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SC19 Student Cluster Competition: Know Your Teams!

November 21, 2019 · Dan Olds

I’m typing this live from Denver, the location of the 2019 Student Cluster Competition… and, oh yeah, the annual SC conference too. The attendance this year should be north of 13,000 people, with the majority attendees here to observe the cluster competition, wager on the results, and cheer their favorite team on to victory. And maybe take a look at the show floor too.

So, without further ado, here are the teams competing for cluster competition glory at SC19.

ETH Zurich: This team came out of the mountains of Switzerland and laid the smack down at ISC19, taking home the Highest LINPACK award and finishing third overall. This is almost unheard of for a rookie team and it makes us wonder what they have in store for SC19. Their coach, Hussien Hadrake, is such a hard driving taskmaster that he would make a Marine Drill Instructor proud. Check out the movie Full Metal Jacket for comparison.

FAU: This is a Germany team that has competed in a total of nine international competitions in China, Europe, and the US. FAU, which is short for Friedrich Alexander Universitat, is best known for their two LINPACK wins and a Bronze medal earned at ISC competitions. In recent years, they’ve finished just short of the upper echelon, but some cluster competition observers think that FAU is ready to make a move on the top players – and that it could be this year.

Nanyang Technological University: I’ve dubbed Team Nanyang “The Pride of Singapore” for good reason. They’ve competed in a total of eleven major tournaments and have had their share of wins. When Nanyang wins , they win in bunches, taking home both the Gold Medal and LINPACK Award at SC17 and Silver and LINPACK at SC18. Definitely a team to have on your short list if you’re wagering on the competition.

NTHU: National Tsing Hua University is a perennial competitor, having participated in an amazing 17 international bouts – including the very first cluster competition held at SC07 in Reno (also known as the “Great Black Out”). Along the way, this team has earned three Gold Medals, a Silver, a Bronze, and an incredible four LINPACK Awards. That’s quite the record. Their most recent Gold win was at ASC19 in Dalian, China. A note for aficionados, NTHU was one of the first teams to utilize GPUs in their cluster at SC11 in Seattle and they rode them to victory and a Gold Medal.

North Carolina State University: One of the first time competitors and, now that I think of it, the first entrant from the Research Triangle Park. We don’t know much about their cluster game, but we do know that there are more Ph.D’s in their neck of the woods than anywhere else in the US. You can’t swing a cat without hitting a Ph.D in fact, which makes it a pretty interesting place (but also unpleasant for both cats, and Ph.Ds). We’ll take a run with the wolf pack and get your more details soon.

Peking University: This is one of the best universities in the world, ranked #41 worldwide by US News and World report and #1 in Asia. Along with all of their academic smarts, they’re also developing a cluster competition game. They’ve participated in four previous competitions, most recently in the big stadium at ASC19. While this team hasn’t scored any major awards yet, they definitely have the brain power and dedication to leave a mark on SC19.

Purdue University: They’ve competed a grand total of 14 times, primarily at SC, but also in Europe at ISC and in China at ASC. Team Boilermaker hasn’t taken home any trophies as yet, but they’ve certainly had their times at bat. Maybe this is the year that the Boilermakers will rise up and grab some cluster competition glory. We’ll keep an eye on them.

Shanghai Jiao Tong University: This is another one of the powerhouse Chinese teams and is getting closer and closer to their Gold Medal. They’ve competed at nine international competitions, taking home two Silver Medals and a Bronze. They seem to always be in the hunt for gold, but not quite making it over the hump. What’s interesting about this team is that their coach is a former four year competitor who intimately knows the ins and outs of the cluster game. The smart money is looking for Jiao Tong to make a move and snare a major award in the next few competitions.

ShanghaiTech University: Up and coming team from China, this is their third competition since their debut at ASC18. They pulled off a surprising second place finish, earning them a place in the ISC18 competition in Frankfurt. This is definitely a skilled team and has the potential to shock the cluster world again at SC19.

Team Tennessee: This team is a combination of students from three different universities: University of Tennessee, Pellissippi State Community and Maryville College. University of Tennessee is a return competitor while Pellissippi and Maryville are brand new. Highly renown Oak Ridge National Laboratory is a team sponsor and, assumedly, is playing a role in the coaching of these students. Having Oak Ridge in as your corner is certainly a plus and could be the crucial element that puts this team into the upper echelon.

Tsinghua University: Tsinghua is the most heavily decorated team in clusterdom, hands down. They’ve competed in more events than anyone else (19) and taken home the most medals (eight Gold, three Silver and three Bronze). They are the only team to complete the Triple Crown of student cluster, winning all three major competitions in a single year – and they did it TWICE. These are truly steely eyed cluster warriors and the team to beat at any competition. However, they aren’t perfect. They finished second at both ASC19 and ISC19, which had some cluster competition observers wondering if their dominance has come to an end. But the smart money is on Tsinghua to be on the podium when the smoke clears.

University of Illinois Urbana-Champaign: This is a team that’s ready to move to the top tier of cluster competitors. They’ve gained experience with their three previous appearances, they have the hardware, and they have the coaching and advisors to put together a winning effort at SC19. The team has had a taste of victory with their third place finish at SC17 and is hungry for more.

University of Tartu: These Estonians have traveled far and wide to compete at cluster events. They’ve made the trip to China once and have competed at Germany’s ISC three times. They were the team involved in the infamous “Sunday, Bloody Sunday” incident at ISC15. In fact, the coach of the current Tartu team was a team member who cut his hand making cable adapters for their cluster. Even though he was bleeding profusely, he refused medical attention until he had finished the cables. This team has that kind of spirit in spades and although they haven’t won the big prizes (yet).

University of Warsaw: The plucky Polish team has fast become a band of veterans with seven cluster competitions under their collective belts. They’ve traveled to Asia, competed in Europe and visited the SC competition three times. They’ve experimented with exotic interconnects and have fought back against hardware problems. But more importantly, the team has pushed hard to improve with every opportunity – and that’s the point of this stuff, right?

University of Washington: This is the first cluster competition from the Pac-12 Huskies. Their football team is having a mediocre season, so the school’s reputation is riding on the performance of their Student Cluster Competition team in Denver – which is the most important collegiate sporting event in the world. We don’t know a lot about this team but considering they’re from the tech-heavy Seattle area, they’ve probably had some high-quality coaching. Maybe there’s some Amazon or Microsoft support in the background

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, who knows? I’ll do my best to find out.

Wake Forest University: Second time up for the Deamon Deacon team from Wake Forest. They had some first-time jitters last year plus the hardware/software gremlins that are typical for a rookie team. But all of that is behind them and it’s time to begin anew. You can’t underestimate the value of experience in these competitions and now that the Deacons have a year under their belts, the team will see considerable improvement this year.

Now that we’ve covered the teams, next up we’ll take a look at the grueling applications and tasks they have before them…Stay tuned…..

Radio Free HPC SC19 Cluster Competition Preview

November 17, 2019 · Dan Olds

The surprisingly popular podcast, Radio Free HPC, takes an in-depth look at the upcoming SC19 Student Cluster Competition. Dan Olds breaks down the teams and former cluster competitor Jessi Lanum provides color commentary while the rest of the guys comment and discuss. Click here with your clicker to listen to the episode.

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ISC’19 Over Performers: Getting the Most out of the Hardware

August 15, 2019 · Dan Olds

I’ve been in the Student Cluster Competition game for a long time. I started following and writing about these competitions back in 2010 and have been working to spread the word ever since then. These are compelling and exciting competitions, no doubt about it.

But every time I see the scores from an event, I notice what seem like anomalies. I see teams that are achieving better scores than it seems like their hardware should support. For example, a team getting a better LINPACK or HPCG score than teams with many more GPUs.

To me, this is an example of a team that did an outstanding job of system and application optimization. The problem is that these teams often don’t post a score high enough to finish in the top three or four and get recognized for their effort. So the question is how to quantify these situations and recognize the teams for their accomplishments? That question has been bothering me for eight years. But I think I have a solution.

It’s All Relative

I burned up a lot of spreadsheets trying to figure out how to best highlight the teams that were punching above their weight when you compare their hardware to their application and benchmark scores. The problem is that I wasn’t looking at the problem correctly. I was trying to come up with a single objective measure of optimization/tuning performance, but as it turns out, the answer is in looking at the relative hardware configurations and benchmark/apps scores. That was the “ah ha!” moment.

From there, it was a feverish several hours of spreadsheeting and then many days of checking my algorithm and logic with people who know what they’re talking about. After all of that, I finally have a model that, I believe, does the best job of using the data I have to show which teams did the best job of getting the most out of their hardware.

Relative Cluster Power vs. App Performance

First, I needed to figure out the relative performance potential of each team’s cluster. I came up with a relatively simple method of normalizing six machine configuration metrics: Total CPU Cores, CPU Frequency, Total Memory, Interconnect Speed, GPU Cores, and GPU memory.

I didn’t try to add any weights to these factors because their relative value will be different for every workload and benchmark. So I simply normalized each individual component, then added up the scores and normalized every machine to the highest score. This is what I’m calling the “Machine Score.”

On the application and benchmark side , I normalized the scores to the highest score in each category and then ranked them. This is what I’m calling the “Application Score.”

The Efficiency Score

The combination of the Machine Score and the Application Score yields the Efficiency Score for each team on each application. If the Efficiency Score is more than 1.0, then that team has over-performed their hardware. As we’ll see in the tables below, there are several teams that significantly out-performed their clusters, even though they might not have had a high enough score to land in the top three or four slots overall. Let’s take a look….

On the P-TRANS section of the HPCC benchmark, University of Hamburg had what we figured was the 10^th best cluster in the competition but they managed to finish third overall on P-TRANS, giving them an efficiency score of 132%. In other words, they got 32% more performance out of their machine than other teams.

Likewise, ETH Zurich had the ninth best cluster in the competition, but grabbed second place on P-TRANS, yielding an efficiency score of 116%. CHPC also did a great job on optimizing and tuning for this benchmark, earning a 113% efficiency score.

These teams really excelled on this application when you consider that the average efficiency was only 50%.

We really saw some surprises when we ran the numbers for CP2K. Taiwan’s National Cheng Kung University (NCKU), with their dual workstation cluster, had only the 13^th best system in the competition. However, they finished fourth on CP2K, making them legends in their own time with an efficiency rating of 168%. Astounding.

University of Warsaw, aka the Warsaw Warriors, had the 11^th best cluster, but pulled down fifth place, giving them an efficiency score of 138%. The Arm aficionados from UPC also did a great job of tuning and earned an efficiency score of 137%. The other team from Taiwan, NTHU, turned in yet another great CP2K efficiency score of 131%.

NCKU was on a tear, tackling the AI Challenge and turning in a second-place finish with an efficiency score of 157%. This was one application that didn’t require a lot of raw power, since students were scored by the accuracy of their result – not the speed of their finish.

ETH Zurich took home first place on the AI application and also scored very high on the efficiency scale with their 132% number. Heidelberg was also very efficient, using their 10^th ranked cluster to nail down a sixth-place finish. Likewise, Nanyang took a 7^th ranked machine and pushed it to a third-place finish on this app.

The Pride of Arm, Team UPC from Spain, took their 14^th ranked system and drove it to an OpenFOAM eighth place finish. While this wasn’t enough to get them on the podium for this application, their efficiency score of 140% is remarkable, particularly given that OpenFOAM wasn’t exactly designed to run on Arm processors.

ETH Zurich and CHPC also did a great job of optimizing OpenFOAM on their clusters, earning efficiency scores of 117% and 113% respectively. This is quite a bit above the field average of 74% efficiency on this application, great job.

Team UPC turned in the highest efficiency score in the competition by finishing eighth on PENNANT. This gave them an amazing efficiency score of 179%…wow…that’s scary high.

University of Heidelberg tuned their 10^th ranked cluster to a second-place finish on PENNANT

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, giving them an efficiency score of 142%, which is also quite good. CHPC and Sun Yat-Sen pulled down efficiency scores of 113% and 112%, but it’s harder to get high efficiency scores when your Machine Score is also high – a team has to do a lot better on the application in order to get a modestly higher efficiency score.

Student Cluster Competitions are about more than just assembling the most hardware possible and brute forcing your way through them. Tuning and workload optimization play a big role in these competitions and I think we’ve finally found a way to quantify these factors. The efficiency score lets us recognize the achievements of these student teams who did a fantastic job on a difficult slate of applications.

If you have any thoughts about how to rate cluster competition efficiency or any ideas about how to make it a better measure, shoot me an email me at: dan.olds@orionx.net

To see how the competition ended, take a look at the awards ceremony video.

If you’d like to see the worldwide rankings of all Student Cluster Competition teams, check out the Student Cluster Competition Leadership List.

ISC’19 Finale: Day-By-Day Thrill Ride

June 28, 2019 · Dan Olds

The ISC19 Student Cluster Competition in Frankfurt, Germany had one of the closest and most exciting finishes in cluster competition history. The overall winner was decided by just over two percentage points and the margin between third and fourth place was less than a single percentage point.

Let’s go through a day-by-day summary of the action and see who won and how.

Day 1: Benchmarks

The first day saw the competitors run LINPACK, HPCC, and HPCG. Only HPCC and HPCG count towards the overall championship score, while their LINPACK score is used to determine the Highest LINPACK Award winner.

Tsinghua University carved out an early lead by notching a perfect two-for-two on HPCG and HPCC. Each benchmark is worth a maximum of 10 points , so Tsinghua takes 20 points on the first day.

Under the ISC scoring system, the winner takes all the possible points for each task, with the other teams getting proportional points based on how well they did vs. the winning score.

Tsinghua narrowly won HPCG, topping Nanyang Tech by less than four percentage points. They built on their HPCG success with their field leading HPCC score, which topped Nanyang by 6%.
CHPC hung in there by taking third place, while Sun Yat-Sen took fourth place. In terms of points, Tsinghua takes Day 1 with 20 points, with Nanyang fractionally behind.

Day 2: Applications Flip the Script

On Day 2, the competitors stared down their first set of HPC applications. The menu for the day was CP2K, Swift, and PENNANT.

Tsinghua held on to their lead by turning in solid scores on all the applications. They nabbed second place on CP2K, third on Swift, and fifth on PENNANT.

The real story on Day 2 was the moves made by CHPC and NTHU. While CHPC only scored seventh on CH2K, it didn’t hurt them much because this app was only worth 10% of their overall competition score. They made up for it with a second place on Swift (10%) and a first-place finish on PENNANT – which counts for 15% in the overall score.

NTHU earned a full ten-point winners share on CP2K, which gave them some momentum. They followed it up by winning Swift, giving them an additional ten points. They faded a bit on PENNANT, only taking down sixth place. Nanyang and Sun Yat-Sen were deadlocked in fourth place, with only five one hundredths of a point separating them.

While it might seem like Tsinghua and CHPC have insurmountable leads, we need to realize that only just over half of the possible points have been handed out so far – there are still 45 potential points to be earned. In short, it’s still anyone’s game, although the teams with scores under 40 have their work cut out for them.

Day 3: The Thrilling Finish

As the sun rose on Day 3, the entire city was tense – knowing that the results of the ISC19 cluster competition would be revealed that afternoon. The teams had no idea of where they stood on the leaderboard

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, so they clustered hard to put up as many points as possible.

Day 3 was a big day. Between the OpenFOAM application, the AI Challenge, and the interview there were 45 points still to be awarded. Anything could happen. In past competitions, we’ve seen leaders stumble and we’ve also seen teams lagging put together a streak that lands them on the podium. That’s exactly what we’ve seen at ISC19.

Tsinghua went into Day 3 with a narrow lead over CHPC. They lost some of their margin when CHPC won OpenFOAM, earning 10 points to Tsinghua’s 9.75 points. At this point, the two teams were nearly tied with Tsinghua (at 58.16%) barely holding off CHPC (with 57.28%). Meanwhile, some of the leaders from Day 2 were showing vulnerabilities with Nanyang, NTHU, and Sun Yat-Sen not posting big scores on OpenFOAM.

The big application of the day, the AI Challenge, was worth a whopping 25 points and would decide the competition. Whoever took the most points on it would probably rule the day.

ETH Zurich parlayed a first-place finish on the AI application into a third-place overall finish – coming out of nowhere to take home the trophy. It’s going to look nice next to their Highest LINPACK Award hardware.

The second-place finisher on the AI Challenge was shocking. Taiwan’s NCKU, with their tiny dual-workstation cluster, amazed everyone by snatching second out of the jaws of their better-equipped rivals. Score one for the underdogs, great job!

Nanyang took third place on the AI app, adding a little over 21 points to their total. EPCC finished fourth, while Sun Yat-Sen took fifth, which was enough to give them the fourth-place finish in the overall competition.

CHPC sealed their win by grabbing the seventh-place position and 20.64 points vs. Tsinghua’s ninth-place finish, which only earned them just over 17 points. While Tsinghua scored a half point better than CHPC on the 10-point interview portion of the competition, it wasn’t quite enough to allow them to overtake CHPC for the win.

With this win, CHPC has now taken home the ISC Overall Championship an astounding four times, pulling even with Tsinghua in number of ISC championships. CHPC also maintained their record of reaching the podium in each of their seven cluster competition outings.

Tsinghua adds another Silver trophy to their overcrowded trophy case and newcomer ETH Zurich can now place two cluster competition trophies (LINPACK and Bronze) on their shelf.

If you’d like to see the awards announcement, check out the lavish two-camera coverage of the ISC’19 Cluster Competition Awards Ceremony below.

Whew! What a competition! We’re almost finished with our coverage of the ISC19 Student Cluster Competition. We will be rolling out a new analysis of how the teams fared on tuning and optimization in our final article.

If you want to see how our student teams rank in worldwide cluster competitions, take a look at the new Student Cluster Competition Leadership List.

ISC’19 Kluster Komp: App Scores Revealed!

June 27, 2019 · Dan Olds

Our exhaustive coverage of the ISC19 Student Cluster Competition continues as we discuss the application scores below. While the scores were typically high, some of the apps, like SWIFT and OpenFOAM, really pushed the students to the edge, judging by the average and median scores.

Here are the final results for the HPC application portion of the competition:

CP2K: This is a quantum chemistry and solid state physics application that can perform atomistic simulations of solids, liquid, molecular, periodic, material, crystal, and biological systems. You want to use Gaussian or plane wave approaches? Go for it. CP2K is like a Swiss Army knife of figuring out the physics behind materials and stuff.

Taiwan’s NTHU nailed the top score, with Tsinghua only four points behind. Sun Yat-Sen took third with a score of 92.11%. National Cheng Kung University from Taiwan came out of no where to take the fourth place slot with Warsaw right behind them to take fifth – great job!

SWIFT: You wake up one morning and realize that you’d like to do some modeling on how gravity and hydrodynamics affect materials. A good example is if you want to see what happens when you drop a cow from a helicopter into a lake. What do you do? You get SWIFT.

While our students weren’t modeling a cow & helicopter scenario, they did have to run SWIFT on their clusters and run SWIFT they did.

NTHU topped the field with their 100% normalized score, showing that they were the swiftest to solution on this app. CHPC was eating NTHU’s dust with their score of 81.49% and Tsinghua was even farther behind with their 72.18% mark. However, this was a difficult application for all of the teams, as shown by the low 22.72% median score.

Mystery Application: The Mystery Application for the ISC19 Student Cluster Competition was the PENNANT application from Los Alamos National Lab. It’s an app that helps find more efficient implementations of unstructured mesh physics on different architectures (like GPUs, for example).

CHPC took the flag on PENNANT (I hate puns, but will stoop to them when I can’t think of anything else) narrowly finishing ahead of Heidelberg, who was less than four points behind. However, Heidelberg barely held off Sun Yat-Sen, who was only two points behind. ETH Zurich and Tsinghua put forth the effort , but the PENNANT gods did not smile upon them. Most of the other teams had a reasonably good time with PENNANT, judging by the median score of 75.62% and average score of nearly 66%.

OpenFOAM is a free open source computational fluid dynamics package that does about anything you’d want to do. Need to work with incompressible flows? Incomprehensible flows? Compressible flows? Or even analyze foam? OpenFOAM is your answer. Same thing for conjugate heat transfers and combustion problems. Hell, it even has Direct Simulation Monte Carlo solvers. What more could you ask for?

Our student teams felt that OpenFOAM was a pretty difficult piece of code to optimize. There are a lot of levers, toggles, and knobs in the software. The plucky South African team from CHPC set a blistering pace with their 100% normalized score on OpenFOAM, but the kids from Team Tsinghua weren’t fare behind at 97.49%. EPCC Edinburgh gets into the top three with a 90% score, while the pride of Switzerland, ETH Zurich, and China’s Sun Yat-Sen crossed the line with nearly identical scores.

AI Application: The AI application this year deals with extremes in weather and has students using TensorFlow and Horovod to train models that are highly accurate when inferencing through the provided datasets.

ETH Zurich’s AI model produced the highest accuracy and earned them 100% on this exercise. Taiwan’s National Chung Keng University grabbed a surprise second place finish with their economical dual-workstation cluster, showing they can beat the big clusterers when it comes to an application that doesn’t demand a lot of raw power.

Nanyang Tech took home third place, sandwiched in between NCKU in second and EPCC Edinburgh in fourth. Sun Yat-Sen nailed down a honorable mention, finishing just behind the leaders.

Interview: The final scored portion of the competition is the interview. This is where HPC experts visit each team and ask piercing questions like “what make you decide on this particular configuration?” and “how did you optimize _____ application?” or “what was your speed up on _______ as compared to your original run?” Teams are really put on the spot as they try to answer a wide variety of questions that cover every part of their competition preparation, execution, and results. The Student Cluster Competition Interview is sort of an art form. Students don’t know what’s going to be asked of them and they don’t know the interview style of individual judges, so they have to be prepared for anything. On top of all this, there can also be language barriers that hinder clear communication.

New team ETH Zurich barely managed to edge out Tsinghua for the top interview score. This is quite a feat, as Tsinghua is typically very strong on their interviews, offering up all sorts of relevant data to the judges. The team from Spain, UPC, did their usual good job with the judges, explaining in detail their system choices and how they took on the apps.

EPCC Edinburgh and CHPC also turned in well above average interview scores.

Our coverage continues with our next article which will show the day-by-day drama in the competition and reveal the winner and top finishers. Finally, for the first time ever, we’re going to show the results of our own score analysis which, we believe, will show the teams that exceled in turning and optimization apart from their hardware configurations.

To check out how the teams stand in all-time competitions, be sure to take a gander at the Student Cluster Competition Leadership List.

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ISC19 Student Cluster Competition: LINs Packed & Conjugates Gradient-ed

June 27, 2019 · Dan Olds

The benchmark results from the recently concluded ISC19 Student Cluster Competition have been compiled, sliced, diced, and analyzed senseless. As you cluster comp fanatics know, this year the student teams are required to run LINPACK, HPCG, and HPCC as part of the ISC19 competition. The LINPACK run is to qualify for the Highest LINPACK award, but doesn’t otherwise contribute to the team final scores. HPCG account for 10% of their overall scores as does HPCC. In this article we’ll be looking at the LINPACK and HPCG results, we’ll cover detailed HPCC outcomes in our next story.

As can be seen by the chart, newcomer ETH Zurich took home the Highest LINPACK trophy, which is quite a feather in their Swiss hats. Teams seldom win a major award in their first outing. Sun Yat-Sen was a close second with their score of 37.83 Tflop/s, which isn’t unusual for them – they’ve won two LINPACK awards in previous competitions.

MGHPCC (Team Boston) took home third by tallying 35.67 Tflop/s on their LINPACK run. This was the team I was betting on to take the LINPACK competition and perhaps set a new student HPL record. They had the perfect configuration for a record LINPACK run: a slim dual-node config, 16 NVIDIA V100 32GB GPUs, and a whopping 1.4 TB of memory. But, alas, it wasn’t to be….the team had some problems with their interconnect and couldn’t get everything cooking in time to turn in a huge score.

CHPC and Edinburgh get honorable mentions for their LINPACK scores, finishing well above the average and median scores of the field.
Typically, the team with the most GPUs wins LINPACK, but that wasn’t the case this time. ETH Zurich only sported eight V100s to accompany their four nodes and 1.5 TB of memory. By our calculations, Zurich had the ninth most powerful configuration in the competition, but still managed to come out at the top of the LINPACK pack.

While Tsinghua didn’t manage to chart on LINPACK , they came back to lead the field on HPCG with a score of 1.8 Tflop/s. Even though they had what we evaluated as the most powerful cluster in the competition, they still didn’t get too close to the record of 2080, established by, guess who, Tsinghua at an ASC competition.

Nanyang Tech turned in a highly competitive 1,810.89 GFlop/s result, which put them pretty close to Tsinghua’s winning score, but just not quite enough to get over that mountain. Taiwan’s NTHU and South Africa’s CHPC take home third place and honorable mention respectively.

It’s interesting to note that both the LINPACK and HPCG scores are lower than in previous competitions. LINPACK in particular looks like it has fallen off a cliff, down by 17 Tflop/s from the record of 56.51 set at SC18. It doesn’t look to me like the hardware is the issue, there were plenty of teams that had enough gear to mount a serious challenge to the record.

Next up

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, we’ll take a deep dive into the HPC Challenge benchmark – it’s eight benchmarks in one, the data from which should fulfill anyone’s quant desires. Stay tuned!

ISC’19 Kluster Komp: HPCC Deeeeep Dive

June 26, 2019 · Dan Olds

The biggest benchmark the student warriors tackled during the ISC19 Student Cluster Competition was the colossal HPC Challenge. This is a collection of benchmarks that has a little something for everyone from memory bandwidth fans to those who can’t get enough raw number crunching. The HPC Challenge benchmark is run as a single job. However, there are some limited ways to optimize and tune the benchmarks before you run them.

We consulted a long-time HPC expert who bills himself as a “former slimy benchmarker” who knows the ins and outs of benchmarking and has used them for both good and evil. When contacted, he said that students have to intimately know the Three Rules of Benchmarking:

“The first rule of benchmarking is read the rules; the second rule of benchmarking is read the rules. And the third rule of benchmarking is to obey the first two rules.”

With that sage advice, let’s take a look at the detailed student scores for HPCC…

HPL is an old friend to most benchmarkers: they know it, they love it. Or at least know how to work it. While the students run LINPACK to qualify for the Highest LINPACK award, they run it again as part of the HPCC benchmark.

CHPC narrowly beat out EPCC for a win on HPL, with Nanyang coming in third place. The average and median scores for the field are kind of low, which is a bit of a surprise to me since the students have had so much practice on it.

P-TRANS is a benchmark that measures the rate at which a system can transpose a large matrix on its diagonal. Depending on the size of the matrix, this can be pretty demanding computationally. It’s possible, and within the HPCC rules, to use a linear algebra library, like BLAS, to optimize the process.

CHPC scored another win on P-TRANS with a score of 49.42 GB/s, well ahead of ETH Zurich’s score of 43.51. University of Hamburg makes their first appearance on the leaderboard with their third place finish. Tsinghua earns an honorable mention for their score of 35.76. All of our top finishers were way above the average and median scores for P-TRANS.

Random Access measures how quickly the system can access memory pages, loading page after page of memory. This one isn’t so much about tuning as it is about having a hardware set up that has good memory characteristics, such as fast DIMMs and low latency. It also helps to have a single DIMM per memory channel. Our former slimey benchmarker says: “Nothing they can do on this except set up for big pages, if they know how to do that – which they certainly should, in my opinion.”

Sun Yat-Sen schooled the rest of their field with their dominating score of 1.1 Gup/sec, which is a serious number of giga updates. Nanyang pulled second place with a score of .50 and Tsinghua was well back with .35 to take third. EPCC earns a mention because their score was well above the average and median scores, nice job.

FFTE (Fast Fournier Transform): FFTE is an algorithm that converts a signal, usually time or space, into a value in a frequency domain. FFTE is often used in engineering, science and mathematics. “Depending on the matrix size, a FFTE library could improve performance, but they don’t know the matrix size coming in, so too bad….”says our slimy benchmarker.

CHPC dominated FFTE by more than doubling the score of the second-place Sun Yat-Sen. EPCC Edinburgh got on the board with a distant third-place finish.

DEGMM: This is a benchmark that multiplies matrices, which is a lot of multiplication as it turns out. Our slimy bench marker says “you can’t do much on DEGMM and stay within the rules, but you can use different compilers and different options within the compilers to find the optimal set for their machine.”

It looks like Tsinghua did exactly that and it paid off. Their score of 2,691.93 was more than double that of second-place Nanyang Tech. Our buddies from UPC make the leaderboard with their 958 score, which is a pretty damned good result for a team that’s driving Arm processors. Great job.

STREAM is a memory bandwidth test. According to our slimy benchmarker, “…more and faster DIMMs are key here, and big pages will make a difference. Need to have a DIMM in every DIMM slot and a motherboard that can drive them.”

Tsinghua, a team that had the highest performance cluster in our evaluation, handily grabbed the STREAM crown by dominating the rest of the field with their score of 816 GB/s. Nanyang took second place with their score of 347.27. The Warsaw Warriors put themselves on the board with a third-place score of 240.74, despite driving a brand new architecture, the NEC Aurora vector system.

Random Ring Bandwidth is a test of MPI bandwidth that measures two cases of MPI bandwidth: 1) a non-simultaneous ping pong that tests MPI bandwidth with no contention

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, and 2) a simultaneous communication that uses random and ring patterns to measure bandwidth with MPI contention.

Random Ring Latency tests the latency of system communications using the same mechanisms as the bandwidth test.

In the bandwidth test, the higher the score, the better. In the latency test, lower is better.

In the ISC19 HPCC benchmark, Nanyang Tech is the Lord of the Rings, taking the top scores in both benchmarks. Tsinghua took second in the ring tests. The team from NCKU, which has the most rudimentary cluster in the competition (although it’s the best price-performer), grabbed third in both tests, putting them on the big board for the first time. EPCC took fourth in the bandwidth test while ETH Zurich took fourth on the latency test.

The overall scores tell the story as Tsinghua grabs the top slot with a 100% normalized score and adds a full 10 points to their competition tally. Nanyang grabs second place and 9.4 points. CHPC and Sun Yat-Sen finish a distant third and fourth , but still get their share of points.

While this is a mega-big benchmark, in the whole scheme of things it only counts for 10% of the total competition score – not enough to give anyone an insurmountable advantage or disadvantage. But it’s fun to look at the deep results and highlight the individual performance of the teams.

Quick plug: check out the Student Cluster Competition Leadership List and see where your favorite team ranks. This is a joint project between the HPC-AI Advisory Council and me and is the culmination of many years of painstaking tracking and research. The list shows every team to ever compete in a Student Cluster Competition and assigns points based on their participation and awards. There are four different cuts of the data, the first being a worldwide ranking, then separate rankings for EMEA, the Americas, and APAC. It will be updated after every competition and more features will be added over time.

In our next articles we’ll be looking at the HPC application scores and then looking at the day-by-day results to show you who won and how they won. Stay tuned….

ISC’19 Configurations: Less is More, GPUs Rule, Again

June 26, 2019 · Dan Olds

I’m constantly amazed by the how many different system configurations we see in Student Cluster Competitions. Given that everyone has to use hardware that’s currently available on the market and they all have to be under the 3,000 watt power cap, you’d think that the systems would gravitate towards a common configuration – but you’d be wrong.

Here’s what each team came up with for the ISC19 cluster competition:

Highlights:

Scotland’s EPCC has the largest configuration by node count with nine. At the other end of the scale, we see Warsaw, Hamburg, NCKU, and Team Boston weighing in with dual nodes. The Boston machine is particularly noteworthy – it has only 80 CPU cores but is sporting 1.4 TB memory and 16 32GB NVIDIA V100 GPUs. Now that’s a configuration that is aimed squarely at taking home the LINPACK trophy.
Hamburg and Warsaw are running NEC Aurora vector machines, which are Xeon-based servers equipped with eight specialized vector accelerators. These teams have done great work in simply getting the application to run on these brand-new architectures – kudos to them.
Chinese server vendor Inspur is sponsoring four teams at ISC19 – the most of any vendor. Their teams include Tsinghua
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, Taiwan’s National Tsing Hua, Sun Yat-Sen, and Heidelberg.
Node counts are down this year, as are total CPU cores. This is the continuation of a trend that started back in 2012 or so with the advent of GPUs. Students are using more of their power budget on accelerators and eschewing CPUs as more and more applications become GPU-enabled. The average number of GPUs in the 2019 field is a whopping three more than what we saw in 2018. That’s an increase of nearly 40%.
We also see a significant rise in system memory, moving from 1,109 TB to 1,367 TB, an increase of almost 25%.

So will small, GPU-packed configurations rule out? Or will we see the larger systems taking the awards? We’ll be revealing detailed results in our next posts, stay tuned.

ISC 2019 Student Cluster Competition: Meet Your Teams!

June 26, 2019 · Dan Olds

Finally! The videos have been rendered, the statistics compiled, and the story lines set. It’s time to share with you the incredible event that was the ISC 2019 Student Cluster Competition.

So what’s a Student Cluster Competition? It’s an event where teams of undergraduate students, representing their respective institutions, design, build, and tune real HPC clusters in order to see who can run real-world scientific benchmarks and applications the fastest. The only limitation on the students is related to power: there’s a hard cap of 3,000 watts.

The ISC19 competition has 14 teams from 11 countries vying for Gold, Silver and Bronze awards, plus separate awards for the Highest LINPACK score and Fan Favorite. We’re covering the entire competition like an afghan on your grandma.

So let’s meet the teams and get a feel for what they’re bringing to the table….

Team Boston: Also known as Team MGHPCC (Massachusetts Green High Performance Computing Center), Team Chowder, Team Bar Fight, etc., is a long-time competitor at international cluster competitions. They’ve participated in a total of 10 events over the years and hit every major competition in Asia (ASC), Europe (ISC) and the US (SC). While the team hasn’t yet been able to spoon their chowder out of the championship trophy, they did get a silver medal along the way. Check out the video below to learn more about the team…..

Team CHPC: This is the team sponsored by South Africa’s Centre for High Performance Computing. They’ve competed at six previous ISC events and grabbed an astounding three Gold medals, two Silver medals and a Bronze. In other words, every time they come to town, they leave with some shiny hardware. Impressive. The team is selected through a grueling intra-country cluster competition that winnowed down a field of 10 teams down to one. I was fortunate enough to attend and have documented the experience in my outstanding “Building the Team: South African Style” article. This year’s CHPC team was highly skilled, but also a very fun interview. You’ll see what I mean in the video below.

Team EPCC: Representing the Edinburgh Parallel Computing Centre, Team EPCC isn’t a stranger to cluster competitions. They’ve participated in four ISC tourneys, taking home a Bronze medal and a Highest LINPACK award. The team has assembled a cluster that received my “Why in the Hell Did You Do That?” award. In one nine node cluster, they have four different Xeon processors. It’s like they went to the EPCC processor drawer and grabbed everything that had the word “Xeon” on it. But the kids are making it work. Watch the video below to learn more about what they’re doing and why….

Team ETH: The pride of Switzerland, Team ETH Zurich is representing the Swiss National Supercomputing Centre. While this is a new team, they certainly don’t seem like cluster competition newbies. They had their system up and applications running way before most new teams and seemed totally comfortable and in command during the competition. Hussein Harake, HPC System Manager at CSCS, is coaching this team and he’s a stern taskmaster. I imagine he was running the team up and down Swiss mountains while yelling at them about the best ways to optimize HPCC. This team is hungry for success in Frankfurt, take a look at the video below and see what I mean…

Team Nanyang: These kids are from Nanyang Technological University in Singapore and they’re one of the teams to keep an eye on. They’ve competed in nine international competitions including three appearances at ASC, ISC and SC. They took home the Gold medal at SC17, also winning the Highest LINPACK award at the same time. Nanyang also scored two silver awards, one at ISC18 and another at SC18. Now that they’ve tasted winning, they want more. Check out the video below to get a gander at this top echelon team…

Team NCKU: This is a first-time team, representing Taiwan’s National Cheng Kung University. The team is at a disadvantage at the ISC19 competition due to budget constraints, they had to seriously scrimp on their cluster. They’re sporting a dual workstation cluster that’s loaded with six NVIDIA 2080 TI consumer GPUs. As you’ll see in the interview below, while these kids don’t have the hardware, they definitely have the brain-ware to compete with any team in the competition. I’d love to see what they could do with a better system….

Team NTHU: The reigning ASC19 champion doesn’t need to be introduced to cluster competition aficionados. The team from Taiwan’s National Tsing Hua University has participated in 15 previous competitions, including ten SC tournaments and five ASC bouts. They competed in the very first cluster competition in 2007. They’ve compiled a damned good record, including three Golds, one Silver and two Bronze medals. Oh, and they’ve won Highest LINPACK four times. Their win at the Inspur-sponsored ASC19 competition earned them Inspur sponsorship for ISC19 and punched their ticket to Frankfurt. Does the win at ASC19 give the team enough momentum to make a big splash at ISC19? Check out the video and see what you think….

Team Sun Yat-Sen: The Chinese Sun Yat-Sen team earned their way to ISC19 by winning second place at the recent Inspur-sponsored ASC19 cluster competition held in Dalian, China. The team has competed in six previous ASC competitions, winning Gold and LINPACK when ASC13 was held on their home court in Guangzhou, China. They added another LINPACK crown at ASC14. They were also a great interview, as you’ll see from the video below. I couldn’t resist taunting them by asking them what their speed-up was on particular applications, then telling them “that’s nice….but I just interviewed Tsinghua and they got an 11.3x speed-up on that app….” It was a lot of fun and they were a great team, take a look at the video to see our interviews….

Team Tsinghua: To date

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, the Tsinghua University cluster competition team has piled up the best record in competition history. They’ve participated in 17 major events, winning Gold ten times, Silver twice and Bronze once. Their most impressive achievement was completing two Triple Crowns (winning all three international competitions in a single year) in 2015 and 2018. They are the odds-on favorite team in every competition they enter, and they enter every major competition. At ISC19, the team is quietly confident and comfortable, but I sense they believe they have something to prove here. Take a look at the video to get a better feel for the team.

Team Hamburg: This team has become a fixture at ISC cluster competitions with ISC19 marking their sixth appearance. While they have yet to win a major award, Team Hamburg has steadily improved since their first competition. This year the team is taking on an additional challenge – they’re one of two teams driving NEC Aurora vector-based clusters. To say that this is a difficult undertaking is an understatement. They’re the test pilots for an entirely new system architecture, one where all of the competition applications have to be ported before they can be optimized. And they’re trying to do this at the same time as they’re competing for cluster competition gold. My hat is off to them…and I don’t lift my hat very easily or often (male pattern baldness).

Team Heidelberg: Heidelberg made their first cluster competition appearance at ISC18, finishing in the upper middle of the pack – which is a great debut. They’re driving a seven node, eight GPU cluster this year, which should give them enough power to make their mark on the ISC19 field. Sometimes the second year is the key for young clusterers; their first year gives them the experience they need to grab the gold in their second year. But we’ve also seen sophomore slumps. We’ll see what happens with Heidelberg.

Team Tartu: This is a team that can best be described by the word ‘gritty.’ ISC19 will be their fourth international competition, with the team competing most recently at ASC19 in Dalian, China. Although the plucky Estonians have yet to win a major award, they never (ever) give up. They are the first team to drive the new fangled 32 core AMD CPUs and they’ve topped off their configuration with 8 32GB NVIDIA V100 GPUs – that’s a lot of power. The heart of their team is their team captain , she has almost single-handedly revived the student cluster competition team at University of Tartu, which is why we gave her a couple of rounds of applause during the video below….

Team Warsaw: This will be the sixth competition for the Warsaw Warriors. They’ve now competed in China, the US and Europe, becoming a seasoned team in the process. The whole Warsaw team, from administrators to mentors to students, are highly enthusiastic about their program and the dividends it pays when it comes to HPC education. Their development over time has been a joy to watch and they’re a true credit to what the Student Cluster Competitions deliver to the participants. They’re also a fun interview, as you’ll see in the video below.

Team UPC: This team is representing the Universitat Politecnica de Catalunya (which is quite the mouthful) from Spain. Backed by the Barcelona Supercomputing Centre, the team has made a life-long commitment to the Arm processor. While technically interesting, the team has toiled away fruitlessly for four years, finishing well back in the pack because they didn’t have CUDA for Arm, meaning they couldn’t add GPUs to their configurations. This is the last year the team will have to deal with this problem – NVIDIA announced at ISC19 that they will be releasing CUDA for Arm by the end of the year. Hallelujah!! The prayers offered up by Team UPC’s ISC18 team have been answered, and we’ll see a much more powerful system from them next year. Yay!

Now that we’ve met the teams, it’s time to meet their clusters. In our next report, we’ll be looking at their hardware, stay tuned….

ISC’19 Student Kluster Kompetition: Apps to confuse & confound

June 25, 2019 · Dan Olds

The line up of benchmarks and real-world HPC applications for this year’s ISC Student Cluster Competition is a mix of old and new, including some apps that have never been run at a sanctioned international event.

Shirt Scramble & Murderers Row of Benchmarks

Starting promptly at 3:00 pm on Monday, the official starters bell rang, and the students were off in a mad dash across the show floor to find their unique team T-shirts. As each team snagged all of their shirts, they will allowed to start their benchmark runs.

The teams had to complete HPCC (a whole bunch of HPC benchmarks crammed together), HPCG (a stress test for every cluster), and a separate LINPACK run to qualify for the Highest LINPACK award.

Application Tuesday

The students started work on their real-world HPC apps on Tuesday. The ISC cluster competition is a series of sprints, with students getting their applications for the day handed out to them each morning. The organizers do this because they want the students to have some free time to experience both the show and the city of Frankfurt. This also helps narrow the focus of the students to optimizing the applications at hand rather than managing the system for day-long or all-night runs.

Here’s a brief rundown on the applications:

CP2K: This is a quantum chemistry and solid-state physics package that can simulate (at the atom level) a whole heck of a lot of materials. The list includes solid stuff, liquids, crystals, and biological matter, which makes it a pretty handy application to have.

Swift: Ever wonder how a star is made? Or a moon? Or a black hole? The forces that shape these things are gravity and hydrodynamics

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, but how do you simulate something so vast and forces so powerful? You get yourself a copy of Swift and a big honking supercomputer to run it on. While you can’t simulate the formation of a star down to the atomic level, Swift helps you describe the process mathematically and calculate out the results – which is a pretty cool thing.

OpenFOAM: While it’s not the most user-friendly application in the world, OpenFOAM is indispensable when it comes to modeling fluid, gas, or really any other things that flow. Want to figure out how gas flows through a pipe? Or how fluids mix in a manufacturing process? OpenFOAM is the ticket. While OpenFOAM has been run in previous cluster competitions, there’s a twist this year: students are not allowed to use GPUs on OpenFOAM in the 2019 ISC competition – making for a more level playing field and a more difficult optimization problem.

Mystery Application (PENNANT): Unstructured meshes, which are basically computational meshes containing arbitrary polygons or polyhedral (whatever the hell those are), are common occurrences in many research problems. However, there aren’t many software packages that convert these problems so they can be solved with accelerators like GPUs. PENNANT is a 2,200 line C++ program that uses CUDA to help convert these problems to GPU-friendly form.

AI Application – Extreme Weather: In this application, the HPC Advisory Council has teamed up with NERSC (National Energy Research Scientific Computing Center) to come up with the workload for this challenge. Students will be provided with a dataset and charged with training a weather model. They will be graded on the accuracy of inferencing unseen data – so it’s not a speed test – the highest accuracy wins.

The ISC19 organizers put together a fiendishly challenging set of applications for the kluster kids this year. There aren’t any lay-ups in this bunch , all will take plenty of thinking and keyboard work to tune and optimize. Stay tuned to see how these plucky undergrads deal with this slate of challenging apps….

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