Nvidia Leads Alpha MLPerf Benchmarking Round

By Tiffany Trader

December 12, 2018

Seven months after the launch of its AI benchmarking suite, the MLPerf consortium is releasing the first round of results based on submissions from Nvidia, Google and Intel. Of the seven benchmarks encompassed in version v0.5 of the would-be benchmarking standard, Nvidia announced that it captured the lead spot in six. Separately, Google (which led the creation of the benchmark) said results show Google Cloud “offers the most accessible scale for machine learning training.”

MLPerf supporting companies (as of Dec. 12, 2018) – click-to-enlarge

As HPCwire reported in May, MLPerf is an emerging 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 in the last half-year. At last count, the website lists 31 supporting companies: the aforementioned Google, Intel, AMD and Baidu as well as ARM, Nvidia, Cray, Cisco, Microsoft and others (but 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. The following table summarizes the seven benchmarks in version v0.5 of the suite, which spans five categories (image classification, object detection, translation, recommendation and reinforcement learning). Time to train is the main performance metric.

MLPerf v0.5 benchmark suite (Source: MLPerf)

Nvidia revealed today that its platforms outperformed the competition by up to 5.3x (faster time to results), showing leading single-node and at-scale results for six of the workloads. Nvidia opted not to submit for reinforcement learning network because, as Ian Buck, vice president and general manager of accelerated computing at Nvidia, explained in an advance press briefing, it is for the most part CPU-based and does not have meaningful acceleration in its current form.

Nvidia submitted for all of the six accelerated benchmarks in two categories — single node (testing up to 16 V100 GPUs in the DGX-2H platform) and at-scale (testing in various configurations, up to 640 GPUs).

In a blog post published today, Nvidia stated that “a single DGX-2 node can complete many of these workloads in under twenty minutes. And in the case of our at-scale submission, we’re completing these tasks in under seven minutes in all but one of the tests.”

Test Platform: DGX-2H – Dual-Socket Xeon Platinum 8174, 1.5TB system RAM, 16 x 32 GB Tesla V100 SXM-3 GPUs connected via NVSwitch (Source: Nvidia, see endnotes for details)
Test Platform: For Image Classification and Translation (non-recurrent), DGX-1V Cluster. For Object Detection (Heavy Weight) and Object Detection (Light Weight), Translation (recurrent) DGX-2H Cluster. Each DGX-1V, Dual-Socket Xeon E5- 2698 V4, 512GB system RAM, 8 x 16 GB Tesla V100 SXM-2 GPUs. Each DGX-2H, Dual-Socket Xeon Platinum 8174, 1.5TB system RAM, 16 x 32 GB Tesla V100 SXM-3 GPUs connected via NVSwitch. (Source: Nvidia, see endnotes for details)

While there are faster ResNet50 competitions out there, they aren’t under the standard MLPerf guidelines, Nvidia told us.

Source: Nvidia (Dec. 10, 2018)

“By improving and delivering on the full-stack optimization and our performance at scale, we decrease training times, which makes research and deployment of AI faster and we improve the cost efficiency,” said Ian Buck in the press briefing. “If I take a DGX Station and look at its value over four years, it’s roughly $1.50/hr, so a little over $6 to train a ResNet50.” Buck added that Titan RTX, announced last week with a list price of $2,499, comes out to just over $2.00 to train a single ResNet50.

Speaking to the value of the “industry’s first comprehensive AI benchmark” and what that means for customers, Buck stated: “Nvidia is no stranger to benchmarks; we certainly have them in the graphics space, we have them in the supercomputing space and we now have them as well in the AI world. Providing a common benchmark, a common set of rules as long as it’s appropriately governed can provide perspective to customers and the rest of the community on the state of everyone’s solution. It also provides a nice common platform for people to innovate, to measure innovation and help companies move the ball forward in improving the performance.”

Google also took time to promote its results today in a blog post, claiming Google Cloud “offers the most accessible scale for machine learning training” and “a 19% TPU performance advantage on a chip-to-chip basis.”

The results show Google Cloud’s TPUs (Tensor Processing Units) and TPU Pods as leading systems for training machine learning models at scale, based on competitive performance across several MLPerf tests,” wrote Urs Hölzle, Senior Vice President of Technical Infrastructure, Google.

“For data scientists, ML practitioners, and researchers, building on-premise GPU clusters for training is capital-intensive and time-consuming—it’s much simpler to access both GPU and TPU infrastructure on Google Cloud,” said Hölzle.

This graphic from Google compares absolute training times for Nvidia’s DGX-2 machine, containing 16 V100 GPUs, with results using 1/64th of a TPU v3 Pod (16 TPU v3 chips used for training and 4 TPU v2 chips used for evaluation). The three benchmarks shown are image classification (ResNet-50), object detection (SSD), and neural machine translation (NMT).

Training time comparison between 1/64th of a TPU v3 Pod (16 TPU v3 chips used for training, plus four separate Cloud TPU v2 chips used for evaluation) and an Nvidia DGX-2 (16 V100 GPUs) (Source: Google Cloud)

The inaugural MLPerf testing only had three submitters: Nvidia, Google and Intel. All submitted for the closed division, which compares hardware platforms or software frameworks on an “apples-to-apples” basis. There were no submissions for the open division, which allows any ML approach that can reach the target quality and is intended to foster innovation. See results here: https://mlperf.org/results/

Noted on its Github page, “MLPerf v0.5.0 is the ‘alpha’ release of an agile benchmark, and the benchmark is still evolving based on feedback from the community.” Changes under consideration include “raising target quality, adopting a standard batch-size-to-hyperparameter table, scaling up some benchmarks (especially recommendation), and adding new benchmarks.” The current suite is limited to training workloads, but according to Nvidia, there are plans to add inference-focused benchmarks. The consortium is working on releasing interim versions of the suite (v0.5.1 and v0.5.2) in the first half of 2019 with a full version 1.0 release planned for the third quarter of 2019.

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!

Senegal Prepares to Take Delivery of Atos Supercomputer

January 16, 2019

In just a few months time, Senegal will be operating the second largest HPC system in sub-Saharan Africa. The Minister of Higher Education, Research and Innovation Mary Teuw Niane made the announcement on Monday (Jan. 14 Read more…

By Tiffany Trader

Google Cloud Platform Extends GPU Instance Options

January 16, 2019

If it's Nvidia GPUs you're after to power your AI/HPC/visualization workload, Google Cloud has them, now claiming "broadest GPU availability." Each of the three big public cloud vendors has by turn touted the latest and Read more…

By Tiffany Trader

A Big Data Journey While Seeking to Catalog our Universe

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterized as transforming data into insights – which is exactly wh Read more…

By James Reinders

HPE Extreme Performance Solutions

HPE Systems With Intel Omni-Path: Architected for Value and Accessible High-Performance Computing

Today’s high-performance computing (HPC) and artificial intelligence (AI) users value high performing clusters. And the higher the performance that their system can deliver, the better. Read more…

IBM Accelerated Insights

Resource Management in the Age of Artificial Intelligence

New challenges demand fresh approaches

Fueled by GPUs, big data, and rapid advances in software, the AI revolution is upon us. Read more…

STAC Floats ML Benchmark for Financial Services Workloads

January 16, 2019

STAC (Securities Technology Analysis Center) recently released an ‘exploratory’ benchmark for machine learning which it hopes will evolve into a firm benchmark or suite of benchmarking tools to compare the performanc Read more…

By John Russell

A Big Data Journey While Seeking to Catalog our Universe

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterize Read more…

By James Reinders

STAC Floats ML Benchmark for Financial Services Workloads

January 16, 2019

STAC (Securities Technology Analysis Center) recently released an ‘exploratory’ benchmark for machine learning which it hopes will evolve into a firm benchm Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

IBM’s New Global Weather Forecasting System Runs on GPUs

January 9, 2019

Anyone who has checked a forecast to decide whether or not to pack an umbrella knows that weather prediction can be a mercurial endeavor. It is a Herculean task: the constant modeling of incredibly complex systems to a high degree of accuracy at a local level within very short spans of time. Read more…

By Oliver Peckham

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

HPCwire Awards Highlight Supercomputing Achievements in the Sciences

January 3, 2019

In November at SC18 in Dallas, HPCwire Readers’ and Editors’ Choice awards program commemorated its 15th year of honoring achievement in HPC, with categories ranging from Best Use of AI to the Workforce Diversity Leadership Award and recipients across a wide variety of industrial and research sectors. Read more…

By the Editorial Team

White House Top Science Post Filled After Two-Year Vacancy

January 3, 2019

Half-way into Trump's term, the Senate has confirmed a director for the Office of Science and Technology Policy (OSTP), the agency that coordinates science poli Read more…

By Tiffany Trader

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Microsoft to Buy Mellanox?

December 20, 2018

Networking equipment powerhouse Mellanox could be an acquisition target by Microsoft, according to a published report in an Israeli financial publication. Microsoft has reportedly gone so far as to engage Goldman Sachs to handle negotiations with Mellanox. Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

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