Nvidia, Google Tie in Second MLPerf Training ‘At-Scale’ Round

By Tiffany Trader

July 10, 2019

Results for the second round of the AI benchmarking suite known as MLPerf were published today with Google Cloud and Nvidia each picking up three wins in the at-scale division. Google for ResNet-50, Transformer and SSD; Nvidia for GNMT, Reinforcement Learning and Mask R-CNN.

In total, Nvidia claimed eight new performance records, three in the at-scale category, with its V100-based clusters, and Google achieved three top at-scale results for its TPU v3 Pods. Like the inaugural MLPerf training round, announced last December, the second round is primarily a two-horse race. Google, Nvidia and Intel are the only companies who submitted to the closed division* and Intel only submitted to one out of the six categories.

Given all the emerging AI silicon startups with heady performance claims, why haven’t any shown up on the MLPerf results?

“Today there really isn’t a production-ready silicon chip that can compete in this space for training,” Karl Freund, consulting lead for HPC and deep learning at industry analyst group Moor Insights & Strategy, told HPCwire. “If you want to do your training in the cloud, you’re going to use Google; if you want to do training on site, you’re going to have to use Nvidia.

“Even by the end of the year, when some companies may have a production ready chip, going from there to having a thousand-chip farm tuned and running at scale to run these benchmarks, it’s going to be another year. And running these benchmarks is really expensive.”

MLPerf Training v0.6 benchmark suite (Source: MLPerf)

Minute Training

Both Nvidia with its DGX-2 SuperPod V100-powered nodes and Google with its TPU Pods achieved “at scale” training times on four of the six benchmarks of about a minute or two.

“There’s a class of models that can now be trained in around a minute,” said Paresh Kharya, director of product management for accelerated computing at Nvidia, in a pre-briefing held for media, citing image classification with ResNet-50, Transformer, GNMT and SSD. “However, there are still some harder problems that take many minutes to train even with the latest state-of-the-art infrastructure. One of the problems is reinforcement learning. We could train the MiniGo model in just under 14 minutes – that was the only submission that Intel had and there was no submission from Google on that model.”

Nvidia said it achieved 20-80 percent more throughput in this testing round on the same DGX-2 hardware used seven months ago, due to software innovations, including to its CUDA-X AI software stack. And since launch in 2017, the same DGX-1 server trains the ResNet-50 model 4 times faster, according to Nvidia.

Detailing Google’s results in a blog post, Zak Stone, senior product manager for Cloud TPUs with the Google Brain Team, stated, “Google Cloud is the first public cloud provider to outperform on-premise systems when running large-scale, industry-standard ML training workloads of Transformer, SSD, and ResNet-50. In the Transformer and SSD categories, Cloud TPU v3 Pods trained models over 84 percent faster than the fastest on-premise systems in the MLPerf Closed Division.”

Google added that compared to the initial alpha results last December, it submitted results on a wider range of tests and employed a full Cloud TPU v3 Pod for the first time. “This additional scale and software optimizations improved our results by up to 62x,” said Google.

Intel reported a measurement of 14.43 minutes to train MiniGo on 32 nodes of a 2-socket Xeon Platinum 8260L processor cluster system. On a single node of a 2-socket Xeon Platinum 9280 system, Intel completed training of the MiniGo model in 77.95 minutes. “These results demonstrate that 2nd generation Intel Xeon Scalable Processors can deliver comparable reinforcement learning (MiniGO) training time as the best accelerator performance in today’s MLPerf 0.6 result publication,” the company said in a statement.

Nvidia was the only company that submitted across all six categories of the industry benchmark suite (which encompasses image classification, object detection, translation, and reinforcement learning). After abstaining from the reinforcement learning category in the previous iteration, Nvidia participated this time, noting that the benchmark had been “refactored by MLPerf to allow for parallelism and meaningful acceleration.”

MLPerf 0.6 Performance at Max Scale. Top to bottom, omitting the “reinforcement learning/MiniGo” category, the Nvidia DGX-2H test machines use 1,536, 480, 256, 240 and 192 V100 GPUs, respectively. The Cloud TPU Pod submissions used 1,024, 1,024, 512, 1,024, and 128 TPU v3.0 chips, respectively. The Intel (MiniGo) submission was conducted on 64 Xeon Platinum 8260L CPUs, going up against three Nvidia DGX-1s (24 V100s). Graphic courtesy Nvidia.

On a “per accelerator” basis, Nvidia swept five out of six categories (see chart below right). Testing was done on one DGX-2H node, comprised of 16 V100s connected via Nvidia’s NVLink switch; except for MiniGo, which was done on one Nvidia DGX-1 (8 V100s). On the ImageNet test, Google’s TPUv3.32 system outperformed Nvidia’s DGX-2H machine; the math works out to 11.25 hours to train the model on one TPUv3 chip versus 14.06 hours on one Nvidia V100.

Nvidia’s Per-Accelerator results – hours to train normalized to one V100 GPU

While competing on a per-accelerator basis might be a lower barrier to entry for newcomers, Google and Nvidia would probably argue that at-scale is mostly what companies care about. “For training, they are not going to wait half a day or more to get their results back,” noted Freund. “They are going to spend the money to get the training done, so they need to see what the results are at scale.

Nvidia’s DGX-2 SuperPod

“Running these benchmarks is very expensive and only the large companies need apply,” added Freund. “Right now, the industry really only has two alternatives for training. Inference will be a different story. There will be close to a score of inference benchmarks published in the next 12 months because it’s a lot easier to do and it’s a wide open market. There’s not an 800lb gorilla sitting on top of it, called Nvidia. There are going to be a lot of startups competing for that space. For training: there’s only two companies now; there will be three when Intel launches Nervana. But it’s going to [continue to] be a very small number.”

MLPerf supporting companies (as of July 09, 2019) – click-to-enlarge

MLPerf is an AI benchmarking suite “for measuring the speed of machine learning software and hardware.” Started by a small group from academia and industry–including Google, Baidu, Intel, AMD, Harvard and Stanford–the project has grown considerably since launching in May 2018. At last count (July 9, 2019), the website lists more than 40 supporting companies: the aforementioned Google, Intel, AMD and Baidu as well as ARM, Nvidia, Cray, Cisco, Microsoft and others (notably, not IBM or Amazon).

According to the consortium, the training benchmark is defined by a dataset and quality target and also provides a reference implementation for each benchmark that uses a specific model. Time to train a model at a specified quality target is the main performance metric. There are six “active” benchmarks in version v0.6 of the suite. (The recommendation benchmark that was part of the v0.5 suite is currently being reviewed.)

MLPerf recently announced the launch of an inference-focused benchmark (see our coverage here); the consortium’s website indicates results for the 0.5 version are due Sept. 6.

* This round also saw a research submission from Alibaba in the closed division and the first open submission – from Fujitsu. To clarify, MLPerf explains, “the Closed division is intended to compare hardware platforms or software frameworks ‘apples-to-apples’ and requires using the same model and optimizer as the reference implementation. The Open division is intended to foster faster models and optimizers and allows any ML approach that can reach the target quality.” The v0.6 results can be reviewed here.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

Intel Speeds NAMD by 1.8x: Saves Xeon Processor Users Millions of Compute Hours

August 12, 2020

Potentially saving datacenters millions of CPU node hours, Intel and the University of Illinois at Urbana–Champaign (UIUC) have collaborated to develop AVX-512 optimizations for the NAMD scalable molecular dynamics cod Read more…

By Rob Farber

Intel’s Optane/DAOS Solution Tops Latest IO500

August 11, 2020

Intel’s persistent memory technology, Optane, and its DAOS (Distributed Asynchronous Object Storage) stack continue to impress and gain market traction. Yesterday, Intel reported an Optane and DAOS-based system finishe Read more…

By John Russell

Summit Now Offers Virtual Tours

August 10, 2020

Summit, the second most powerful publicly ranked supercomputer in the world, now has a virtual tour. The tour, implemented by 3D platform Matterport, allows users to virtually “walk” around the massive supercomputer Read more…

By Oliver Peckham

Supercomputer Simulations Examine Changes in Chesapeake Bay

August 8, 2020

The Chesapeake Bay, the largest estuary in the continental United States, weaves its way south from Maryland, collecting waters from West Virginia, Delaware, DC, Pennsylvania and New York along the way. Like many major e Read more…

By Oliver Peckham

Student Success from ‘Scratch’: CHPC’s Proof is in the Pudding

August 7, 2020

Happy Sithole, who directs the South African Centre for High Performance Computing (SA-CHPC), called the 13th annual CHPC National conference to order on December 1, 2019, at the Birchwood Conference Centre in Kempton Pa Read more…

By Elizabeth Leake

AWS Solution Channel

University of Adelaide Provides Seamless Bioinformatics Training Using AWS

The University of Adelaide, established in South Australia in 1874, maintains a rich history of scientific innovation. For more than 140 years, the institution and its researchers have had an impact all over the world—making vital contributions to the invention of X-ray crystallography, insulin, penicillin, and the Olympic torch. Read more…

Intel® HPC + AI Pavilion

Supercomputing the Pandemic: Scientific Community Tackles COVID-19 from Multiple Perspectives

Since their inception, supercomputers have taken on the biggest, most complex, and most data-intensive computing challenges—from confirming Einstein’s theories about gravitational waves to predicting the impacts of climate change. Read more…

New GE Simulations on Summit to Advance Offshore Wind Power

August 6, 2020

The wind energy sector is a frequent user of high-power simulations, with researchers aiming to optimize wind flows and energy production from the massive turbines. Now, researchers at GE are preparing to undertake a lar Read more…

By Oliver Peckham

Intel Speeds NAMD by 1.8x: Saves Xeon Processor Users Millions of Compute Hours

August 12, 2020

Potentially saving datacenters millions of CPU node hours, Intel and the University of Illinois at Urbana–Champaign (UIUC) have collaborated to develop AVX-51 Read more…

By Rob Farber

Intel’s Optane/DAOS Solution Tops Latest IO500

August 11, 2020

Intel’s persistent memory technology, Optane, and its DAOS (Distributed Asynchronous Object Storage) stack continue to impress and gain market traction. Yeste Read more…

By John Russell

Summit Now Offers Virtual Tours

August 10, 2020

Summit, the second most powerful publicly ranked supercomputer in the world, now has a virtual tour. The tour, implemented by 3D platform Matterport, allows use Read more…

By Oliver Peckham

Research: A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic

August 5, 2020

Within the past years, hardware vendors have started designing low precision special function units in response to the demand of the machine learning community Read more…

By Hartwig Anzt and Jack Dongarra

Implement Photonic Tensor Cores for Machine Learning?

August 5, 2020

Researchers from George Washington University have reported an approach for building photonic tensor cores that leverages phase change photonic memory to implem Read more…

By John Russell

HPE Keeps Cray Brand Promise, Reveals HPE Cray Supercomputing Line

August 4, 2020

The HPC community, ever-affectionate toward Cray and its eponymous founder, can breathe a (virtual) sigh of relief. The Cray brand will live on, encompassing th Read more…

By Tiffany Trader

Machines, Connections, Data, and Especially People: OAC Acting Director Amy Friedlander Charts Office’s Blueprint for Innovation

August 3, 2020

The path to innovation in cyberinfrastructure (CI) will require continued focus on building HPC systems and secure connections between them, in addition to the Read more…

By Ken Chiacchia, Pittsburgh Supercomputing Center/XSEDE

Nvidia Said to Be Close on Arm Deal

August 3, 2020

GPU leader Nvidia Corp. is in talks to buy U.K. chip designer Arm from parent company Softbank, according to several reports over the weekend. If consummated Read more…

By George Leopold

Supercomputer Modeling Tests How COVID-19 Spreads in Grocery Stores

April 8, 2020

In the COVID-19 era, many people are treating simple activities like getting gas or groceries with caution as they try to heed social distancing mandates and protect their own health. Still, significant uncertainty surrounds the relative risk of different activities, and conflicting information is prevalent. A team of Finnish researchers set out to address some of these uncertainties by... Read more…

By Oliver Peckham

Supercomputer-Powered Research Uncovers Signs of ‘Bradykinin Storm’ That May Explain COVID-19 Symptoms

July 28, 2020

Doctors and medical researchers have struggled to pinpoint – let alone explain – the deluge of symptoms induced by COVID-19 infections in patients, and what Read more…

By Oliver Peckham

Nvidia Said to Be Close on Arm Deal

August 3, 2020

GPU leader Nvidia Corp. is in talks to buy U.K. chip designer Arm from parent company Softbank, according to several reports over the weekend. If consummated Read more…

By George Leopold

Intel’s 7nm Slip Raises Questions About Ponte Vecchio GPU, Aurora Supercomputer

July 30, 2020

During its second-quarter earnings call, Intel announced a one-year delay of its 7nm process technology, which it says it will create an approximate six-month shift for its CPU product timing relative to prior expectations. The primary issue is a defect mode in the 7nm process that resulted in yield degradation... Read more…

By Tiffany Trader

Supercomputer Simulations Reveal the Fate of the Neanderthals

May 25, 2020

For hundreds of thousands of years, neanderthals roamed the planet, eventually (almost 50,000 years ago) giving way to homo sapiens, which quickly became the do Read more…

By Oliver Peckham

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Neocortex Will Be First-of-Its-Kind 800,000-Core AI Supercomputer

June 9, 2020

Pittsburgh Supercomputing Center (PSC - a joint research organization of Carnegie Mellon University and the University of Pittsburgh) has won a $5 million award Read more…

By Tiffany Trader

HPE Keeps Cray Brand Promise, Reveals HPE Cray Supercomputing Line

August 4, 2020

The HPC community, ever-affectionate toward Cray and its eponymous founder, can breathe a (virtual) sigh of relief. The Cray brand will live on, encompassing th Read more…

By Tiffany Trader

Leading Solution Providers

Contributors

Nvidia’s Ampere A100 GPU: Up to 2.5X the HPC, 20X the AI

May 14, 2020

Nvidia's first Ampere-based graphics card, the A100 GPU, packs a whopping 54 billion transistors on 826mm2 of silicon, making it the world's largest seven-nanom Read more…

By Tiffany Trader

Australian Researchers Break All-Time Internet Speed Record

May 26, 2020

If you’ve been stuck at home for the last few months, you’ve probably become more attuned to the quality (or lack thereof) of your internet connection. Even Read more…

By Oliver Peckham

15 Slides on Programming Aurora and Exascale Systems

May 7, 2020

Sometime in 2021, Aurora, the first planned U.S. exascale system, is scheduled to be fired up at Argonne National Laboratory. Cray (now HPE) and Intel are the k Read more…

By John Russell

‘Billion Molecules Against COVID-19’ Challenge to Launch with Massive Supercomputing Support

April 22, 2020

Around the world, supercomputing centers have spun up and opened their doors for COVID-19 research in what may be the most unified supercomputing effort in hist Read more…

By Oliver Peckham

Joliot-Curie Supercomputer Used to Build First Full, High-Fidelity Aircraft Engine Simulation

July 14, 2020

When industrial designers plan the design of a new element of a vehicle’s propulsion or exterior, they typically use fluid dynamics to optimize airflow and in Read more…

By Oliver Peckham

John Martinis Reportedly Leaves Google Quantum Effort

April 21, 2020

John Martinis, who led Google’s quantum computing effort since establishing its quantum hardware group in 2014, has left Google after being moved into an advi Read more…

By John Russell

$100B Plan Submitted for Massive Remake and Expansion of NSF

May 27, 2020

Legislation to reshape, expand - and rename - the National Science Foundation has been submitted in both the U.S. House and Senate. The proposal, which seems to Read more…

By John Russell

Google Cloud Debuts 16-GPU Ampere A100 Instances

July 7, 2020

On the heels of the Nvidia’s Ampere A100 GPU launch in May, Google Cloud is announcing alpha availability of the A100 “Accelerator Optimized” VM A2 instance family on Google Compute Engine. The instances are powered by the HGX A100 16-GPU platform, which combines two HGX A100 8-GPU baseboards using... Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This