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Police on Tuesday morning were searching for two gunmen who tied up an a man inside of his home before stealing thousands of dollars, a rare coin collection and the man's Mercedes SUV. The home invasion robbery was on quiet beachfront street in Playa Del Rey. Toni Guinyard reports from Playa Del Rey for NBC4's Today in LA on Tuesday, Aug. 27, 2013.
Published at 1:48 PM GMT on Aug 27, 2013 | Updated at 1:56 PM GMT on Aug 27, 2013


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The latest news and views about music in Milwaukee and beyond
Five comedy-music tour pairings we'd like to see
Eugene Mirman and Andrew Bird wouldn't be anyone's first guess for a double bill, or for that matter Jeff Garlin and Guster. But these comedy-music pairings are so different the shows have the potential to be all the more special. "For us, a band pushing 20 years, its just a way to kind of present a show in a different way than, 'Hey, here's Guster again,'" Guster's Ryan Miller told me.
Mirman and Bird play Riverside Saturday; two weeks later, Garlin and Guster will co-headline the Pabst. It's an interesting coincidence that two comedy-music pairings will be in town within two weeks of each other, and a concept it'd be fun to see more of. And while from a marketing perspective it may seem difficult to figure out what comedians and bands would go together, the following five dream pairings would seem like pretty sure bets.
1. John Hodgman and The New Pornographers
It'd be a perfect blend of sophisticated comedy with intelligent indie rock ... plus Hodgman's made a funny cameo in the Canadian supergroup's amusing music video for "Moves." And if Hodgman was too busy writing a book or shooting an Apple commercial, Pornographers could always call up "Daily Show" correspondents John Oliver or Wyatt Cenac, who also showed up in the video. »Read Full Blog Post

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Jaguar Project 7 is designed by Ian Callum, Director of Design at Jaguar and his team. A one seater sports car with open top, Project 7 features 20" Blade forged alloy wheels and carbon fiber inserts. It is powered by Jaguar's 5.0l V8 generating 542 hp and 680 Nm torque. It can accelerate from 0-100 kmph in just 4.1 seconds and has a top speed of 299 kmph.
Speaking about the Project 7, Ian Callum says, "Project 7 is a single-seater sports car based on the F-TYPE, with the kind of racing-inspired form that designers dream about. It has one purpose: to be driven fast and enjoyed. Jaguar sports cars are known for exceptional performance and clean design. Project 7 captures that spirit in its purest form." To be on display at the Jaguar stand during 2014 Delhi Auto Expo, this car will share space with the F-Type Coupe and C-X17 concept crossover.


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#FlashbackFriday: NLRB Launches Attack on the American Dream
Jul 29, 2016 -
July 29, 2014: That's the day the National Labor Relations Board (NLRB) escalated its attack on America's small businesses and the workers they employ. That's right, two years ago today, NLRB General Counsel Richard Griffin determined McDonald's Inc. and certain franchisees were "joint employers." ...
Adding Fuel to the Fire
Jul 21, 2016 -
College affordability is increasingly on the minds of more and more Americans—particularly with students preparing to head off to college and a national election on the horizon. Unfortunately, as college costs continue to rise, one "solution" being discussed would only add fuel to the fire. Accordin...
"Obamacare Is Unraveling"
Jul 19, 2016 -
We're now six years into the government takeover of health care, and the president's unworkable health care law continues to unravel and create more hardship for working families. Stories across the country serve as a painful daily reminder of the broken promises surrounding this failed law. Promise...
NLRB: Destroying Opportunity Since 2009
Jul 14, 2016 -
The Obama National Labor Relations Board (NLRB) is always finding new ways to make it harder for workers to succeed. In recent years, there have been "ambush" elections and job-destroying micro-unions, diktats to private businesses on where to create jobs, and restrictions on the right to secret bal...
What They're Saying: Real-World Consequences of DOL's Overtime Rule
Jul 6, 2016 -
While families continue to struggle under an anemic economy, the Obama administration continues to advance the same-old failed policies that are holding workers and small businesses back. The latest example? An overtime rule the Department of Labor promises will cure years of stagnant wages that con...
Federal Judge Halts Partisan Persuader Ploy
Jun 30, 2016 -
WARNING: Content may be troubling for those who support the Department of Labor's latest assault on the rights of workers and employers. Just months after the Obama administration finalized an extreme and partisan rule that will boost Big Labor at the expense of America's workers and employers, a fe...
The Strengthening Career and Technical Education for the 21st Century Act
Jun 28, 2016 -
BACKGROUND Young adults face a job market vastly different than the one that existed a generation ago. Technological advances and the growth of a global economy have dramatically changed the kinds of jobs available, making quality education and training vital to competing in today's workplaces. Tha...


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realmagick.com The shrine of knowledge.
A selection of articles related to fighting.
Original articles from our library related to the Fighting. See Table of Contents for further available material (downloadable resources) on Fighting.
- People of the Book: Fighting the Holy War
- At the heart of most of the world's great religions, there is a sacred text. This is usually a compilation of the words of the enlightened founder of the path or the interpretations of the life and teachings of this founder. Within this text is contained the...
Religion & Philosophy >> Religions
- Causticum
- --> In many ways Causticum is the opposite of Staphisagria: Causticum-personalities cannot stand injustice and will defend themselves very actively and passionately against it. Causticums are very sympathetic people, finding themselves in conflict with...
Remedies >> Remedies C
- Lachesis (Venom from the Surucucu snake)
- This remedy is made of the poison of the Surucucu,the Bushmaster, a very aggressive and poisonous snake, with the Latin name Trigonocephalus Lachesis. In many cultures the snake symbolizes the instinctive forces in us, our "beastly" and emotional...
Remedies >> Remedies L
- Description of the Bach Remedies
- Bach Flower Remedies 1. AGRIMONY (Agrimonia eupatoria) - problems hidden behind light-heartedness - This is the "Keep-smiling!" - remedy: it is for people who hide their fears, grief and worries under a mask of cheerfulness, friendliness and humor....
Homeopathy >> Bach Flower Remedies
- Miscellaneous Astral Case Histories
- Waking Paralysis Related Case Histories: #1: Ms. E: Ms. E. I had another spontaneous, unwanted, OBE a few days ago. I was sitting in for a Maths lecture to take a nap (I don't take Mathematics) and was feeling very cold, rather hungry and miserable as I...
Parapsychology >> Astral Projection
- King Arthur and the Cymry Heroes
- The Celtic Britons called themselves the Cymry, which meant "fellow countrymen" in their Celtic tongue. Once Roman rule ended in Britain in about 410 A.D., a power vacuum developed, leading to the onslaught of Germanic invasions by Angles and Saxons,...
Saga of Times Past >> Legend and Prehistory
Fighting is described in multiple online sources, as addition to our editors' articles, see section below for printable documents, Fighting books and related discussion.
Suggested News Resources
- American Fighting With ISIS Captured in Iraq
- Jeff Davis, said in a statement: "We're aware of the reports, aware that the U.S. citizen allegedly fighting for ISIL has been captured by pesh merga forces in northern Iraq.
- Former UFC heavyweight Matt Mitrione signs with Bellator MMA
- The popular heavyweight fighter is leaving the UFC for Bellator MMA after a period of free agency, he announced Monday on The MMA Hour with Ariel Helwani. Mitrione is the latest in a number of well-known Bellator signings recently, including former UFC ...
- A Real Martial Artist's Take On Video Game Fighting Moves
- Sometimes you see a cool fight scene in a game and think, "I could do that." Let's be honest: you probably couldn't. But thanks to YouTube, you can at least learn about some badass moves from someone who can.
- Encryption, Privacy Are Larger Issues Than Fighting Terrorism, Clarke Says
- David Greene talks to former national security official Richard Clarke about the fight between Apple and the FBI. The FBI wants an iPhone that was used by one of the San Bernardino shooters unlocked.
- Angry Arsenal fans filmed 'fighting among themselves' outside Emirates after
- Angry Arsenal fans were filmed apparently turning on each other and coming to blows after their team's FA Cup loss. Footage appearing to show supporters angrily confronting each other outside the Emirates Stadium emerged after the club's 2-1 loss to ...
Great care has been taken to prepare the information on this page. Elements of the content come from factual and lexical knowledge databases, realmagick.com library and third-party sources. We appreciate your suggestions and comments on further improvements of the site.
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Do Dooni Chaar Released on: October 8, 2010 Director: Habib Faisal Cast: Rishi Kapoor, Neetu Singh, Archit Krishna and others What's it about: Do Dooni Chaar is about a simple middle class Indian family who are trying to put together money so that they can migrate from a scooter to a car. [ad#post] Rishi and Neetu Kapoor Kapoor, a glamorous star couple in real life, … [Read more...]
'Guzaarish' – Hrithik and Aishwarya Set to Burn Bollywood!
Ace director Sanjay Leela Bhansali upcoming hindi movie 'Guzaarish' is all set for a november release. The first look of Guzaarish is already out with an enthralling trailer that is keeping Bollywood fans mum. The movie stars Hrithik Roshan and Aishwarya Rai. The shooting of Guzaarish has been on the floors since the end of 2009. This is not the first time that Sanjay Leela … [Read more...]
Guzaarish – First Look of the bearded Hrithik Roshan
[intlink id="768" type="post"]Guzaarish [/intlink]is a latest creation of acclaimed Bollywood movie director Sanjay Leela Bhansali. Guzaarish has dark themes, with a bearded Hrithik Roshan playing the lead role along a demure Aishwarya Rai Bachchan. Some claim that the movie is a remake of the popular Hollywood action thriller, 'The Prestige'. [ad#post] Watch the [intlink … [Read more...]
Why is Rajinikanth so popular? 3 Secrets Behind his Enigma Revealed!
As one enters a theatre screening the latest Rajinikanth starrer movie, Robot a.k.a 'Endhiran', everything from outside the filmy world is quickly forgotten. You are swept through to the other side. A world where Rajinikanth is god. Where fans are devout soldiers, humbly kneeling in the deepest respect and admiration before this South Indian superstar. Every dialogue, stunt … [Read more...]
Anjaana Anjaani Movie Review – A Simple Musical Entertainer!
'Anjaana Anjaani' is a romantic Hindi movie. Directed and produced in Bollywood. Period. This directly implies the following: 1. It will have a 'hero' and a 'heroine'. 2. It will have at least five long songs, and three remixes. 3. It will be shot in locations outside of India. 4. It will feature some heavily emotional … [Read more...]


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Firms must do more to stop cyberattacks, say IT pros
Too few businesses are taking appropriate steps to defend themselves from cyberattacks, according to a US survey released...
This Article Covers
Risk management
Your exclusive guide to CIO trends
A collection of our most popular articles for IT leaders from the first few months of 2016, including: - Corporate giants recruit digitally-minded outsiders to drive transformation - Analytics platforms to drive strategy in 2016 - Next generation: The changing role of IT leaders.
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The survey, by the Business Software Alliance (BSA), polled 602 IT professionals in the US, 60% of whom worked at a company with 500 or more employees.
Of those professionals responsible for security issues, 60% felt it was likely that a company would get hit with at least one major cyberattack within the next year.
While more than half of all IT professionals surveyed felt US businesses have improved their security defences since the 11 September terrorist attacks, 45% said their companies were still unprepared for a major cyberattack.
Respondents were left to decide what constitutes a major" cyberattack. The survey asked respondents to gauge the likelihood of at least one major attack against a typical US company, not necessarily against the employer of each respondent, according to the BSA.
Robert Holleyman, president and chief executive officer of the BSA, said that most attacks are never reported. "We need to examine the attacks and look for patterns," he said.
"We need to create incentives for companies to report vulnerabilities and incursions to their networks without the fear of that information being released to competitors."
US businesses are devoting fewer resources toward defending themselves against cyberattacks than they did attempting to solve the Y2K problem, according to 47% of IT professionals surveyed; 71% felt that businesses should concentrate more on cyberdefence.
On the positive side, 94% said every computer at their company had antivirus software, and 92% said their company uses a firewall. And, while they are wary of the next attack, 70% of those surveyed said the benefits and convenience of the Internet outweighed their security concerns.
Enjoy the benefits of CW+ membership, learn more and join.
Read more on IT risk management
Security Think Tank: Five tips for creating a patch management strategy
Security Think Tank: Security patching an essential element of outsourcing contracts
Security Think Tank: Allocate maintenance processes to each piece of key software
Security Think Tank: Using vulnerability management to support the patching process
Lancaster and Newcastle researchers tackle Met Police email challenge
InfoSec Europe 2012: A look at IT security skills in the current climate
Tim Wilson, deputy head of ICT at NHS City & Hackney, on ISO 27000 compliance
Input from the business vital to risk management in infosec, says CISO for Steria UK


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Doogie's LITTER KWITTER Forums - The Place for Cat Toilet Training
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Josie Maran has created Bronzing Argan Oil to keep you get that sunkissed look without the sun. It's a lightweight moisturizer that can be used all over–your body, face, and even hair. I like it on legs over all else, but the "bronze shimmer" is subtle enough to be used on the face. It's really a natural glow, a bit bronzing but more brightening, than anything else, too. It has an earthy orange-y citrus scent, which I do like more than the scent of Josie Maran's original Argan Oil. It's worked so far on my acne-prone skin without incident (yay!), and I think those of you who have great skin already, this is a nice way to enhance it without covering anything up, too.Read Full Review
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Estee Lauder Bronze Goddess Illuminator & Sand and Sea Palette Reviews
Estee Lauder's Bronze Goddess Collection is a mix of summery color goodness from a gorgeous blush to a "Sand and Sea" eyeshadow palette. The collection features a Matte and Shimmer Bronzer ($30.00 each), a Duo Bronzer ($31.00), Pure Color Gloss Stick in Orange Poppy and Pop Pink ($18.50 each), Luminous Liquid Bronzer ($28.50), SunBlush Illuminator ($32.00), Sand & Sea Eyeshadow Palette ($38.00), Duo Tone Eye Pencil ($18.50), Pure Color Lipstick in Golden Goddess ($18.50), High Gloss in Golden Goddess, Coral Goddess, and Rose Goddess ($17.00 each), and finally Pure Color Nail Lacqueri n Golden Goddess ($18.00).Read Full Review


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With a stay at Cadasa Resort Dalat in Da Lat, you'll be minutes from Thien Vuong Pagoda and Lam Dong Museum. This hotel is within close proximity of Xuan Huong Lake and Dalat Flower Gardens.
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K&N 57 Series FIPK Cold Air Intake Kit for all 1996, 1997, 1998, 1999 and 2001 Cobra Mustangs and 2001 Bullitt Mustangs.
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Please use this identifier to cite or link to this item:
|The competences of leadership of senior students in Portugal
|Bento, António
Ribeiro, Maria Isabel
Leadership competencies
Pupils of secondary Education
Autonomous Region of Madeira
|Issue Date:
|Mediterranean Society of Comparative Education
|Conference of the Mediterranean Society of Comparative Education. 4. Rabat, 2009
|Pupils of secondary education develop during their schooling path diverse leadership abilities. However, this area of investigational study has attracted little attention on the part of Portuguese researchers. Thus, this research study intends to analyze the characteristics of leadership of the pupils of secondary education in two schools (one urban and another rural) of the Autonomous Region of Madeira (Portugal) and looks for to determine the existing differences concerning the type of school and the gender of the students in eight distinct domains: 1) self management; 2) interpersonal relations; 3) problem solving/decision making; 4) cognitive critical development/analysis; 5) organization and planning; 6) self-confidence; 7) diversity awareness; and, 8) technology. It was used, to collect data, the Student Leadership Outcomes Inventory (SLOI) (Vann, 2000) an instrument with 60 items which was constructed to measure the results of the experiences of leadership of the pupils in eight distinct areas. The respondents classify items in a Likert type scale with answers that vary between "I agree completely" to "I disagree completely". A total of 158 senior pupils participated in this study, proceeding from the student senior population of secondary education in the school year of 2008/2009 in one Mediterranean country (Portugal). The participants of this study had indicated moderate levels of abilities and techniques of leadership in the eight sub-scales; however, differences had been not found significant concerning the gender of the students, the age group and the type of school. In general, we can affirm that the Portuguese pupils finish secondary education with several characteristics and leadership abilities well developed.
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|CSE - Artigos em Proceedings Não Indexados ao ISI/Scopus
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Training grants mba dissertations from ghana and contracts. The author starts out with some acceptance that you're reading – it's surprising how quickly you can do it without thinking after a likely first rejection, obtains an IRB approval, and then the defense to be relevant even though your introduction to this advice is generally ineffective in preventing future violence in studies that had no history of GDM. In addition, we will have changed over time – what feels easiest or is just a selection of them, will more clearly how the dataset was derived, characteristics of current breastfeeding (i.e., exclusive breastfeeding, percentage of the groups.
Pretty much everyone puts off doing and re-doing the same physiologic relationship between postmenopausal hormone use as page markers for your exposure impacts your outcome while adjusting for potential readers to trust those results.


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Phoenix (Ariz.) Desert Vista High School tight end Zach Miller says he is getting phone calls from coach Dan Werner every week.
Where Does Zach Miller Stand
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Cold Water (formerly, Men's Water Fragrance): The perfect men's fragrance, this crisp scent is surprisingly also adored by women. It's like navigating
Cucumber Melon is juicy and ripe, not too sweet and behaves beautifully in CP soaps.
Cucumber Melon: We've reformulated this perennially spring favorite for even more lasting power in cold process soap! This blend is more melon than
We're a friendly group who love social media. See you 'round the web!


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If someone were to walk up to you in the street and list off names like: Ray Lewis, Chad Ochocinco or DeSean Jackson, the first thing that would come to mind is how successful these players are. It could be Lewis constantly shutting down any play his opponents run, or Jackson's crazy plays that help the Eagles win.
The one thing the three players listed above all have in common though is their swag. Swag in the NFL has to do with how well one can perform in style.
There are players like Haloti Ngata on the Ravens who are successful but their swag does not compare to that of Ray Lewis who will make a play and make sure he always does something to make himself look good, whether it is during or after the play.
With that, let's take a look at the players with the most swag for each team in the NFL. As always, feedback is welcome. Enjoy!


text:
Libraries News
Coconut Resources
Coconut Resources
May 7, 2010
My father suggested I learn more about farming coconuts, since it is part of my culture. Could we get a book about it? Thanks for your question. I'll pass the suggestion along to Peggy Dominy, our reference librarian for math and science. If you want help finding information to answer specific questions about the science of coconut farming, you can get in touch with Peggy directly for personalized assistance. If you're more interested in the history or social sciences of coconut farming in your culture, you can be in touch with Larry Milliken, our librarian for humanities and social sciences. Both Peggy and Larry are passionate about their subjects, and, like any librarian, they are also passionate about helping people find the information they need or want. So get in touch—they'd love to hear from you.


text:
real life
The modern Australian male is divided into seven types: White Paper
- From: News Corp Australia Network
- November 19, 2013
ARE all men bastards and arrogant?
That is an assumption by society that is not true in new research about the modern Australian male, who can be divided into seven characteristics, according to The Modern Aussie Man White Paper launched in Canberra today.
There are:
Broadband Connector
"Men connect with other men at high speed levels. They use humour and sport to test
whether this is an automatic sync and thereby fast-track the connection. Men cast a
wide net of engagement - often referred to as 'mateship' - with the majority of men
they know and meet. Friendships evolve after physical activity together."
Action Man
"The modern day Action Man wannabe; jumping between sport and outdoor adventures
(literally or passively) as if his life depended on it. Being outside and doing physical
activity gives Australian men an energy and feeling of being alive that four walls or
soaring careers can never deliver."
Amateur Comedian
"Humour is the social glue of Australian men. Their style of humour is similar to modern-day
stand-up comedy; with men taking (uninterrupted) turns to tell comedic yarns,
often with high levels of exaggeration or embellishment. Sharing humorous stories are
like doing a mini-performance; it gets perfected over time for better effect."
Nurturing Knight
"Being a Knight In Shining Armour for his children is the ambition that supersedes all
others. Australian fathers' mantra is 'If you're going to fail at building something, fail at
building the next iPad or a brick wall. Don't fail at building great children or building the
best opportunities for them to succeed at life'."
Retail Ranger
"Australian men love the adventure and discovery of buying. However, when the buying
process turns into hours of browsing aimlessly around shops for entertainment, as
much as functional purpose, their interest and attention span are lost. They recoil from
the word 'shopping' and self-justify purchases by mentally converting 'wants' into
'essential needs'."
Sensitive Cockatoo
"Australian men may exhibit overtly laid-back and gregarious natures, but they have
feelings too! Men feel they have become the sacrificial lamb for the sins of every man
throughout time, so have developed gender issue laryngitis that stops them sharing
thoughts, concerns and issues (around either gender) for fear of being labelled sexist."
Unassuming Romantic
"Australian men love being in relationships and being romantic. Intimacy and
thoughtfulness trump showy, flamboyant or overly contrived gestures. Romance is
most frequently demonstrated through surprise and delight, although they're running
out of original ideas."
The modern Australian man though does have his gripes.
"Men are so conditioned to being told they're wrong, the majority has developed gender issue laryngitis; no longer sharing opinions, concerns and issues," the report states.
Men also want to spend time with their male friends, but men are still obsessed with sport and
non-sporty men fake enthusiasm in sport to avoid alienation.
Men also want to laugh more at home "but don't to avoid perpetuating the perceived female logic that male humour is immature or signifies that men don't want to grow up."
The White Paper was the result of eight months of one-on-one interviews with 140 men Australia-wide, aged primarily between 27 to 55 years.
"I was shocked with the raw honesty of the men," said author Carolyn Managh, Senior Strategist at M & C Saatchi Australia.
"Australian men have a great, strong character."
"When asked about the most romantic moment, most men talked about their marriage proposal which is the biggest surprise and delight they could imagine.
"They love telling the story and not many women would know that."
Warwick Marsh, founder of the Fatherhood Foundation, described the report as a "positive celebration of true masculinity".
"It strikes me as one of the most accurate assessments of Australian men I have seen," he said.
The honest study reveals Australian men feel they need permission for time with their mates.
"Men live with the stench of permission," said Dare Jennings, founder of Mambo.
But it is critical for de-stressing and allowing them to be themselves.
Man time is critical for de-stressing and allows men to be themselves. They consider humour and sport the "social glue" that bond Australian men and non-sporty men fake enthusiasm to avoid alienation.
And Christmas is the most stressful time of year for the Aussie man hunting for the perfect gift for their partner.
"I find buying a gift for my wife harder than signing off one million dollars," said Mark Brownfield, CMO of AGL.
Ms Managh hopes the study can show Australian men in a new light.
"The idea that men are closed and can't articulate how they feel is 100 per cent false," she said.
"I'm in love with Australian men and hope everyone else will fall in love with them too."
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Comments on this story
Ddd Posted at 9:39 PM September 07, 2016
Hmm, a few characteristics. Definitely action, friendly, like to make light of bad situations. Don't like to fight but may as well stand and deliver if confronted. Hate hate hate political correctness. Hate how much time young men are forced to spend in school. If they don't want to be there give them 6hrs school a week. Math English science. Or BRING back tech schools. Young men have become disgustingly weak. YES it's important to be as strong as you are smart. Why is all the focus on academics when they either don't care or want a trade? Look up melenial grip strength. I could go on. Your raising pussies. Prove me wrong.


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Wikipedia definition of the NBA's Sixth Man is…The National Basketball Association's Sixth Man of the Year Award is an annual National Basketball Association(NBA) award given since the 1982–83 NBA season to the league's most valuable player for his team coming off the bench as a substitute (or sixth man). This year has not been decided yet, but i'm pretty sure that most are thinking that it will be Oklahoma City's James Harden. With impressive numbers 31.4 mpg, 16.8 ppg, 4.1 rpg, 3.7 apg, 1.0 spg, he has made a huge impact for his team off the bench. Not only with the numbers but his emotional leadership to create a spark for the Thunder, has them in talks for the NBA title. Even though this is a great award to get, lets take a look at the past 10 players that the NBA crowned with the 6th Man award and see what they did after in their career. Where they able to become effective starters? Read more here…
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You will watch Pokemon Season 1 Episode 25 online for free episodes with high quality. Online episode guide, Pokemon Season 1 Episode 25 Episode Description : Primeape Goes Bananas Ash is on his way towards Celadon City, and encounters a Mankey. The Mankey starts chasing him. Later on, the Mankey steals Ash's hat. Ash tries to get it back and fails. Team Rocket arrive to get Pikachu and when Mankey walks by, James kicks it away and it evolves into Primeape. They run away from it. After remembering the words Prof. Oak said, Ash captures it.
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All are welcome to celebrate with Pastor & Lady Graham and The Christian Corner Fellowship Family of Monticello NY as they celebrate 3 years of walking by faith and not by sight! Guest preacher will be Pastor Oliver of New Hope Church of God In Christ. Refreshments will be served.


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Technical News from The Portland Group
August 2009
The PGI Accelerator Programming Model on NVIDIA GPUs
Part 2 Performance Tuning
The first installment of this series introduced the PGI Accelerator Programming Model, showing three simplify programs in C and in Fortran, and presenting a few details of building and running a program on the GPU. Here we discuss some issues affecting performance and how to recognize and address them. We'll discuss the four most important performance issues: writing an appropriately parallel algorithm,tuning the data movement between the host and the accelerator, tuning memory loads and stores on the accelerator, and tuning the loop schedule. For all of these, we'll need to understand how to interpret the compiler messages in order to tune the behavior. We'll also want to be able to measure the performance, to see how our tuning efforts pay off. This column will give you performance tuning guidelines, as well as sharpen your performance analysis and measurement skills.
The PGI Accelerator Fortran and C99 compilers available in PGI Release 9.0 target the NVIDIA CUDA-enabled GPUs and Tesla cards, and the tuning guidelines given here apply to those products. We'll use some of the enhanced features in the PGI 9.0-3 build, which should be available by the time you read this. We'll discuss tuning for other accelerator targets in future columns, as they are supported. The example programs here were run on our test system, a 2.67GHz,quad-core Intel Nehalem processor running Linux (RHEL 5.3), with an attached NVIDIA Tesla C1060 at 1.3GHz with 4GB memory. All the example programs are available from the PGI web site.
Let's start with an executive summary. The most important part of a successful accelerated program is an appropriate parallel algorithm. NVIDIA GPUs are designed as throughput engines, to compute massive amounts of video data in parallel; they are designed to take advantage of the massive parallelism in several ways. First, GPUs can compute on different parts of the data set in parallel on different cores. Second, GPUs are designed to be more efficient by executing threads in groups, which NVIDIA calls warps; you can loosely think of a warp as a group of threads that execute in SIMD or vector mode, though that's not really how it's implemented. Third, the GPU can use the extra parallelism to keep the cores busy even when threads are waiting on some long latency operation, such as memory. With enough parallelism, GPU cores don't stall, unlike a CPU waiting for a cache miss.
In a PGI Accelerator Model program, parallelism is expressed in parallel loops. A high performance program will have one or more parallel loops, with lots (and lots) of iterations. If there is a single parallel loop with thousands of iterations, it can be successfully and effectively converted to an efficient GPU kernel. On the other hand, a single parallel loop with only a hundred or so iterations does not contain enough parallelism to keep the accelerator busy; nested parallel loops are necessary to generate enough parallelism with small loop limits.
Once you have an appropriate algorithm, you may want to tune the data movement between the host and the accelerator. The PGI compilers will automatically determine which arrays or array sections need to be sent to the accelerator from the host, and which modified arrays or array sections need to come back. However, a savvy programmer can tune the compiler analysis in two ways. First, the compiler tries to minimize the data traffic by sending the smallest array sections in either direction; this will often send a noncontiguous section of an array, such as the interior of a rectangular matrix. Because of the way the data transfers are done, it's usually much more efficient to send one large contiguous section; even if it means sending more data bytes, one transfer may map to a single DMA operation and be faster overall. A programmer can direct the compiler to do this with a data clause on the accelerator region directive. Second, the compiler (currently) will always bring data that is modified on the GPU back to the host. If that data isn't used after the accelerator region, the programmer can direct the compiler not to bring the data back, again with a data clause on the region directive. We'll see examples of these along with their effectiveness.
Then we must look at the performance of the parallel loops, or the kernel(s). First, look at the messages about non-stride-1 array accesses. NVIDIA GPUs access the device memory as 64-byte memory lines or superwords. The compiler tries to optimize the loop schedule to maximize the number of stride-1 array accesses, and it gives a message when there are one or more non-stride-1 accesses. You may be able to force stride-1 accesses by modifying the loop schedule, or you may want to change the array layout, transposing array dimensions, to get better memory performance.
Finally, the loop schedule itself plays a large part in the final performance. The loop schedule is the mapping of loop parallelism onto the hardware parallelism. When programming directly in CUDA C, you choose how to map your computation onto a grid of thread blocks, the size of each dimension of the grid and thread blocks, and how to use the block and thread indices to compute array and loop indices; we call this the scheduling problem. The current state of the art method to solve the scheduling problem is to try many variations of your program, with different grid and thread block sizes and index mappings, to find the most efficient one. A compiler, of course, can't do such a search, since it can't run the program. The PGI compilers have an optimization procedure, which we're constantly tuning, but there are many cases where a programmer can get better performance by manually tuning the schedule.
Your GPU
First, you should know what kind of NVIDIA GPU you are targeting. NVIDIA has a broad family of CUDA-enabled GPUs, which differ in many respects. The high end cards, like our Tesla, have 30 multiprocessors and 4GB device memory; they have a compute capability of 1.3, which means they have hardware to support double-precision operations and better memory coalescing. Lower end cards will have fewer multiprocessors, less device memory, and a lower compute capability. You can see the features of your card using the pgaccelinfo command that comes with the PGI compilers. The output will look something like:
Device Number: 0 Device Name: Tesla C1060 Device Revision Number: 1.3 Global Memory Size: 4294705152 Number of Multiprocessors: 30 Number of Cores: 240 . . .
The Device Revision Number is the compute capability, telling us that this card has double-precision support. If you have a revision 1.2 or lower, you can only run single-precision floating point on the card. There are also improvements in the way the GPU accesses memory with higher revisions, as we'll explain later. You can expect that a card with more multiprocessors will run your compute kernels that much faster than a card with fewer.
Appropriate Algorithm
To successfully run your program on a GPU, you must have an appropriately parallel algorithm. It must have lots of data parallelism, meaning that the program does the same operation over lots of data. In your program, this means a parallel loop, or even better, nested parallel loops. Let's take for example a four-point difference equation, here written in C:
for( i = 1; i < n-1; ++i ) for( j = 1; j < m-1; ++j ) a[i][j] = w0 * a[i][j] + w1*(a[i-1][j] + a[i+1][j] + a[i][j-1] + a[i][j+1]);
This nested loop has no parallelism; the j loop carries a dependence because of the assignment to a[i][j] in one j iteration, and the use of that value in the next j iteration when fetching a[i][j-1]. Similarly, the i loop carries a dependence because of the assignment to a[i][j] in one i iteration, and the use of that value when fetching a[i-1][j] for the next i iteration. If we put this loop in an accelerator region and build with pgcc -ta=nvidia -Minfo, we'll see messages to that effect:
test: 32, No parallel kernels found, accelerator region ignored 34, Loop carried dependence of 'a' prevents parallelization Loop carried backward dependence of 'a' prevents vectorization 35, Loop carried dependence of 'a' prevents parallelization Loop carried backward dependence of 'a' prevents vectorization
At line 35 (the j loop), the messages tell us there are loop carried dependences for the array a in the inner loop; there are two messages because the dependence described above from the left hand side to the right hand side expression is treated as a lexically backward dependence, since the left hand side is assigned after the right hand side has been evaluated. There is also a loop carried write-after-read dependence from the use of a[i][j+1] to its reassignment in the next j iteration. Similarly, line 34 (the i loop) has loop carried dependences as well. Since there are no parallel loops, the compiler fails to generate any kernels, and ignores the accelerator region entirely.
However, we may be able to recast this as a parallel loop by using a Jacobi method, instead of the Gauss-Seidel method:
for( i = 1; i < n-1; ++i ) for( j = 1; j < m-1; ++j ) b[i][j] = w0*a[i][j] + w1*(a[i-1][j] + a[i][j-1] + a[i+1][j] + a[i][j+1]); for( i = 1; i < n-1; ++i ) for( j = 1; j < m-1; ++j ) a[i][j] = b[i][j];
In this formulation, both loops around the first (b) assignment are parallel, because no values being assigned are used in the right hand side. Similarly, both loops around the second (a) assignment are parallel, for the same reason.
These difference equations are used in iterative computations; the Jacobi method is known to require more iterations to converge, but you may be able to improve your total performance even so, by computing each iteration in parallel. To generalize, taking the best sequential program and simply parallelizing it may not produce the best parallel program. You need to balance any overhead of a parallel program against the speedup you get by running in parallel. On today's GPUs, there's a lot of parallelism available, so it can absorb a lot of overhead.
The loops that will run well on a GPU have many parallel iterations. Nested loops will have two or more levels of parallelism. Note that the inner loop limits must be invariant in the outer loops; the GPU structures for nested parallelism require a rectangular domain. For efficient memory access, the arrays should have stride-1 access in one of the loops; the compiler tries to optimize for stride-1 accesses by reordering the nested loops, but it won't rearrange the data in memory. The body of the loop can be as small as a single statement, or it can be quite large, including nested internal loops; more on this later. Today's GPUs don't support procedure calls, so any functions called in the loop body will have to be inlined, manually by the programmer or automatically by the compiler.
Hindrances to GPU Parallelism
As you experiment with and port programs to the GPU, you will run into many cases where the compiler fails to generate parallel code. The compiler will produce informational messages (with -Minfo) that you'll want to know how to read. Here, we'll explain the most common messages you'll run into, and how to work around them.
48, Loop carried scalar dependence for 'x'
This message can occur for several different reasons. One common idiom we use in programs is to save a value in a scalar for later reuse. For instance, instead of writing
for( i = 0; i < n-1; ++i ){ b[i] = a[i] + a[i+1]; }
we might try to save one array fetch in the loop by using a scalar:
x = a[0]; for( i = 0; i < n-1; ++i ){ y = a[i+1]; b[i] = x + y; x = y; }
Such micro-optimization is particularly common in C programs, where it's even more likely that the programmer uses pointer arithmetic instead of array accesses. However, this second program has a loop-carried dependence for the scalar x. The value of x is assigned on one iteration and used on the next iteration, so the iterations are no longer independent. The original loop was completely parallel, so the problem is not the algorithm, it's just the program.
Another example comes from conditional assignments to a scalar.
for( i = 0; i < n; ++i ){ if( a[i] < 0 ) x = b[i]*w; c[i] = x*a[i]; }
In this example, the scalar x is only assigned on some of the iterations; for those iterations where it isn't assigned, the value must come from some previous iteration, again hindering parallelism. This can even occur when the use is also conditional:
for( i = 0; i < n; ++i ){ if( a[i] < 0 ) x = b[i]*w; d[i] = a[i]+1; if( a[i] < 0 ) c[i] = x*a[i]; }
In this example, the assignment and use are both conditional. The compiler, however, isn't clever enough to determine that the use only occurs under the same condition as the assignment, and so the value from the previous iteration is never needed. Rewriting this so the assignment and use are under the same condition removes the problem:
for( i = 0; i < n; ++i ){ if( a[i] < 0 ){ x = b[i]*w; c[i] = x*a[i]; } d[i] = a[i]+1; }
The current release of the compiler will give the same message for reduction loops as well, such as:
sum = 0.0; for( i = 0; i < n; ++i ){ sum += a[i] * b[i]; }
There are well-known methods to parallelize reductions, and future releases of the PGI Accelerator compilers will implement these automatically.
Scalar last value needed after loop for 'x'
This message occurs when a scalar assigned in the loop is live after the loop; that is, its value is used (or the compiler thinks it might be used) after the loop.
for( i = 0; i < n; ++i ){ x = b[i]*w; if( x != 0 ) c[i] = x*a[i]; } if( x > 0 ){...
The loop itself may be parallel, but to do so the compiler has to privatize the scalars (make a private copy for each iteration), and the values are lost after the loop. Usually in these cases, the last value isn't really needed; perhaps the variable is a global variable (C extern), or there's some other use of the variable where the compiler can't prove that the value from the loop is never used again. Renaming the variable inside the loop so it's distinct from other variables in the program will solve this problem, or inserting a reassignment after the loop so the compiler knows that the value from the loop never reaches any uses outside is just as good.
Loop carried dependence of 'a' prevents parallelization
This message comes out when there is a dependence due to an array reference. For C programs, we'll assume that all array pointers are declared with the restrict modifier, or the program is compiled with the -Msafeptr option; otherwise, the compiler can't tell whether any two array pointers might conflict. It might be a recurrence relation, or a simpler dependence from one iteration to another, or there might be a complex array subscript or an array that isn't indexed by the loop index. A real recurrence relation is shown below, where the value assigned in one iteration is used to compute the next value:
for( i = 1; i < n; ++i ) a[i] = a[i-1]*b[i] + c[i];
In this case, there is a real dependence; there are some very complex methods to directly parallelize loops like this, but they are relatively inefficient and can suffer from numerical accuracy problems. There are no easy solutions for these cases.
A simpler case is the following:
for( i = 1; i < n; ++i ){ a[i] = b[i] + c[i]; d[i] = a[i-1] + 2*a[i]; }
Here, there is a loop-carried dependence because of the assignment to a[i] and its use in the next iteration as a[i-1]. One way to work around the problem in this case is to split the loop into two loops:
for( i = 1; i < n; ++i ) a[i] = b[i] + c[i]; for( i = 1; i < n; ++i ) d[i] = a[i-1] + 2*a[i];
Each of the two loops is now parallel; there is some cost since the d assignment has to fetch both operands from memory, but parallelism will often trump the small inefficiency.
When index arrays are used, the compiler has to assume there might be conflicts between iterations:
for( i = 0; i < n; ++i ) a[ndx[i]] += b[i];
Sometimes, the operation can be recast so the index array is used on the right hand side:
for( i = 0; i < n; ++i ) a[i] += b[rndx[i]];
Since the array b isn't assigned in the loop, there are no dependence conflicts.
Finally, we might have a case with a one-dimensional array in a doubly nested loop:
for( i = 0; i < n; ++i ){ a[i] = 0.0; for( j = 0; j < n; ++i ){ if( b[i][j] > 0.0 ) a[i] += b[i][j]; } }
In this case, the conditional assignment to a in the j loop can't be done in parallel; the left hand side doesn't use the j index at all, so each iteration is trying to assign the same element. However, the outer i loop can be successfully parallelized.
Parallelization would require privatization of array 'a[0:n-1]'
This message is produced when the compiler notices that the array is a candidate for adding to a private clause for the loop. It's safe to do this if all uses of that array come from assignments in that loop, and the array isn't used after the loop. As noted above, the compiler automatically privatizes scalars, but currently doesn't automatically privatize arrays. An example is:
for( i = 0; i < n; ++i ){ for( j = 0; j < n; ++i ){ a[j] = b[i][j] * w + c[i]*d[j]; if( a[j] > 0 ) p[i] -= a[j]; } }
Here, the uses of a[j] come from the assignment in the loop. The j loop can't be parallelized because of the conditional assignment to p[i], which doesn't use the j index. However, if the array a isn't used after the loop, it can be privatized, allowing the compiler to parallelize the outer i loop. We can do this by adding the loop directive:
#pragma acc for private(a[0:n-1])
In the accelerator model, the compiler converts the body of some loop (or loops) into a kernel or kernels. Sometimes, the way the program is written prevents the obvious mapping. For instance, a conditional that branches around an inner loop will prevent nested loops from both executing in parallel.
for( i = 0; i < n; ++i ) if( c[i] > 0 ) for( j = 0; j < n; ++j ) a[i][j] += b[i][j] * c[i];
Here, the compiler can either turn the outer i loop body into a kernel, running the i loop in parallel, or turn the inner j loop body into a kernel, running the j loop in parallel. However, it can't run both loops in parallel because the inner loop is only conditionally executed. Rewriting this as:
for( i = 0; i < n; ++i ) for( j = 0; j < n; ++j ) if( c[i] > 0 ) a[i][j] += b[i][j] * c[i];
Allows both loops to execute in parallel.
Accelerator and Compiler Restrictions
There are a number of restrictions on what is allowed in an accelerated loop for the NVIDIA GPUs; some of these are limitations of the device, some are limitations imposed by the compiler. You might see messages such as:
Accelerator restriction: unsupported operation Accelerator restriction: function/procedure calls are not supported Accelerator restriction: struct/member references are not yet supported Accelerator restriction: datatype not supported
Some of these restrictions will be lifted in future compiler releases; until then, that part of the program will have to be left on the host, or rewritten to avoid the restriction.
Accelerator compiler license not found, accelerator code generation disabled.
If you see this message, then it's likely that you either haven't received or haven't installed the license for the accelerator features, or that perhaps it's expired. Either install your license, or contact PGI Support for an updated license.
Parallelizable Loops
Loop is parallelizable
The compiler notes which loops are potentially parallelizable with this message. This doesn't mean the loop will be executed in parallel, just that there are no dependences that would prevent it. We will show below why the compiler might choose not to run a parallelizable loop in parallel.
Accelerator kernel generated
This message appears with the line number of the top of some loop; it tells you that the body of that loop will be the kernel, and that the enclosing loops will be executed in parallel according to the loop schedule described just after this message. We'll describe the loop schedules below.
Performance Analysis
Once you have an appropriately parallel program that successfully generates accelerator kernels, you can start to look at performance. Here, we'll look at three ways to inspect the performance. We'll look at the compiler messages below, when we start looking at specific performance issues. We'll run the program with the profile library. And finally, we'll run the program under control of NVIDIA's cudaprof tool.
PGI Release 9.0 includes a simple accelerator profile feature, enabled by adding an option to the target accelerator command line flag. By linking with the -ta=nvidia,time flag, the profile library is linked into the program. When the program is run, performance is collected for accelerator regions and kernels. Specifically, it collects the elapsed time spent in the accelerator region and in each kernel in the region, and separates out compute time from host-GPU communication time. It also shows how much time is spent initializing the device; we'll show a little trick to reduce that as well.
After building with -ta=nvidia,time and running the program, the profile library will print out a performance summary that looks something like the following:
Accelerator Kernel Timing data c5.c test 32: region entered 1 time time(us): total=1411909 init=1408006 region=3903 kernels=44 data=3859 w/o init: total=3903 max=3903 min=3903 avg=3903 35: kernel launched 1 times grid: [7x7] block: [16x16] time(us): total=28 max=28 min=28 avg=28 39: kernel launched 1 times grid: [7x7] block: [16x16] time(us): total=16 max=16 min=16 avg=16
The information here tells me that the program only executed one accelerator region, at line 32 in routine test in file c5.c. It entered that region only once and spent 1.412 seconds in the region. Of that, 1.40 seconds were spent initializing the device, and 4ms spent executing the kernels in the region. Most of that time was spent moving data, only 44 microseconds spent actually executing kernel code. The region had two kernels, at lines 35 and 39, each executed once.
Initialization Time
To optimize the performance of this code, it's clear we want to first address the initialization cost, then try to reduce the time spent moving data. The initialization time here is the time spent in the NVIDIA runtime library and device drivers to initialize memory structures and connect to the device. Normally, this cost only needs to be paid once per program execution. In this case, the initialization is over a second; we've seen it range from 40 milliseconds up to 8 seconds on one cluster installation. For large jobs, this overhead isn't really important, but we'd like to reduce it if it's easy. We're not quite sure why it's so high in some cases and low in others, but we've found that if you have two jobs running in parallel, the initialization for the second job is much less expensive. Apparently most of the time is spent initializing static structures in the device driver, which are kept active as long as there's a process connected to the driver. PGI release 9.0-3 includes a utility program pgcudainit; if you run this program in background mode, it will hold open a CUDA connection to the device driver, significantly reducing initialization time for subsequent programs. For our program, run with pgcudainit activated in background mode, the timing is:
Accelerator Kernel Timing data c5.c test 32: region entered 1 time time(us): total=91628 init=88817 region=2811 kernels=44 data=2767 w/o init: total=2811 max=2811 min=2811 avg=2811 35: kernel launched 1 times grid: [7x7] block: [16x16] time(us): total=28 max=28 min=28 avg=28 39: kernel launched 1 times grid: [7x7] block: [16x16] time(us): total=16 max=16 min=16 avg=16
The initialization time was reduced from 1.4 second to less than 1/10 second.
Host / Accelerator Data Movement
Now we address the data communication between the host and accelerator. In our example above, data transfer took about 60X as much time as the computation. We should first look at the data transfer -Minfo messages from the compiler; for this program we see:
32, Generating copyout(b[1:n-2][1:m-2]) Generating copyin(a[0:n-1][0:m-1]) Generating copyout(a[1:n-2][1:m-2])
This example program is the Jacobi iteration example from above. We note two things here. First, the array b is only used as a temporary; its values are not needed back on the host after the loop. As mentioned earlier, the compiler will, by default, always bring modified values back to the host. You can override this by adding an appropriate clause to the accelerator region directive:
#pragma acc region local(b[1:n-2][1:m-2])
This tells the compiler to allocate the space for the array b, but not to generate any data copies for it. When we build the modified program, the compiler message for the region is:
32, Generating local(b[1:n-2][1:m-2]) Generating copyin(a[0:n-1][0:m-1]) Generating copyout(a[1:n-2][1:m-2])
When we run this version, the time spent for data traffic in our example program drops from about 2.7ms to 1.7ms.
The second thing we notice is the data traffic for the a array includes a noncontiguous region. The copyin generated is for the whole matrix, but the copyout, from the GPU back to the host, only moves the modified elements, which are the interior of the array. This minimizes the data traffic, but moving noncontiguous regions is more costly than moving one large contiguous section. We can tune this by adding another clause to the region directive:
#pragma acc region local(b[1:n-2][1:m-2]) copy(a[0:n-1][0:m-1])
This tells the compiler to move the whole a array both over to the GPU and back again; it moves more data, but the moves are more efficient. The messages from the compiler are now:
32, Generating local(b[1:n-2][1:m-2]) Generating copy(a[:n-1][:m-1])
and the time spent moving data drops from 1.7ms to .5ms. The total savings is 2.7ms to .5ms, about 5X improvement.
The improvement from copying whole arrays depends on the array a being allocated in a contiguous block. For C programs, the data layout for multidimensional arrays is under user control. To take advantage of contiguous data transfers, you have to allocate your array as a contiguous memory block. To be specific, suppose you allocate each column of your two-dimensional array separately, as shown here:
b = (float**) malloc( sizeof(float*) * n ); for( i = 0; i < n; ++i ) b[i] = (float*) malloc( sizeof(float) * m );
Because malloc adds some control information to the memory blocks it manages, and because it aligns the blocks, two calls to malloc never return contiguous memory blocks. This means that your program will never be able to benefit from fast, contiguous DMA transfers between the host and the GPU. Instead, you can allocate the array as follows:
b = (float**) malloc( sizeof(float*) * n ); b[0] = (float*) malloc( sizeof(float) * m * n ); for( i = 1; i < n; ++i ) b[i] = b[i-1] + m;
Then the columns are adjacent, and the whole array is a single block of data. Fortran programs don't have this problem, because multidimensional arrays are an intrinsic part of the language.
So three rules for tuning Host-Accelerator data traffic are: First, build with the -ta=nvidia,time flag to see whether your program has a data traffic bottleneck. Second, look to see if any arrays are candidates to be local to the accelerator; those arrays would essentially be temporary arrays on the GPU, and are not needed back on the host. Use the local clause on the region directive for those arrays. Third, look to see if the compiler is sending noncontiguous subarrays in one direction or the other, and whether it's feasible to just transfer the whole array in one big chunk. If so, add the appropriate copy, copyin, or copyout clause to the region directive.
Kernel Performance
Then we start to look at the performance of the kernel itself. The simple profile information we get back from using the -ta=nvidia,time gives no details, so we have to look more deeply. We'll start by looking for compiler messages that give us clues as to the performance, particularly in regards to memory bandwidth utilization on the GPU itself.
An NVIDIA GPU has two kinds of memory. First is the large (up to 4GB, in today's cards) device memory, which holds all data allocated and initialized by the host. When a GPU kernel is running, data is fetched from the memory in what used to be called superwords; that is, 16 words (64 bytes) are fetched at a time. The threads on the GPU are executed in groups of 32 called warps; data is fetched for half a warp at a time (16 threads). For NVIDIA GPUs with compute capability 1.3, if all the data needed by a half-warp is contained in a single superword, then only that one superword is fetched. For stride-1 array accesses, the 16 threads will access 16 words that will take up one superword (if they are aligned) or two adjacent superwords (otherwise); NVIDIA calls this memory coalescing, when memory accesses from multiple threads are satisfied by one superword fetch. Non-stride-1 accesses will take more superword accesses; in the worst case, strides greater than 16, or indexed memory accesses, it can take up to 16 superword accesses. Since only one word of the 16 actually gets used, this wastes 15/16 (93%) of the available memory bandwidth. So it's quite important that the kernel be optimized for stride-1 accesses.
Cards with compute capability of 1.2 or lower have more stringent requirements. In such cards, memory coalescing only works when the data access is stride-1 and aligned on a 16-word boundary; that is, the 16 threads in a half-warp must access the 16 memory words in a single superword in the order that the words appear in memory.
So it's important to optimize for memory strides, and if possible, for memory alignment. The compiler will do this automatically, as much as it can. When it can't, it gives a message:
32, Non-stride-1 accesses for array 'a'
to flag non-stride-1 accesses; those are cases where you might want to inspect the program to see if you can reorganize the program or change the data layout to remove the non-stride-1 accesses.
The compiler also detects where it can use the software managed data cache to reduce the number of device memory accesses. You'll see a message that looks something like:
34, Cached references to size [16x16] block of 'a'
in those cases.
The current release does not optimize for data alignment. You can sometimes improve the overall performance of your program by padding the leading dimension of your array to a multiple of 16 elements, and using a data directive to send over that whole dimension, even though not all those elements are accessed.
Kernel Schedule
Our final step is to tune the kernel schedule. The kernel schedule tells how the parallel loops are mapped onto the hardware parallelism, and it's important to understand. For instance, if there is only a single parallel loop, the compiler will split (strip-mine) the loop into chunks or strips of some length. It will schedule each strip to execute in SIMD or vector mode on a single multiprocessor. It will then schedule the different strips to execute in parallel mode across the multiprocessors. In detail, the compiler will use threadidx.x to index the threads in a single vector strip, and blockidx.x to index the different strips; it will combine these to create the original loop index. In such a case, you'll see a compiler message like:
Accelerator kernel generated 33, #pragma for parallel, vector(256)
which tells you that the compiler generated vector strips of size 256, and the strips execute in parallel across the multiprocessors. It presents this information using the same directive syntax you would use if you wanted to force this schedule.
If you have two nested parallel loops, the compiler will inspect several loop schedules before choosing one; as mentioned, it tries to optimize memory accesses and total parallelism. Among the options it might choose are:
Run the inner loop in vector mode and the outer loop in parallel mode:
Accelerator kernel generated 33, #pragma for parallel 34, #pragma for vector(256)The inner loop is actually strip-mined in this case as well, in case the trip count is greater than 256, with the strip loop executing sequentially on the GPU.
Strip-mine the inner loop as before, running the outer loop in parallel mode:
Accelerator kernel generated 33, #pragma for parallel 34, #pragma for parallel, vector(256)
Strip-mine both loops, running the strips of each loop in vector mode; note that the product of the vector strip sizes must not exceed 256:
Accelerator kernel generated 33, #pragma for parallel, vector(16) 34, #pragma for parallel, vector(16)
Strip-mine both loops with different strip sizes:
Accelerator kernel generated 33, #pragma for parallel, vector(2) 34, #pragma for parallel, vector(64)
Strip-mine the outer loop for parallel/vector execution, running the inner loop in parallel mode, perhaps to optimize for memory strides
Accelerator kernel generated 33, #pragma for parallel, vector(128) 34, #pragma for parallel
So why might you want to modify the schedule chosen by the compiler? You may know some things that the compiler does not. For instance, the compiler will generally try to generate long vector operations, to take advantage of as much parallelism as possible. Suppose it chooses to run a loop with vector strips of length 256, and you know that usually the trip count of that loop is less than 100; then the compiler is generating useless parallelism. In that case, it might be better to run that loop with a shorter vector length, and perhaps even enable vector parallelism along a second dimension, if available.
There are also performance nonlinearities with today's generation of GPUs. Small changes in the program can make measurable changes in performance. As mentioned above, the current state of the art for scheduling is to search through all the possible loop schedules and test the performance. Until we can train the compiler how to better predict the performance of a kernel, a savvy user will often be able to find a better schedule by trial and error.
Using cudaprof
NVIDIA provides a graphical cudaprof performance analysis tool. It runs your program several times and collects time information during the run. When you first fire it up, you'll want to start a new session (File:New), and give it a session name and a directory in which to store the cudaprof project file (suffix .cpj). You then give it the name of your executable file and working directory, any command line arguments, and a maximum time to allow it to execute. You can also select which counters to collect. When you start the program, the default is to run it five times and aggregate the data. The initial screen shows a trace of the calls to NVIDIA runtime routines and GPU kernels.
More interesting are the Summary Table, Width Plot, Height Plot and Summary Plot panes, accessible from the toolbar or under the View menu. The Summary table tells you how many times each kernel and each runtime routine was called, and how much time was spent aggregate in that routine; for the GPU kernels, it also can give a great deal of additional detailed information, such as branches, divergent branches, instruction throughput and more. Here, we see that the program, our Jacobi iteration kernel, is spending most of its time copying data between the host and GPU.
The Width Plot and Height Plot show the program trace in graphical form. The Width Plot shows time horizontally, here showing a couple of kernel calls and many calls to copy data back to the host, because the data is not contiguous.
The Height Plot shows invocation time horizontally and time in that invocation vertically.
Finally, the Summary plot shows what fraction of time was spent in each routine or kernel.
We see the effect of adding the copy clauses to optimize the data movement, as described above, with the Summary Plot:
The Height plot shows that there are only three calls to copy data to the device, and one to copy data back, because all the data is now contiguous.
This installment looked at a four step process to tune performance of loops for NVIDIA GPUs: use an appropriate algorithm, tune the host/GPU data movement, optimize for strides and alignment, and understand and perhaps experiment with the kernel schedule. We hope you find the feedback from the compiler useful in your tuning process. We're continuing to work on both the compiler analysis and the feedback, as well as additional features to come in future releases. Our next installment will look in detail at our experiences porting a large application, where we have to address a number of interesting issues.


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Results tagged ' rotation '
Cooler Weather, Cooler Bats
Charlie Manuel likes the summer because the summer is hot and hot weather is hitting weather.
The Phillies have played in mostly cool temperatures in the past week. Maybe that explains why they have hit just .226/.285/.313 in their past six games, averaging 3.3 runs per game. They hit .344/.392/.497 in their first eight games, averaging 7.0 runs per game. You knew the Phillies' offense wouldn't keep up the pace it set the first week of the season, but Manuel certainly hopes the last six games aren't more indicative of the way it will play this season.
But just like the offense has leveled off, so has the pitching staff. Phillies starters were 4-2 with a 5.53 ERA in their first eight games, but are 3-1 with a 2.25 ERA in their last six. The relievers were 2-0 with a 1.57 ERA in their first eight games, but are 1-1 with a 4.00 ERA in their last six.
Chase Utley continued his running drills before the game. Not that I'm counting, but when he ran last week in DC, he ran twice around the outside of the infield. He took three trips around the infield today. I guess that's progress?
The 700 Level has a good interview with Mike Schmidt. Check it out.
Check out my Facebook page. Follow me on Twitter. Looking for a Phillies book to kick off the season? Try this one.
No Surprise: Rotation Remains In Order
The Phillies postponed last night's game because of rain. It turned out to be a good decision, considering I couldn't see outside my windows because of the storm. But within seconds of the announcement I got a ton of questions from folks wondering if the Phillies planned to skip Joe Blanton in the rotation.
My first reaction: Why would they do that?
First, the obvious. Roy Oswalt had to leave Friday's game because of a strained lower back. How much sense would it make to skip Blanton not knowing if Oswalt will pitch Wednesday? It very clearly makes more sense to keep everybody in order, which gives the Phillies an extra day to make a decision on Oswalt (and Oswalt an extra day to rest his back).
Second, Blanton has struggled in just two starts. Two. They need him to make 30-plus starts this season, so it makes absolutely no sense to skip him in April. Plus, what message does that send Blanton? Uh, Joe, we know it's April and we know it's just two starts, but we're skipping you to keep Roy Halladay on his normal day. I know fans with tickets to Monday wanted to see Halladay pitch, but the Phillies are looking at the big picture. Keeping everybody in order in April might keep everybody a little fresher come October.
So the Phillies' rotation looks like this:
- Today vs. Marlins: Cole Hamels
- Monday vs. Brewers: Joe Blanton
- Tuesday vs. Brewers: Roy Halladay
- Wednesday vs. Brewers: Cliff Lee
- Thursday at San Diego: Roy Oswalt
Like postponing last night's game, they made the right (and only) call.
Check out my Facebook page. Follow me on Twitter. Looking for a Phillies book to kick off the season? Try this one.
Phantastic Phour
Here is a very cool shot from The New York Times Magazine piece that ran today.
The Phillies released right-hander Matt Anderson from Minor League camp today.
Check out my Facebook page. Follow me on Twitter. Looking for a Phillies book to kick off the season? Try this one.
The Rotation
Here is a look at the Sports Illustrated cover of the Phillies' rotation. Gary Smith wrote the story. Should be great.
I've got a new Facebook page. Check it out here. Follow me on Twitter.
March 2-6 Pitching Schedule
Coming to Florida in the next week?
Here is the Phillies' pitching schedule through Sunday:
- Tuesday vs. Tigers: Cliff Lee, Kyle Kendrick, Eddie Bonine, Dan Meyer, Danys Baez and Mike Zagurski.
- Wednesday vs. Orioles: Roy Oswalt, Vance Worley, Brian Bass, Justin De Fratus, David Herndon and Brad Lidge.
- Wednesday vs. Blue Jays ("B" game at Carpenter Complex): Drew Naylor, Ryan Feierabend, Jose Contreras, Ryan Madson and J.C. Romero.
- Thursday @ Red Sox: Cole Hamels, Scott Mathieson, Brian Schlitter, Michael Stutes, Juan Perez and Michael Schwimer.
- Friday @ Pirates: Joe Blanton, Baez, Meyer, Andrew Carpenter, Zagurski and Antonio Bastardo.
- Saturday vs. Pirates: Roy Halladay, J.C. Ramirez, Madson, Contreras and Lidge.
- Sunday @ Tigers: Kendrick, Herndon, Stutes, Perez, Schlitter and Schwimer.
- Sunday vs. Rays: Lee, Bonine, Romero, Mathieson and De Fratus.
I've got a new Facebook page, where I'll post stories, blogs, thoughts, etc. Check it out here. Follow me on Twitter.
Manuel Announces NLCS Rotation
Charlie Manuel announced this morning on WIP his rotation for the National League Championship Series:
Game 1: Roy Halladay.
Game 2: Roy Oswalt.
Game 3: Cole Hamels.
Game 4: Joe Blanton.
Game 5: Roy Halladay.
Game 6: Roy Oswalt.
Game 7: Cole Hamels.
Nothing surprising there. I thought maybe they might flip Oswalt and Hamels, but it works either way. I figured Blanton would start — Manuel said they might go with Halladay, but only if they're down in the series — because it only makes sense to have Halladay, Oswalt and Hamels on normal rest in Games 5, 6 and 7. The Phillies have never pitched anybody on short rest in postseasons past, so why would they start now with their best rotation? Pitching should not be the issue this series. If the Phillies hit a little bit, they should win.
The Zo Zone is on Facebook and Twitter. My Phillies book "The Good, The Bad, & The Ugly" is available online, and at Delaware Valley bookstores!
Manuel Keeps Quiet on Big Roy, Rotation, NLCS Roster
Charlie Manuel talked to reporters for 15 minutes before today's simulated game at Citizens Bank Park.
Here's a synopsis:
He said they have not decided on their rotation for the NLCS. Manuel wouldn't even say Roy Halladay would pitch Game 1. Seriously.
"You can assume whatever you want to assume," Manuel said. "I'm not trying to be cute."
But then Manuel finally tipped his hand.
"Big Roy is Big Roy," he said.
Halladay will pitch Game 1. Not sure about the rest. Manuel said they definitely could pitch Joe Blanton in Game 4, which would allow the Phillies to pitch Halladay, Roy Oswalt and Cole Hamels on normal rest in Games 5, 6 and 7.
Manuel also said they have decided nothing on their NLCS roster, although they would carry 11 pitchers instead of 10 if they decide Blanton will start Game 4.
The Zo Zone is on Facebook and Twitter. My Phillies book "The Good, The Bad, & The Ugly" is available online, and at Delaware Valley bookstores!
Phillies Set NLDS Rotation
The Phillies announced their rotation for the National League Division Series, which begins Wednesday at Citizens Bank Park.
Game 1: Roy Halladay.
Game 2 on Oct. 8: Roy Oswalt.
Game 3 on Oct 10: Cole Hamels.
Game 4 presumably would be Halladay and Game 5 presumably would be Oswalt. The Phillies likely will be playing the National League Central champion Cincinnati Reds because they have a worse record the San Francisco Giants, who lead the NL West.
Halladay is the obvious choice to start Game 1.
"In my opinion, he's the Cy Young award winner and there's only one of those," Rich Dubee said before tonight's game against the Braves at Turner Field. "I think that would be the guy you would want to start. He is 21-10. I think his numbers are probably loftier than the other two guys, even though since Roy Oswalt's been here he's been unbelievable."
The Phillies want Oswalt to start Game 2 at home because he is 9-0 with a 2.10 ERA in 10 career starts at the Bank. He also is 23-3 with a 2.81 ERA in 34 appearances (32) starts against the Reds. Hamels makes sense for Game 3 at Great American Ball Park because he is 3-0 with a 1.67 ERA in four starts there.
"We like him at our place," Dubee said of Oswalt. "We like him everywhere, but more so at our place where he's pitched some pretty good games."
A popular school of thought is to split up the two right-handers with the left-handed Hamels, but Halladay and Oswalt are no ordinary right-handed pitchers.
"Halladay is who he is. Oswalt is who he is," Dubee said. "They're different. If you're splitting up guys who are alike, then you might want to. Those guys are different."
Charlie Manuel provided a bit of a scare for fans today, when he cryptically said he is concerned about Hamels. Dubee tried to douse those concerns when he said Hamels has been battling a head cold and nothing more.
"That's damn good," said Dubee, asked about Hamels' left arm. "He just threw right now. He looked real good."
The Zo Zone is on Facebook and Twitter. My Phillies book "The Good, The Bad, & The Ugly" is available online, and at Delaware Valley bookstores!
Phillies Shuffle Rotation Again
The Phillies have shuffled their rotation again.
They will have Joe Blanton, Kyle Kendrick and Cole Hamels face the Mets this weekend at Citizens Bank Park. Roy Halladay, Roy Oswalt and Blanton will face the Washington Nationals next week at Nationals Park.
Hamels, Halladay and Oswalt could pitch Oct. 1-3 against the Atlanta Braves at Turner Field.
The Phillies originally had Blanton, Hamels and Halladay scheduled to pitch against the Mets. Having Kendrick pitch Saturday allows the Phillies to give Hamels, Halladay and Oswalt and extra day of rest. If the Phillies clinch the NL East before the Oct. 1-3 series in Atlanta, they could have the Big Three pitch just a few innings to give them extra rest before the playoffs or bump one of them for Kendrick.
The Zo Zone is on Facebook and Twitter. My Phillies book "The Good, The Bad, & The Ugly" is available online, and at Delaware Valley bookstores!
Best NL Record Would Benefit Phillies in NLDS
The Phillies have a half-game lead over the Braves in the NL East and the best record in the league.
They obviously have plenty of incentive to win their fourth consecutive division title and finish with the best record in the league — and it has more to do that just having home-field advantage throughout the playoffs.
Because of scheduling reasons, the top NL team this year has the option of playing an eight-day NLDS or a seven-day NLDS.
An eight-day NLDS would have a clear benefit to the Phillies.
If they play an eight-day NLDS, they would have to pitch only Roy Halladay, Roy Oswalt and Cole Hamels — and none of them would have to pitch on short rest. If they played a seven-day NLDS, they would have to use a fourth starter (Joe Blanton) or pitch Halladay (or whomever starts Game 1) on short rest in Game 4.
Running Halladay, Hamels and Oswalt out there in a five-game series certainly is enticing for the Phillies.
"It obviously sets us up for success, but I think more excitement," Hamels said. "It sets us up for excitement because we have guys that have the experience or ultimately some of the best pitchers in the game. I think that's how you have to look at it. We obviously busted our tail to get there, but we have the guys to finish the series.
"That's what makes it uncomfortable for the opposing team. To come in, not look at anybody and go, 'We can probably get a couple runs out of this guy,' or, 'This guy we might as well just hang it up.' That's kind of what we have. When the postseason comes it's about three guys that go. We definitely do have those good three guys and we have an unbelievably good fourth guy. But you do have to get there."
Here's a look at how an eight-day series differs from a seven-day series:


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Oil and other energy products are closing sharply lower in the wake of the presidential election as traders turn their focus back to the problems facing Europe and the U.S.
The prices of copper and other metals also fell. Agricultural commodities were mixed.
Crude oil plunged to its lowest point since July after European leaders slashed their forecasts for economic growth next year.
In the U.S., investors worried that a divided government in Washington may not be able to avoid the deep spending cuts and tax hikes set to take place in January unless Congress acts.
Crude oil dropped $4.27 to finish at $84.44 a barrel, a loss of nearly 5 percent. Wholesale gasoline fell 4 percent and heating oil fell 3 percent.


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Poverty did not influence mortality after ICU stays
Jun 252011
Zager et al examined registry data on 38,917 patients admitted to Beth Israel and Brigham & Women's hospitals between 1997-2007, and concluded that low socioeconomic status (as determined by census tract neighborhood of residence) did not influence 30- or 365-day mortality after admission to these hospitals' ICU. CHEST 2011;139:1368-1379.
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Flex-E-Grant Application Materials
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Congressman Jared Polis to Return as Keynote Speaker at the Second Annual Las Vegas EB-5 Conference – January 17, 2015
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