Baidu Researcher Pushes GPU Scalability for Deep Learning

By Tiffany Trader

June 20, 2016

Editor’s Note: While Andrew Ng, chief scientist at Baidu was delivering his ISC keynote this morning on how HPC is supercharging AI, his colleague Greg Diamos, research scientist at Baidu’s Silicon Valley AI Lab, was preparing to present a paper on GPU-based deep learning at the 33rd International Conference on Machine Learning in New York.

Greg Diamos, senior researcher, Silicon Valley AI Lab, Baidu, is on the front lines of the reinvigorated frontier of machine learning. Before joining Baidu, Diamos was in the employ of NVIDIA, first as a research scientist and then an architect (for the GPU streaming multiprocessor and the CUDA software). Given this background, it’s natural that Diamos’ research is focused on advancing breakthroughs in GPU-based deep learning. Ahead of the paper he is presenting, Diamos answered questions about his research and his vision for the future of machine learning.

HPCwire: How would you characterize the current era of machine learning?

Greg Diamos Baidu headshot
Greg Diamos

Diamos: There are two strong forces in machine learning. One is big data, or the availability of massive data sets enabled by the growth of the internet. The other is deep learning, or the discovery of how to train very deep artificial neural networks effectively. The combination of these two forces is driving fast progress on many hard problems.

HPCwire: There’s a lot of excitement for deep learning – is it warranted and what would you say to those who say they aren’t on-board yet?

Diamos: It is warranted. Deep learning has already tremendously advanced the state of the art of real world problems in computer vision and speech recognition. Many problems in these domains and others that were previously considered too difficult are now within reach.

HPCwire: What’s the relationship between machine learning and high-performance computing and how is it evolving?

Diamos: The ability to train deep artificial neural networks effectively and the abundance of training data has pushed machine learning into a compute bound regime, even on the fastest machines in the world. We find ourselves in a situation where faster computers directly enable better application level performance, for example, better speech recognition accuracy.

HPCwire: So you’re presenting a paper at the 33rd International Conference on Machine Learning in New York today. The title is Persistent RNNs: Stashing Recurrent Weights On-Chip. First, can you explain what Recurrent Neural Networks are and what problems they solve?

Diamos: Recurrent neural networks are functions that transform sequences of data – for example, they can transform an audio signal into a transcript, or a sentence in English into a sentence in Chinese. They are similar to other deep artificial neural networks, with the key difference being that they operate on sequences (e.g. an audio signal of arbitrary length) instead of fixed sized data (e.g. an image of fixed dimensions).

Figure 5 Baidu Diamos ICML 2016HPCwire: Can you provide an overview of your paper? What problem(s) did you set out to solve and what was achieved?

Diamos: It turns out that although deep learning algorithms are typically compute bound, we have not figured out how to train them at the theoretical limits of performance of large clusters, and there is a big opportunity remaining. The difference between the sustained performance of the fastest RNN training system that we know about at Baidu, and the theoretical peak performance of the fastest computer in the world is approximately 2500x.

The goal of this work is to improve the strong scalability of training deep recurrent neural networks in an attempt to close this gap. We do this by making GPUs 30x more efficient on smaller units of work, enabling better strong scaling. We achieve a 16x increase in strong scaling, going from 8 GPUs without our technique to 128 GPUs with it. Our implementation sustains 28 percent of peak floating point throughput at 128 GPUs over the entire training run, compared to 31 percent on a single GPU.

HPCwire: GPUs are closely associated with machine learning, especially deep neural networks. How important have GPUs been to your research and development at Baidu?

Diamos: GPUs are important for machine learning because they have high computational throughput, and much of machine learning, deep learning in particular, is compute limited.

HPCwire: And a related question – what does the scalability offered by dense servers all the way up to large clusters enable for deep learning and other machine learning workloads?

Diamos: Scaling training to large clusters enables training bigger neural networks on bigger datasets than are possible with any other technology.

HPCwire: What are you watching in terms of other processing architecture (Xeon Phi Knights Landing, FPGAs, ASICs, DSPs, ARM and so forth)?

Diamos: In the five year timeframe I am watching two things: peak floating point throughput and software support for deep learning. So far GPUs are leading both categories, but there is certainly room for competition. If other processors want to compete in this space, they need to be serious about software, in particular, releasing deep learning primitive libraries with simple C interfaces that achieve close to peak performance. Looking farther ahead to the limits of technology scaling, I hope that a processor is developed in the next two decades that enables deep learning model training at 10 PFLOP/s in 300 Watts, and 150 EFLOP/s in 25 MWatts.

HPCwire: Baidu is using machine learning for image recognition, speech recognition, the development of autonomous vehicles and more, what does the research you’ve done here help enable?

Diamos: This research allows us to train our models faster, which so far has translated into better application level performance, e.g. speech recognition accuracy. I think that this is an important message for people who work in high performance computing systems. It provides a clear link between the work that they do to build faster systems and our ability to apply machine learning to important problems.

Relevant links:

ICML paper: Persistent RNNs: Stashing Recurrent Weights On-Chip: http://jmlr.org/proceedings/papers/v48/diamos16.pdf

Video about Greg’s work at Baidu: https://www.youtube.com/watch?v=JkXbTOt_JxE

Subscribe to HPCwire's Weekly Update!

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

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire