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!

Is Data Science the Fourth Pillar of the Scientific Method?

April 18, 2019

Nvidia CEO Jensen Huang revived a decade-old debate last month when he said that modern data science (AI plus HPC) has become the fourth pillar of the scientific method. While some disagree with the notion that statistic Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing the bounds of what's possible in business and science, in w Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Google Open Sources TensorFlow Version of MorphNet DL Tool

April 18, 2019

Designing optimum deep neural networks remains a non-trivial exercise. “Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expens Read more…

By John Russell

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Bridging HPC and Cloud Native Development with Kubernetes

The HPC community has historically developed its own specialized software stack including schedulers, filesystems, developer tools, container technologies tuned for performance and large-scale on-premises deployments. Read more…

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the General Chair of SC19 -- is an ACM Distinguished Scientist. Read more…

By HPCwire Editorial Team

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the Read more…

By HPCwire Editorial Team

Intel Gold U-Series SKUs Reveal Single Socket Intentions

April 18, 2019

Intel plans to jump into the single socket market with a portion of its just announced Cascade Lake microprocessor line according to one media report. This isn Read more…

By John Russell

BSC Researchers Shrink Floating Point Formats to Accelerate Deep Neural Network Training

April 15, 2019

Sometimes calculating solutions as precisely as a computer can wastes more CPU resources than is necessary. A case in point is with deep learning. In early stag Read more…

By Ken Strandberg

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

Nvidia Doubles Down on Medical AI

April 9, 2019

Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery. The GPU leader said Monday it will collaborate with the American College of Radiology (ACR) to provide clinicians with its Clara AI tool kit. The partnership would allow radiologists to leverage AI techniques for diagnostic imaging using their own clinical data. Read more…

By George Leopold

Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions

April 9, 2019

With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workfl Read more…

By John Russell

AI and Enterprise Datacenters Boost HPC Server Revenues Past Expectations – Hyperion

April 9, 2019

Building on the big year of 2017 and spurred in part by the convergence of AI and HPC, global revenue for high performance servers jumped 15.6 percent last year Read more…

By Doug Black

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

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The 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

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s 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

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro 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

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

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

Air Force Research Laboratory Unveils First Shared, Classified DoD HPC Capability

February 28, 2019

In a ceremony on Tuesday, the Air Force Research Laboratory unveiled four new computing clusters, providing the capability for what it is calling the first-ever 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