MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

By John Russell

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – released MLPerf, a nascent benchmarking tool “for measuring the speed of machine learning software and hardware.” Arrival of MLPerf follows what has been a smattering of ad hoc AI performance comparisons trickling to market. Last week, RiseML blog compared Google’s TPUv2 against Nvidia V100. Today Intel posted a blog with data showing for select machine translation using RNNs “the Intel Xeon Scalable processor outperforms NVidia V100 by 4x on the AWS Sockeye Neural Machine Translation model.”

For quite some time there has been vigorous discussion around the need for meaningful AI benchmarks with proponents suggesting that the lack of meaningful benchmark tools has restrained AI adoption. Quoted in the MLPerf announcement is AI pioneer Andrew Ng, “AI is transforming multiple industries, but for it to reach its full potential, we still need faster hardware and software.” The hope is better, standardized benchmarks will help AI technology developers create such products and allow adopters to make informed AI-enabling technology selections.

MLPerf says its primary goals are to:

  • Accelerate progress in ML via fair and useful measurement
  • Enable fair comparison of competing systems yet encourage innovation to improve the state-of-the-art of ML
  • Keep benchmarking effort affordable so all can participate
  • Serve both the commercial and research communities
  • Enforce replicability to ensure reliable results

Comparisons of AI performance (h/w and s/w) have so far largely been issued by parties with vested interest, such as Intel’s blog today entitled, “Amazing Inference Performance with Intel Xeon Scalable Processors.” This isn’t a knock on Intel. Such comparisons often contain useful insight, but they are also often structured to demonstrate one vendor’s superiority over a competitor. A standardized benchmark mitigates tweaking of tests to get the result one wants.

The MLPerf effort is emulating, for example, past efforts such as SPEC (The Standard Performance Evaluation Corporation). “[T]he SPEC benchmark helped accelerate improvements in general purpose computing. SPEC was introduced in 1988 by a consortium of computing companies. CPU Performance improved 1.6X/year for the next 15 years. MLPerf combines best practices from previous benchmarks including: SPEC’s use of a suite of programs, SORT’s use one division to enable comparisons and another division to foster innovative ideas, DeepBench’s coverage of software deployed in production, and DAWNBench’s time-to-accuracy metric,” says MLPerf.

Addison Snell, CEO of Intersect360 Research, noted, “AI is on the minds of so many enterprises today, that any effort to provide neutral benchmarking guidance is of heightened importance, especially with the range of competing technologies at play. However, AI is such a diverse field, I doubt any single benchmark will become dominant over time. Consider all the zeal around big data and analytics five years ago; despite everyone’s attempts to define it, the industry didn’t provide a unified, common benchmark. I expect the same will happen with AI.”

MLPerf is a “good and useful” step said Steve Conway, senior research vice president, Hyperion Research, “because there has been a real lack of benchmarks for buyers and sellers for years to show the differences between AI products and solutions. This benchmark appears to be written for bounded problems that predominate today in early AI. Later on we are going to need additional benchmarks as AI starts getting into unbounded problems that will be the most economically important problems. Bounded problems are relatively simple like voice and image recognition or game playing. An unbounded problem is diagnosing a cancer versus a bounded problem of reading an MRI; it’s being able to recommend decision on really complicated questions.”

MLPerf is available now on GitHub but still in a very early stage, as emphasized by MLPerf, “This release is very much an ‘alpha’ release — it could be improved in many ways. The benchmark suite is still being developed and refined, see the Suggestions section below to learn how to contribute. We anticipate a significant round of updates at the end of May based on input from users.”

Currently there are reference implementations for each of the seven benchmarks in the MLPerf suite (excerpted from GitHub):

  • Image classification– Resnet-50 v1 applied to Imagenet.
  • Object detection– Mask R-CNN applied to COCO.
  • Speech recognition– DeepSpeech2 applied to Librispeech.
  • Translation– Transformer applied to WMT English-German.
  • Recommendation– Neural Collaborative Filtering applied to MovieLens 20 Million (ml-20m).
  • Sentiment analysis– Seq-CNN applied to IMDB dataset.
  • Reinforcement– Mini-go applied to predicting pro game moves.

Each reference implementation provides the following: code that implements the model in at least one framework; a Dockerfile which can be used to run the benchmark in a container; a script which downloads the appropriate dataset; A script which runs and times training the model; and documentaiton on the dataset, model, and machine setup.

According to the GitHub site, the benchmarks have been tested on the following machine configuration:

  • 16 CPUs, one Nvidia P100.
  • Ubuntu 16.04, including docker with nvidia support.
  • 600GB of disk (though many benchmarks do require less disk).

It will be interesting to watch whether the industry coalesces around a few AI benchmarks or if benchmarks proliferate. In such a young market, many are likely to offer benchmarking tools and services. For example, Stanford – which is MLPerf member – recently ran its first DAWNBench v1 Deep Learning results.

Stanford reported: “April 20, 2018 marked first deep learning benchmark and competition that measures end-to-end performance: the time/cost required to achieve a state-of-the-art accuracy level for common deep learning tasks, as well as the latency/cost of inference at this state-of-the-art accuracy level. Focusing on end-to-end performance provided an objective means of normalizing across differences in computation frameworks, hardware, optimization algorithms, hyperparameter settings, and other factors that affect real-world performance.”

One DAWN competitor, fast.ai– a young company offering AI training and developing AI software tools – reached out to HPCwire touting its performance (see company blog for results). These benchmarks matter, and it seems very likely that any Stanford-run exercise is serious and should be taken seriously. That said, others may be less so. An effort such as MLPerf could help clear the currently muddy waters going forward when comparing AI claims.

Link to MLPerf user guide: https://mlperf.org/assets/static/media/MLPerf-User-Guide.pdf

* Additional reporting by Tiffany Trader

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!

University of Chicago Researchers Generate First Computational Model of Entire SARS-CoV-2 Virus

January 15, 2021

Over the course of the last year, many detailed computational models of SARS-CoV-2 have been produced with the help of supercomputers, but those models have largely focused on critical elements of the virus, such as its Read more…

By Oliver Peckham

Pat Gelsinger Returns to Intel as CEO

January 14, 2021

The Intel board of directors has appointed a new CEO. Intel alum Pat Gelsinger is leaving his post as CEO of VMware to rejoin the company that he parted ways with 11 years ago. Gelsinger will succeed Bob Swan, who will remain CEO until Feb. 15. Gelsinger previously spent 30 years... Read more…

By Tiffany Trader

Roar Supercomputer to Support Naval Aircraft Research

January 14, 2021

One might not think “aircraft” when picturing the U.S. Navy, but the military branch actually has thousands of aircraft currently in service – and now, supercomputing will help future naval aircraft operate faster, Read more…

By Staff report

DOE and NOAA Extend Computing Partnership, Plan for New Supercomputer

January 14, 2021

The National Climate-Computing Research Center (NCRC), hosted by Oak Ridge National Laboratory (ORNL), has been supporting the climate research of the National Oceanic and Atmospheric Administration (NOAA) for the last 1 Read more…

By Oliver Peckham

Using Micro-Combs, Researchers Demonstrate World’s Fastest Optical Neuromorphic Processor for AI

January 13, 2021

Neuromorphic computing, which uses chips that mimic the behavior of the human brain using virtual “neurons,” is growing in popularity thanks to high-profile efforts from Intel and others. Now, a team of researchers l Read more…

By Oliver Peckham

AWS Solution Channel

Now Available – Amazon EC2 C6gn Instances with 100 Gbps Networking

Amazon EC2 C6gn instances powered by AWS Graviton2 processors are now available!

Compared to C6g instances, this new instance type provides 4x higher network bandwidth, 4x higher packet processing performance, and 2x higher EBS bandwidth. Read more…

Intel® HPC + AI Pavilion

Intel Keynote Address

Intel is the foundation of HPC – from the workstation to the cloud to the backbone of the Top500. At SC20, Intel’s Trish Damkroger, VP and GM of high performance computing, addresses the audience to show how Intel and its partners are building the future of HPC today, through hardware and software technologies that accelerate the broad deployment of advanced HPC systems. Read more…

Honing In on AI, US Launches National Artificial Intelligence Initiative Office

January 13, 2021

To drive American leadership in the field of AI into the future, the National Artificial Intelligence Initiative Office has been launched by the White House Office of Science and Technology Policy (OSTP). The new agen Read more…

By Todd R. Weiss

Pat Gelsinger Returns to Intel as CEO

January 14, 2021

The Intel board of directors has appointed a new CEO. Intel alum Pat Gelsinger is leaving his post as CEO of VMware to rejoin the company that he parted ways with 11 years ago. Gelsinger will succeed Bob Swan, who will remain CEO until Feb. 15. Gelsinger previously spent 30 years... Read more…

By Tiffany Trader

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

By John Russell

Intel ‘Ice Lake’ Server Chips in Production, Set for Volume Ramp This Quarter

January 12, 2021

Intel Corp. used this week’s virtual CES 2021 event to reassert its dominance of the datacenter with the formal roll out of its next-generation server chip, the 10nm Xeon Scalable processor that targets AI and HPC workloads. The third-generation “Ice Lake” family... Read more…

By George Leopold

Researchers Say It Won’t Be Possible to Control Superintelligent AI

January 11, 2021

Worries about out-of-control AI aren’t new. Many prominent figures have suggested caution when unleashing AI. One quote that keeps cropping up is (roughly) th Read more…

By John Russell

AMD Files Patent on New GPU Chiplet Approach

January 5, 2021

Advanced Micro Devices is accelerating the GPU chiplet race with the release of a U.S. patent application for a device that incorporates high-bandwidth intercon Read more…

By George Leopold

Programming the Soon-to-Be World’s Fastest Supercomputer, Frontier

January 5, 2021

What’s it like designing an app for the world’s fastest supercomputer, set to come online in the United States in 2021? The University of Delaware’s Sunita Chandrasekaran is leading an elite international team in just that task. Chandrasekaran, assistant professor of computer and information sciences, recently was named... Read more…

By Tracey Bryant

Intel Touts Optane Performance, Teases Next-gen “Crow Pass”

January 5, 2021

Competition to leverage new memory and storage hardware with new or improved software to create better storage/memory schemes has steadily gathered steam during Read more…

By John Russell

Farewell 2020: Bleak, Yes. But a Lot of Good Happened Too

December 30, 2020

Here on the cusp of the new year, the catchphrase ‘2020 hindsight’ has a distinctly different feel. Good riddance, yes. But also proof of science’s power Read more…

By John Russell

Esperanto Unveils ML Chip with Nearly 1,100 RISC-V Cores

December 8, 2020

At the RISC-V Summit today, Art Swift, CEO of Esperanto Technologies, announced a new, RISC-V based chip aimed at machine learning and containing nearly 1,100 low-power cores based on the open-source RISC-V architecture. Esperanto Technologies, headquartered in... Read more…

By Oliver Peckham

Azure Scaled to Record 86,400 Cores for Molecular Dynamics

November 20, 2020

A new record for HPC scaling on the public cloud has been achieved on Microsoft Azure. Led by Dr. Jer-Ming Chia, the cloud provider partnered with the Beckman I Read more…

By Oliver Peckham

NICS Unleashes ‘Kraken’ Supercomputer

April 4, 2008

A Cray XT4 supercomputer, dubbed Kraken, is scheduled to come online in mid-summer at the National Institute for Computational Sciences (NICS). The soon-to-be petascale system, and the resulting NICS organization, are the result of an NSF Track II award of $65 million to the University of Tennessee and its partners to provide next-generation supercomputing for the nation's science community. Read more…

Is the Nvidia A100 GPU Performance Worth a Hardware Upgrade?

October 16, 2020

Over the last decade, accelerators have seen an increasing rate of adoption in high-performance computing (HPC) platforms, and in the June 2020 Top500 list, eig Read more…

By Hartwig Anzt, Ahmad Abdelfattah and Jack Dongarra

Aurora’s Troubles Move Frontier into Pole Exascale Position

October 1, 2020

Intel’s 7nm node delay has raised questions about the status of the Aurora supercomputer that was scheduled to be stood up at Argonne National Laboratory next year. Aurora was in the running to be the United States’ first exascale supercomputer although it was on a contemporaneous timeline with... Read more…

By Tiffany Trader

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

By John Russell

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

Programming the Soon-to-Be World’s Fastest Supercomputer, Frontier

January 5, 2021

What’s it like designing an app for the world’s fastest supercomputer, set to come online in the United States in 2021? The University of Delaware’s Sunita Chandrasekaran is leading an elite international team in just that task. Chandrasekaran, assistant professor of computer and information sciences, recently was named... Read more…

By Tracey Bryant

Leading Solution Providers

Contributors

Top500: Fugaku Keeps Crown, Nvidia’s Selene Climbs to #5

November 16, 2020

With the publication of the 56th Top500 list today from SC20's virtual proceedings, Japan's Fugaku supercomputer – now fully deployed – notches another win, Read more…

By Tiffany Trader

Texas A&M Announces Flagship ‘Grace’ Supercomputer

November 9, 2020

Texas A&M University has announced its next flagship system: Grace. The new supercomputer, named for legendary programming pioneer Grace Hopper, is replacing the Ada system (itself named for mathematician Ada Lovelace) as the primary workhorse for Texas A&M’s High Performance Research Computing (HPRC). Read more…

By Oliver Peckham

At Oak Ridge, ‘End of Life’ Sometimes Isn’t

October 31, 2020

Sometimes, the old dog actually does go live on a farm. HPC systems are often cursed with short lifespans, as they are continually supplanted by the latest and Read more…

By Oliver Peckham

Nvidia and EuroHPC Team for Four Supercomputers, Including Massive ‘Leonardo’ System

October 15, 2020

The EuroHPC Joint Undertaking (JU) serves as Europe’s concerted supercomputing play, currently comprising 32 member states and billions of euros in funding. I Read more…

By Oliver Peckham

Gordon Bell Special Prize Goes to Massive SARS-CoV-2 Simulations

November 19, 2020

2020 has proven a harrowing year – but it has produced remarkable heroes. To that end, this year, the Association for Computing Machinery (ACM) introduced the Read more…

By Oliver Peckham

Nvidia-Arm Deal a Boon for RISC-V?

October 26, 2020

The $40 billion blockbuster acquisition deal that will bring chipmaker Arm into the Nvidia corporate family could provide a boost for the competing RISC-V architecture. As regulators in the U.S., China and the European Union begin scrutinizing the impact of the blockbuster deal on semiconductor industry competition and innovation, the deal has at the very least... Read more…

By George Leopold

Intel Xe-HP GPU Deployed for Aurora Exascale Development

November 17, 2020

At SC20, Intel announced that it is making its Xe-HP high performance discrete GPUs available to early access developers. Notably, the new chips have been deplo Read more…

By Tiffany Trader

HPE, AMD and EuroHPC Partner for Pre-Exascale LUMI Supercomputer

October 21, 2020

Not even a week after Nvidia announced that it would be providing hardware for the first four of the eight planned EuroHPC systems, HPE and AMD are announcing a Read more…

By Oliver Peckham

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