Nvidia Responds to Google TPU Benchmarking

By Tiffany Trader

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia’s K80 GPU (see our coverage, Google Pulls Back the Covers on Its First Machine Learning Chip), and it didn’t take Nvidia long to respond. Unlike the semi-contentious back-and-forth between Nvidia and Intel over benchmarking methodology (see Nvidia Cries Foul on Intel Phi AI Benchmarks), Nvidia took a decidedly more friendly approach in responding to Google. Google of course is a big buyer of Nvidia gear – both for internal neural net training workloads and for accelerating HPC and AI workloads inside its Google Compute Engine cloud.

Responding in a blog post published earlier today, Nvidia is choosing to frame the recent TPU results not as a potential competitive threat, but as as a clear sign of the ascendancy of accelerated computing. “Without accelerated computing, the scale-out of AI is simply not practical,” is the conclusion that Nvidia draws.

“While Google and Nvidia chose different development paths, there were several themes common to both our approaches,” observed Nvidia CEO Jen-Hsun Huang, noting:

  • AI requires accelerated computing. Accelerators provide the significant data processing demands of deep learning in an era when Moore’s law is slowing.
  • Tensor processing is at the core of delivering performance for deep learning training and inference.
  • Tensor processing is a major new workload enterprises must consider when building modern data centers.
  • Accelerating tensor processing can dramatically reduce the cost of building modern data centers.

Nvidia heartily applauds Google for its AI successes (“The startling precision of its Google Now service; the landmark victory over the world’s greatest Go player; Google Translate’s ability to operate in 100 different languages”), but also makes sure to highlight how its GPU technology has progressed since the 2015-timeframe when the TPU was deployed in Google datacenters.

In September 2016, Google released the P40 GPU, based on the Pascal architecture, to accelerate inferencing workloads for modern AI applications, such as speech translation and video analysis. Recall that Google benchmarked the TPU against the older (late 2014-era) K80 GPU, based on the Kepler architecture, which debuted in 2012. Nvidia created the following chart to “quantify the performance leap from K80 to P40, and to show how the TPU compares to current NVIDIA technology.”

The Google paper, scrupulous in exploring potential criticisms to its methodology, references the newer P40 silicon, noting 1) “the…P40 was unavailable in early 2015, so isn’t contemporary with our [TPU]”; 2) “We also can’t know the fraction of P40 peak delivered within our rigid time bounds”; and 3) “If we compared newer chips, Section 7 shows that we could triple performance of the…TPU just by using the K80’s GDDR5 memory (at a cost of an additional 10W).”

Based on TDP specs, the TPU is more efficient than the P40 on an operations-per-watt basis by a 6.2X margin (for 8-bit inferencing workloads).

Google cited other reasons to indicate that the TPU is “not an easy target” (refer to Section 7 of the paper, “Evaluation of Alternative TPU Designs”), but keep in mind the TPU can only satisfy inferencing workloads. The training phase of deep learning is far more complicated and GPUs have the lead currently.

Nvidia emphasizes the P40’s ability to accelerate both phases of deep learning:

“The P40 balances computational precision and throughput, on-chip memory and memory bandwidth to achieve unprecedented performance for training, as well as inferencing. For training, P40 has 10x the bandwidth and 12 teraflops of 32-bit floating point performance. For inferencing, P40 has high-throughput 8-bit integer and high-memory bandwidth,” Nvidia states.

Is it surprising that Google, a company without a track record in chip manufacturing, can design a processor to rival or surpass a leading silicon vendor such as Nvidia? With sufficiently deep pockets, anyone can create a custom ASIC that beats general-purpose hardware for a narrow application. The question is whether the strategy will pay off. With deep learning algorithms still evolving at light speed, it can be risky to lock down hardware functionality if you’ll need to change out the silicon a year later, when the algorithms refresh. But Google, running the largest compute infrastructure in the world, is a special case that can mine physical scales of economy even if it isn’t able to amortize the outlay over very long periods. Google hinted that a successor to “this first generation” of TPUs is in the works and may even be working on a third-gen for all we know. The company that gave the world MapReduce and TensorFlow is widely known for innovating far ahead of what it makes public.

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!

And So It Begins…Again – The FY19 Exascale Budget Rollout (and things look good)

February 23, 2018

On February 12, 2018, the Trump administration submitted its Fiscal Year 2019 (FY-19) budget to Congress. The good news for the U.S. exascale program is that the numbers look very good and the support appears to be stron Read more…

By Alex R. Larzelere

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with partner Leibniz Supercomputing Center (LRZ) in Germany. The ser Read more…

By Tiffany Trader

Start-up Aims AI at Automated Tuning of Complex Systems

February 22, 2018

Today’s bigger, more complex, connected and intelligent systems have an exponentially higher number of connections, dependencies, interfaces, protocols and processing architectures that, if not optimized, will hamstrin Read more…

By Doug Black

HPE Extreme Performance Solutions

Experience Memory & Storage Solutions that will Transform Your Data Performance

High performance computing (HPC) has revolutionized the way we harness insight, leading to a dramatic increase in both the size and complexity of HPC systems. Read more…

Do Cryptocurrencies Have a Part to Play in HPC?

February 22, 2018

It’s easy to be distracted by news from the US, China, and now the EU on the state of various exascale projects, but behind the vinyl-wrapped cabinets and well-groomed sales execs are an army of Excel-wielding PMO and Read more…

By Chris Downing

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Start-up Aims AI at Automated Tuning of Complex Systems

February 22, 2018

Today’s bigger, more complex, connected and intelligent systems have an exponentially higher number of connections, dependencies, interfaces, protocols and pr Read more…

By Doug Black

HOKUSAI’s BigWaterfall Cluster Extends RIKEN’s Supercomputing Performance

February 21, 2018

RIKEN, Japan’s largest comprehensive research institution, recently expanded the capacity and capabilities of its HOKUSAI supercomputer, a key resource manage Read more…

By Ken Strandberg

Neural Networking Shows Promise in Earthquake Monitoring

February 21, 2018

A team of Harvard University and MIT researchers report their new neural networking method for monitoring earthquakes is more accurate and orders of magnitude faster than traditional approaches. Read more…

By John Russell

HPE Wins $57 Million DoD Supercomputing Contract

February 20, 2018

Hewlett Packard Enterprise (HPE) today revealed details of its massive $57 million HPC contract with the U.S. Department of Defense (DoD). The deal calls for HP Read more…

By Tiffany Trader

Fluid HPC: How Extreme-Scale Computing Should Respond to Meltdown and Spectre

February 15, 2018

The Meltdown and Spectre vulnerabilities are proving difficult to fix, and initial experiments suggest security patches will cause significant performance penal Read more…

By Pete Beckman

Brookhaven Ramps Up Computing for National Security Effort

February 14, 2018

Last week, Dan Coats, the director of Director of National Intelligence for the U.S., warned the Senate Intelligence Committee that Russia was likely to meddle in the 2018 mid-term U.S. elections, much as it stands accused of doing in the 2016 Presidential election. Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Japan Unveils Quantum Neural Network

November 22, 2017

The U.S. and China are leading the race toward productive quantum computing, but it's early enough that ultimate leadership is still something of an open questi Read more…

By Tiffany Trader

AMD Showcases Growing Portfolio of EPYC and Radeon-based Systems at SC17

November 13, 2017

AMD’s charge back into HPC and the datacenter is on full display at SC17. Having launched the EPYC processor line in June along with its MI25 GPU the focus he Read more…

By John Russell

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

IBM Begins Power9 Rollout with Backing from DOE, Google

December 6, 2017

After over a year of buildup, IBM is unveiling its first Power9 system based on the same architecture as the Department of Energy CORAL supercomputers, Summit a Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Leading Solution Providers

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

Perspective: What Really Happened at SC17?

November 22, 2017

SC is over. Now comes the myriad of follow-ups. Inboxes are filled with templated emails from vendors and other exhibitors hoping to win a place in the post-SC thinking of booth visitors. Attendees of tutorials, workshops and other technical sessions will be inundated with requests for feedback. Read more…

By Andrew Jones

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

GlobalFoundries, Ayar Labs Team Up to Commercialize Optical I/O

December 4, 2017

GlobalFoundries (GF) and Ayar Labs, a startup focused on using light, instead of electricity, to transfer data between chips, today announced they've entered in Read more…

By Tiffany Trader

Tensors Come of Age: Why the AI Revolution Will Help HPC

November 13, 2017

Thirty years ago, parallel computing was coming of age. A bitter battle began between stalwart vector computing supporters and advocates of various approaches to parallel computing. IBM skeptic Alan Karp, reacting to announcements of nCUBE’s 1024-microprocessor system and Thinking Machines’ 65,536-element array, made a public $100 wager that no one could get a parallel speedup of over 200 on real HPC workloads. Read more…

By John Gustafson & Lenore Mullin

Flipping the Flops and Reading the Top500 Tea Leaves

November 13, 2017

The 50th edition of the Top500 list, the biannual publication of the world’s fastest supercomputers based on public Linpack benchmarking results, was released Read more…

By Tiffany Trader

V100 Good but not Great on Select Deep Learning Aps, Says Xcelerit

November 27, 2017

Wringing optimum performance from hardware to accelerate deep learning applications is a challenge that often depends on the specific application in use. A benc Read more…

By John Russell

SC17: Singularity Preps Version 3.0, Nears 1M Containers Served Daily

November 1, 2017

Just a few months ago about half a million jobs were being run daily using Singularity containers, the LBNL-founded container platform intended for HPC. That wa Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This