the x_*_s shared-memory arrays are updated (not shown). Results are first stored in a partial sum shared-memory array sum_s. After all threads in the block have completed the partial summation, 25 of the 125 threads perform the final summation and store the result in y. The indices il=1:5 and pl=1:5 are unit-offset variants of i and p.
In addition to matrix-vector multiplication, the reduction operation is prevalent in the BiCGSTAB algorithm. The reductions appearing in lines 4, 5, 9, and 12 of Figure 1 are implemented in two steps. The first step, done on the device, is a reduction within each thread block. The second step is to return the resulting partial sums to the host for the final reduction. The reason for doing the final reduction on the host is twofold. First, performing the final reduction on the device would require either use of atomic functions to accumulate between blocks, or launching a separate kernel with just one block. Neither operation is particularly efficient. Second, the result needs to be brought back to the host in any event because it is used by the next kernel. For example, the final reduction of step 5 is done on the host, where its result, ρ, is used to calculate β on the host in step 6. β is then placed into constant memory before launching the next kernel which performs step 7. Similarly α in step 9 and ω in step 12 are put into constant memory before launching the kernels which calculate steps 10 and 13-14. The use of partial sums or multiple accumulators for reductions is both more efficient and more accurate than using a single accumulator.
Performance data for the CUDA Fortran implementation of BWAVES is shown in Table 2 below. As with the CPU runs, the overall times in seconds are provided along with a breakdown of time spent in the LHS, RHS, and BiCGSTAB components of the runs (in parentheses). For each platform, two sets of numbers are presented:
- concurrent runs where the LHS calculation on the device overlaps the data transfers and computation of the RHS on the host, and
- serial runs where no overlap occurs.
The serialization was achieved through enabling CUDA profiling which effectively changes the nonblocking data transfers into blocking transfers. Only the time taken to launch the LHS kernel is recorded for the LHS time in the concurrent runs, and is therefore not reported. The actual time required for computing the LHS on the GPU is given by the first number in parentheses for the serial runs. In some cases it appears that the BiCGSTAB routine takes longer for the concurrent run than it does for the serial run on the same platform. However, this is not the case. In these cases the RHS calculation on the host and the subsequent host to device data transfer complete before the LHS. The additional time reported simply reflects the time waiting for the LHS calculation to finish.
|C2050 (ECC on)
|C2050 (ECC off)
|Harpertown (E5440, 2.83 GHz)
|Nehalem (X5550, 2.67 GHz)
Table 2: Results for CUDA Fortran runs on Harpertown and Nehalem systems with C1060 (Tesla) and C2050 (Fermi) GPUs. The numbers are times in seconds for the runs, and the breakdown of time spent in the left-hand side (on device), right-hand side (on host), and bi-conjugate gradient (on device) portions of the code are given in parentheses, respectively. For each platform both concurrent and serial runs are given. In the concurrent runs the computation of the LHS on the GPU overlap those of the RHS on the CPU. In these cases no time is reported for the LHS as only the launch time is recoded. The actual time taken be the LHS computation is given in the breakdown of the serial runs.
Verification of Results
BWAVES generates three different output files that can be used for result verification:
- the L2 norm of dq(l,i,j,k), the change in the dependent variables after the last time step,
- the residual convergence after each time step, and
- the cumulative sum of iterations for convergence over all times steps.
The major difference between platforms appears in the last of these. From this data (shown in Table 3) we see that parallel runs, whether on multiple cores of the CPU or on the GPU, require fewer iterations overall. This is due to the improved accuracy of parallel reductions over their serial counterparts. These reductions are used to find search directions for the BiCGSTAB algorithm; the more accurate these search directions the fewer iterations needed.
|CPU Only
|C1060 (Tesla)
|C2050 (Fermi)
|ECC On
|ECC Off
(E5440, 2.83 GHz)
(X5550, 2.67 GHz)
Table 3: The number of total iterations of the BiCGSTAB loop for the 100 time steps, as output in bwaves2.out. The parallel reductions are more accurate than serial reductions, resulting in obtaining better search directions and fewer iterations overall. This is seen both in comparing serial and parallel CPU-only runs as well as GPU and CPU runs. Both concurrent and serial GPU runs produce the same result, thus only a single value for each is reported here.
The L2 norm of the change vector after the last time step shows excellent agreement across platforms, as indicated in Table 4.
|CPU Cores
|CPU Only
|C1060 (Tesla)
|C2050 (Fermi)
(E5440, 2.83 GHz)
(X5550, 2.67 GHz)
Table 4: The L2 norm of the dependent variables change vector after the last time step as reported in bwaves3.out. Both concurrent and serial GPU runs produce identical results, as do C2050 (Fermi) runs with ECC both on and off, so these columns are collapsed.
Table 5 summarizes our results for BWAVES CPU and GPU performance. Because both host and device execute sections of the code, overall performance is dependent on both CPU and GPU architectures. However, to the first order, the performance can be categorized by the GPU architecture. Runs on the C1060 were between 50 and 52 seconds. Here, the LHS calculation on the GPU is the bottleneck in the LHS/RHS overlap as can be seen in the serialized runs in Table 2. For the C2050 this is no longer the case and the different CPU platforms play a larger role in the overall performance. Of significance to note is that regardless of the GPU architecture, all CUDA runs outperform any CPU-only run, and a single Harpertown core with a C1060 outperforms an eight-core Nehalem system.
Finally, no changes were made to the code between any of the runs. This is not particularly notable when comparing CPU and GPU runs as the execution naturally takes different code paths. However, it is noteworthy in the context of different GPU architectures. Specifically, the execution configurations used when launching CUDA kernels were identical for the C1060 Tesla and C2050 Fermi runs. The same optimization principles apply to both GPU architectures, such as limiting PCI traffic and hiding it with computation whenever possible, efficient use of device bandwidth through coalescing global memory transactions and reducing multiple reads of the same data through shared memory.
|CPU Cores
|CPU Only Time (s)
|C1060 (Telsa) Time (s)
|C2050 (Fermi) Time (s)
|ECC on
|ECC off
(E5440, 2.83 GHz)
(X5550, 2.67 GHz)
Table 5: Comparison of CPU and GPU results for BWAVES on both Harptertown and Nehalem systems with and without Tesla and Fermi cards. Two Fermi runs are performed, with and without ECC. In each cell of the table the top number is the overall time, and the bottom numbers in parentheses reflects the cumulative execution time for the LHS, RHS, and BiCGSTAB components of the benchmark. For concurrent runs the LHS time recorded reflects only the time to launch the kernel due to the nonblocking of CUDA kernel launches, and is not reported.
Additional information on this port is available in the PGI Accelerator Files section of the PGI website.
SPEC® is a registered trademark of the Standard Performance Evaluation Corporation


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text:
Tragedy in Oak Creek
Joint Statement from Members of the Common Council
We are deeply shocked, saddened and dismayed by the senseless tragedy that occurred yesterday in Oak Creek. Our prayers and sincerest wishes for healing go out to all of the victims, their families, the Sikh Temple of Wisconsin, and the entire Sikh community.
We also pray for Oak Creek Police Lt. Brian Murphy, and we know full well it was because of his and Oak Creek's swift police response that a wider tragedy was averted, and lives were most definitely saved.
The tragedy occurred at the gurdwara – or house of worship – that the temple members worked so diligently to build for themselves and their families. Many of us attend churches and other peaceful places of worship, and we simply cannot imagine such an event taking place where we pray and seek spiritual healing. It is our hope that this event can help encourage greater understanding of the Sikh faith, and that it can remind us that there are many more things that unite us than separate or divide us.
Political Contributions Tracker
Displaying political contributions between people mentioned in this story. Learn more.
- March 28, 2016 - Michael Murphy received $100 from Terry Witkowski
- November 19, 2015 - Robert Bauman received $50 from Terry Witkowski
- September 21, 2015 - Jose G. Perez received $50 from Terry Witkowski
Press Releases by Milwaukee Common Council
Outrage at ambush of MPD officer & other police shootingsJul 19th, 2016 by Milwaukee Common Council
Joint Statement from All Members of the Common Council
Taking "Home" out of Home RuleJun 24th, 2016 by Milwaukee Common Council
Joint Statement from All Members of the Common Council
Council will recognize state championship sports teamsDec 14th, 2015 by Milwaukee Common Council
These two state championship teams will be recognized for their teamwork and victories during the 2015 Wisconsin Interscholastic Athletic Association state volleyball and soccer tournaments.
Arena district proposal to be heard Tuesday by ZND CommitteeSep 14th, 2015 by Milwaukee Common Council
Testimony to be heard only from principals, city officials
DSHA basketball player Arike Ogunbowale to be honored at Tuesday's Common Council MeetingFeb 9th, 2015 by Milwaukee Common Council
Divine Savior Holy Angels High School basketball player Arike Ogunbowale will be honored at the Common Council meeting tomorrow
Members of the Common Council Respond to Ald. Dudzik's RemarksJan 23rd, 2015 by Milwaukee Common Council
Joint Statement from members of the Common Council


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HourlyMore than 30 hrs/week
Start DateDecember 7, 2012
Need someone to post a list of over 1000 phones into wordpress with some basic content and an image. will need to know how to size phone images and find them off the search engine.
You will need to make content about 100 words or less per post about the phone.
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The John Howard Society of North Island
Registered Name: THE JOHN HOWARD SOCIETY OF NORTH ISLAND
Business Number: 107542524RR0001
About This Charity
Our Mission
We provide programs and support to help children, youth, and families lead safer, healthier lives.
About Our Charity
One of our initiatives that currently needs support is our KidStart Program.
John Howard KidStart is a preventative volunteer mentoring program focusing on the positive development of children and youth ages 6-18. Mentoring services are delivered by carefully screened and trained volunteers. KidStart relies on donations, grants, monthly sponsors (KidStart Champions), and Gaming funds - Province of BC.
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text:
Hi ,
i'm a first year student in biology from Brussels and, as most students do, have passed a math exam today. I'm not quite sure whether my solution to one of the questions is correct. I was wondering if somebody could check/confirm my answer? thx a lot ^^
The 2nd degree linear non homogenic DE is as follows :
y'' - 2y' - 3y = e^(-x)
Using the method of variation of parameters to find the particular solution Yp I found this general solution
Y = Yc + Yp = c1 e^(3x) + c2 e^(-x) – e^(-x)* ((1 + 4x) / 16)
Is this correct? I'm also wondering if I should have used the method of undetermined coefficients instead?
What I ment is you cant take derivatives of your solution and plug them into the ode and check your solution. This is exactly like solving
and plugging into it to check your solution.
IF you look at the attached immage above this code
and it did give the correct solution.


text:
Following official visits to Kansas, N.C. State and Virginia, Devonte Graham, the No. 36 ranked player in the 2014 class, verbally committed to the University of Kansas.
PG Devonte Graham Headed To KU
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The senior season of the Sooners 2008 committed class is well underway and many of the members have gotten off to strong starts for their individual teams.
Calhoun, Johnson continue to roll
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Please help, complete novice! Where do i get dump from?
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I believe some people had some favourite bits that werent in the movie.
Am i right?
Mine was "i prefer brunettes" or "why dont you want to be my wife"
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Team: Golden State Warriors
College: Davidson College
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