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 industy updates delivered to you every week!

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, provided a brief but insightful portrait of Nvidia’s rese Read more…

By John Russell

ORNL Helps Identify Challenges of Extremely Heterogeneous Architectures

March 21, 2019

Exponential growth in classical computing over the last two decades has produced hardware and software that support lightning-fast processing speeds, but advancements are topping out as computing architectures reach thei Read more…

By Laurie Varma

Interview with 2019 Person to Watch Jim Keller

March 21, 2019

On the heels of Intel's reaffirmation that it will deliver the first U.S. exascale computer in 2021, which will feature the company's new Intel Xe architecture, we bring you our interview with our 2019 Person to Watch Jim Keller, head of the Silicon Engineering Group at Intel. Read more…

By HPCwire Editorial Team

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

Insurance: Where’s the Risk?

Insurers are facing extreme competitive challenges in their core businesses. Property and Casualty (P&C) and Life and Health (L&H) firms alike are highly impacted by the ongoing globalization, increasing regulation, and digital transformation of their client bases. Read more…

What’s New in HPC Research: TensorFlow, Buddy Compression, Intel Optane & More

March 20, 2019

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, Read more…

By John Russell

At GTC: Nvidia Expands Scope of Its AI and Datacenter Ecosystem

March 19, 2019

In the high-stakes race to provide the AI life-cycle solution of choice, three of the biggest horses in the field are IBM, Intel and Nvidia. While the latter is only a fraction of the size of its two bigger rivals, and has been in business for only a fraction of the time, Nvidia continues to impress with an expanding array of new GPU-based hardware, software, robotics, partnerships and... Read more…

By Doug Black

Nvidia Debuts Clara AI Toolkit with Pre-Trained Models for Radiology Use

March 19, 2019

AI’s push into healthcare got a boost yesterday with Nvidia’s release of the Clara Deploy AI toolkit which includes 13 pre-trained models for use in radiolo Read more…

By John Russell

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

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

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

Quick Take: Trump’s 2020 Budget Spares DoE-funded HPC but Slams NSF and NIH

March 12, 2019

U.S. President Donald Trump’s 2020 budget request, released yesterday, proposes deep cuts in many science programs but seems to spare HPC funding by the Depar Read more…

By John Russell

Nvidia Wins Mellanox Stakes for $6.9 Billion

March 11, 2019

The long-rumored acquisition of Mellanox came to fruition this morning with GPU chipmaker Nvidia’s announcement that it has purchased the high-performance net Read more…

By Doug Black

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

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

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

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

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

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

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

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

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

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

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 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

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

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

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

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

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