IBM Raises the Bar for Distributed Deep Learning

By Tiffany Trader

August 8, 2017

IBM is announcing today an enhancement to its PowerAI software platform aimed at facilitating the practical scaling of AI models on today’s fastest GPUs. Scaling to 256 GPUs with its new distributed deep learning (DLL) library, IBM reports that it has bested previous records set by Google and Facebook on two well-known image recognition workloads.

“This is one of the bigger breakthroughs I have seen in a while in all of the deep learning industry announcements over the last six months,” said Patrick Moorhead, president and principal analyst of Moor Insights & Strategy. “The interesting part is that it is from IBM, not one of the web giants like Google, which means it is available to enterprises from on-prem use using OpenPower hardware and PowerAI software or even through cloud provider Nimbix.”

The crux of the announcement is a new communication algorithm developed by IBM Research scientists and encapsulated as a communication library, called PowerAI DDL. The library and APIs are available today as a technical preview to Power users as part of the PowerAI version 4.0 release. Other efforts to improve multi-node communication have tended to focus on only a single deep learning framework, so it’s notable that the PowerAI DDL is being integrated into multiple frameworks. Currently TensorFlow, Caffe and Torch are supported with plans to add Chainer.

Customers who don’t have their own Power systems can access the new PowerAI software via the Nimbix Power Cloud.

“Like the hyperscalers and large enterprises, Nimbix has been working to build distributed capability into deep learning frameworks and it just so happens that what IBM is announcing is effectively a turnkey software solution that implements that in multiple frameworks,” said Nimbix CEO Steve Hebert.

“This is truly an HPC technology,” he continued. “It’s taking some of the best software components of traditional HPC and marrying those up with AI and deep learning to be able to deliver that solution. Our platform is ideally suited for scaling out in the HPC sense, very low latency for codes that get that linear scaling of problem sizes. That means for deep learning we can start to tackle enterprise-class deep learning problems basically on day one. For this to become available to any company or consumer outside of [the big hyperscalers], like Google, Baidu, etc., it really democratizes access to everybody.”

The multi-ring communication algorithm within DDL is described (see IBM Research paper) as providing a good tradeoff between latency and bandwidth, as well as being adaptable to a variety of network configurations. The full method is proprietary but section 4 of the paper provides a fairly detailed description of the library and algorithm.

The current PowerAI DDL implementation is based on Spectrum MPI. “MPI provides many needed facilities, from scheduling processes to basic communication primitives, in a portable, efficient and mature software ecosystem” state the researchers, although they add the “core API can be implemented without MPI if desired.”

To evaluate the performance of its new PowerAI Distributed Deep Learning library, IBM performed two experiments using a cluster of 64 IBM “Minsky” Power8 SL822LC servers, each equipped with four Nvidia Tesla P100 GPUs connected through Nvidia’s high-speed NVLink interconnect. The systems occupied four racks (16 nodes each), connected via InfiniBand.

IBM reports that the combination of its Power hardware and software offers better communication overhead for the Resnet-50 neural network using Caffe than what Facebook recently achieved with the Caffe2 deep learning software. The IBM Research DDL software achieved an efficiency of 95 percent using Caffe on its 256-GPU Minsky cluster whereas Facebook achieved 89 percent scaling efficiency on a 256 NVIDIA P100 GPU accelerated DGX-1 cluster using the Caffe2 framework. Implementation differences that could affect the comparison, e.g., Caffe versus Caffe2, are discussed in the IBM Research paper.

Scaling results using Caffe with PowerAI DLL to train a ResNet-50 model using the ImageNet-1K data set on 64 Power8 servers that have a total of 256 Nvidia P100 GPUs (Source: IBM)

In the second benchmark test, IBM Research reported a new image recognition accuracy of 33.8 percent for a Resnet-101 neural network trained on a very large data set (7.5 million images, part of the ImageNet-22k set). The previous record published by Microsoft in 2014 demonstrated 29.8 percent accuracy.

IBM Research fellow Hillery Hunter observed that a 4 percent increase in accuracy is a big leap forward as typical improvements in the past have been less than 1 percent.

Further, with IBM’s distributed deep learning approach, the ResNet-101 neural network model was trained in just seven hours, compared to the 10 days it took Microsoft took to train the same model. IBM reported a scaling efficiency of 88 percent.

Sumit Gupta, vice president of AI and HPC within IBM’s Cognitive Systems business unit, believes the increased speed and accuracy will be a huge boon to enterprise clients. “Part of challenge has been if it takes 16 days to train an AI model it’s not really practical,” he said. “You only have a few data scientists when you work in a large enterprise and you really need to make them productive so bringing down that 16 days to 7 hours makes data scientists much more productive.”

Certain applications are particularly time-constrained. “In security, military, fraud protection, and autonomous vehicles you often only have minutes or seconds to train a system to deal with a new exploit or problem but currently it generally takes days,” said market analyst Rob Enderle. “This effectively reduces days to hours, and provides a potential road map to get to minutes and even seconds.” It’s scenarios like these that make buying Power Systems to speed deep learning far easier to justify, he added.

The list of use cases seemingly grows longer by the day. Recommendation engines, credit card fraud detection, mortgage analysis, upsell/cross-sell to retail clients, shopping experience analysis are all getting a lot of attention from IBM’s customers.

“The giants like Microsoft and Google and others who have consumer apps, they obviously are getting on the consumer platform a lot of data all the time. So their use cases in many cases are very obvious, finding images of dogs in Google photos,” for example, said Gupta. “But we see enterprise clients have lots of data and lots of use cases they are now getting around to using these methods.”

The next step for IBM researchers is to document scaling beyond 256 GPUs as their current findings indicate that is feasible. “We don’t see a reason why the method would slow down when we double the size of the system,” said Gupta.

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!

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that have occurred about once a decade. With this in mind, the ISC Read more…

2024 Winter Classic: Texas Two Step

April 18, 2024

Texas Tech University. Their middle name is ‘tech’, so it’s no surprise that they’ve been fielding not one, but two teams in the last three Winter Classic cluster competitions. Their teams, dubbed Matador and Red Read more…

2024 Winter Classic: The Return of Team Fayetteville

April 18, 2024

Hailing from Fayetteville, NC, Fayetteville State University stayed under the radar in their first Winter Classic competition in 2022. Solid students for sure, but not a lot of HPC experience. All good. They didn’t Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use of Rigetti’s Novera 9-qubit QPU. The approach by a quantum Read more…

2024 Winter Classic: Meet Team Morehouse

April 17, 2024

Morehouse College? The university is well-known for their long list of illustrious graduates, the rigor of their academics, and the quality of the instruction. They were one of the first schools to sign up for the Winter Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pressing needs and hurdles to widespread AI adoption. The sudde Read more…

Kathy Yelick on Post-Exascale Challenges

April 18, 2024

With the exascale era underway, the HPC community is already turning its attention to zettascale computing, the next of the 1,000-fold performance leaps that ha Read more…

Software Specialist Horizon Quantum to Build First-of-a-Kind Hardware Testbed

April 18, 2024

Horizon Quantum Computing, a Singapore-based quantum software start-up, announced today it would build its own testbed of quantum computers, starting with use o Read more…

MLCommons Launches New AI Safety Benchmark Initiative

April 16, 2024

MLCommons, organizer of the popular MLPerf benchmarking exercises (training and inference), is starting a new effort to benchmark AI Safety, one of the most pre Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Instit Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips Read more…

The VC View: Quantonation’s Deep Dive into Funding Quantum Start-ups

April 11, 2024

Yesterday Quantonation — which promotes itself as a one-of-a-kind venture capital (VC) company specializing in quantum science and deep physics  — announce Read more…

Nvidia’s GTC Is the New Intel IDF

April 9, 2024

After many years, Nvidia's GPU Technology Conference (GTC) was back in person and has become the conference for those who care about semiconductors and AI. I Read more…

Google Announces Homegrown ARM-based CPUs 

April 9, 2024

Google sprang a surprise at the ongoing Google Next Cloud conference by introducing its own ARM-based CPU called Axion, which will be offered to customers in it 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…

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…

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…

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…

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…

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…

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…

Leading Solution Providers

Contributors

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…

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…

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…

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…

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

March 18, 2024

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

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

The GenAI Datacenter Squeeze Is Here

February 1, 2024

The immediate effect of the GenAI GPU Squeeze was to reduce availability, either direct purchase or cloud access, increase cost, and push demand through the roof. A secondary issue has been developing over the last several years. Even though your organization secured several racks... Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire