Data Management at NERSC in the Era of Petascale Deep Learning

By Rob Farber

May 9, 2018

Now that computer scientists at Lawrence Berkeley National Laboratory’s National Energy Research Scientific Computing Center (NERSC) have demonstrated 15 petaflops deep-learning training performance on the Cray Cori supercomputer, the NERSC staff is working to address the data management issues that arise when running production deep-learning codes at such scale. The existing deep learning tools were not designed to efficiently ingest or manage the terabyte- to petabyte-sized deep-learning training sets that scientists can now use on this leadership class supercomputer. “Enabling the NERSC user community to perform deep learning at scale on Cori,” Quincey Koziol (Staff, Berkeley Lab) observes, “means scientists can use deep learning as part of their leading-edge scientific efforts.”

Thus NERSC staff are working to break new ground in adapting existing deep-learning frameworks to run efficiently at scale on thousands of nodes while giving researchers the ability to create and manage training sets containing tens to hundreds of terabytes of data in a portable fashion. For these datasets, it is imperative that they are formatted so Cori can ingest them efficiently at runtime.

Appreciating the magnitude of the petascale data management problem

To appreciate the magnitude of the petascale data management problem, consider that the 9,600 Intel Xeon Phi nodes used in the 15 petaflops deep learning training performance contained over a petabyte of main memory. (Specifically, 921.6 terabytes of DDR4 RAM and 153.6 terabytes of high-bandwidth 3D stacked memory.)

The first petascale training runs on the Cray XC40 Cori supercomputer focused on scalability, which left lots of room for groundbreaking research in training on really big datasets. Kurth, et.al. noted in their paper “Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data” that the climate dataset contained 15 TB of data and the HEP (High Energy Physics) data contained 10 million images. With more than a petabyte of RAM contained in 9,600 nodes, Cori can obviously utilize much larger data sets.

Not so obvious are the asynchronous data management issues that crop up after the data has been ingested and the training run has started. These asynchronous methods use prefetching and lots of communications, so per-node memory usage and network performance are critical to running at the petascale.

Without getting too technical, the 15 petaflops deep learning performance was achieved using a hybrid, asynchronous implementation of the SGD (stochastic gradient descent) numerical optimization method. SGD is a common numerical method used by popular packages such as Caffe (used in the 15 petaflops Cori runs) and TensorFlow.

Thorsten Kurth (Application Performance Specialist, NERSC) observes that, “Tensorflow is the most widely used framework and is therefore a primary optimization target at the moment, but the deep learning software world changes rapidly so that sustainable implementations are necessary. Thus it makes sense to create libraries of optimized kernels that can be used by many deep learning frameworks. This same idea can be used to create methods for the data feeding/IO operations.” These optimized libraries can then be rapidly adopted to new upcoming frameworks such as pytorch and mxnet, Kurth observes.

Addressing the challenges

Given the popularity of TensorFlow, the NERSC team is working to adapt TensorFlow to run at scale on Cori. The main challenges, Koziol observes, are threefold:

  • TensorFlow uses text or binary images for input rather than HDF5 or another data format typically used by HPC scientists. Koziol and NERSC are currently integrating HDF5 with TensorFlow.
  • TensorFlow uses a client-server model rather than MPI, which is the typical communications package for scientific applications that run on HPC systems. This means that there are no collective operations inside TensorFlow, which can cause performance issues.
  • TensorFlow uses an asynchronous training that is very loosely coupled, which means data prefetching is critical to prevent performance from suffering due to data starvation. Conversely, prefetching increases the per-node memory consumption, so an appropriate balance must be struck to prefetch “just enough and no more.” Finding that ideal balance without overburdening any node or set of nodes with data in a large (think hundred- or thousand-node) training run is a fertile research area as NERSC brings TensorFlow into a new scaling realm.

HDF5 integration

The data management aspects of deep learning are often overlooked as researchers work to speed training and find the right ANN (Artificial Neural Network) architecture(s) to solve complex problems.

In reality, much of a data scientist’s time is spent creating a clean, representative dataset for training. The data challenge becomes that much larger and unwieldly when creating data for a petascale, deep-learning-capable, leadership-class supercomputer like Cori. Data management is sometimes referred to as the Victorian Era Child of the 21st Century – to be seen and not heard. Unfortunately, the challenges associated with Cori-sized datasets simply cannot be ignored.

Prior to joining NERSC, Koziol was director of core software and high-performance computing at the HDF Group, where he spent 11 years developing the HDF5 I/O middleware package and overseeing the group’s HPC development efforts. This makes Koziol a natural to incorporate the versatile HDF5 data model into TensorFlow. HDF5 is a Hierarchical Data Format that can represent very large, complex numerical datasets along with their metadata in a portable format that can be moved between machines. HDF5 1.10.2 is the current, latest version. The specification is open, and the tools are open source. Development of HDF5 is done by the HDF Group, a nonprofit corporation.

The benefits of HDF5 integration into TensorFlow means that scientists can use tools and a data format that have been developed over decades to enable scientists to portably manage even the largest scientific datasets. Portability means the data preprocessing and data cleaning can happen on remote systems using familiar open-source tools and frameworks. Once ready, the data can be moved onto Cori and ingested into TensorFlow. According to Koziol, this helps address the challenge of “How do we get data into the system fast enough?”

Those who are interested can find the scripts and one example of HDF5 integration in the NERSC cori-tf-distributed-examples repository on github. Specifically, https://github.com/NERSC/cori-tf-distributed-examples.

Other work in progress

NERSC is also working to address TensorFlow’s memory consumption issue and speed the collective operations. However, these are non-trivial problems that will take time. As Koziol observes, “The MPI community has been thinking about collectives for about 20 years. TensorFlow is currently only about two years old.”

Along with the per-node memory consumption challenges that must be addressed when using asynchronous training methods, researchers are also rapidly increasing the complexity of the ANNs they use to solve complex problems. Deeper and more complex ANNs utilize more parameters, which further exacerbates the memory consumed per node problem. For example, calculating the gradient for SGD in TensorFlow is becoming an issue even when running on small systems.

The NERSC team has to contend with those issues as well as prefetching and buffering of data used to support the asynchronous operations during training, so the CPU is used as effectively as possible. The large memory of the Intel Xeon Phi nodes helps, as does the fact that the data extraction and training both occur on the CPU, but finding the right configuration can be challenging, Koziol notes. “Sometimes it helps to have a small number of fat nodes,” he observes.

Steps to the future

Koziol emphasizes that deep learning workloads stress the data ingest capabilities of current supercomputers. He hopes future supercomputer designs will incorporate more features to speed data ingest for data-intensive workloads like deep learning.

Current supercomputer designs have focused on burst buffers for checkpoint/restart, a common write-optimized I/O operation used in modeling and simulation software in which the state of the simulation is quickly saved (the checkpoint operation) so that thousands of hours of compute time won’t be lost in the event of a failure. In the unlikely event that something bad does happen, the supercomputer simply reloads the last checkpoint from storage (a restart operation) and continues with the calculation once the problem is fixed. The frequency of the checkpoint operation dictates how much supercomputer runtime will be lost in the event of a failure.

As deep learning becomes an ever more common workload on supercomputers, Koziol envisions a future where supercomputers are specifically designed to support faster data ingest for deep learning and other data-intensive workloads.

Summary

The NERSC Cori supercomputer has made the training of deep-learning ANNs a member of the petascale application club. Now the NERSC data management team is working to make this petascale capability available to its users to facilitate their ability to perform leading-edge science. Incorporating HDF5 into TensorFlow is an excellent beginning to making TensorFlow a petascale-capable platform for deep learning.

Rob Farber is a global technology consultant and author with an extensive background in HPC and advanced computational technology that he applies at national labs and commercial organizations. He can be reached at [email protected]

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!

RIKEN Post-K Supercomputer Named After Japan’s Tallest Peak

May 23, 2019

May 23 -- RIKEN President Hiroshi Matsumoto announced that the successor to the K computer will be named Fugaku, another name for Mount Fuji, which is the tallest mountain peak in Japan. Supercomputer Fugaku, developed b Read more…

By Tiffany Trader

Cray’s Emerging Market & Technology Director Arti Garg Peers Around HPC/AI Corner

May 23, 2019

In her position as emerging market and technology director at Cray, Arti Garg doesn't just have a front-row seat to the future of computing, she plays an active role in making that future happen. Key to Garg's role is understanding how deep learning scientists are using state-of-the-art HPC infrastructures and figuring out how to push those limits further. Read more…

By Tiffany Trader

Combining Machine Learning and Supercomputing to Ferret out Phishing Attacks

May 23, 2019

The relentless ingenuity that drives cyber hacking is a global engine that knows no rest. Anyone with a laptop and run-of-the-mill computer smarts can buy or rent a phishing kit and start attacking – or it can be done Read more…

By Doug Black

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.

For 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

Who’s Driving Your Car?

Delivering a fully autonomous driving (AD) vehicle remains a key priority for both manufacturers and technology firms (“firms”). However, passenger safety is now a top-of-mind concern due in great part, to fatalities resulting from driving tests over the past years. Read more…

TACC’s Upgraded Ranch Data Storage System Debuts New Features, Exabyte Potential

May 22, 2019

There's a joke attributed to comedian Steven Wright that goes, "You can't have everything. Where would you put it?" Users of advanced computing can likely relate to this. The exponential growth of data poses a steep challenge to efforts for its reliable storage. For over 12 years, the Ranch system at the Texas Advanced Computing Center... Read more…

By Jorge Salazar, TACC

Cray’s Emerging Market & Technology Director Arti Garg Peers Around HPC/AI Corner

May 23, 2019

In her position as emerging market and technology director at Cray, Arti Garg doesn't just have a front-row seat to the future of computing, she plays an active role in making that future happen. Key to Garg's role is understanding how deep learning scientists are using state-of-the-art HPC infrastructures and figuring out how to push those limits further. Read more…

By Tiffany Trader

Combining Machine Learning and Supercomputing to Ferret out Phishing Attacks

May 23, 2019

The relentless ingenuity that drives cyber hacking is a global engine that knows no rest. Anyone with a laptop and run-of-the-mill computer smarts can buy or re Read more…

By Doug Black

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

HPE to Acquire Cray for $1.3B

May 17, 2019

Venerable supercomputer pioneer Cray Inc. will be acquired by Hewlett Packard Enterprise for $1.3 billion under a definitive agreement announced this morning. T Read more…

By Doug Black & Tiffany Trader

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

CCC Offers Draft 20-Year AI Roadmap; Seeks Comments

May 14, 2019

Artificial Intelligence in all its guises has captured much of the conversation in HPC and general computing today. The White House, DARPA, IARPA, and Departmen Read more…

By John Russell

Cascade Lake Shows Up to 84 Percent Gen-on-Gen Advantage on STAC Benchmarking

May 13, 2019

The Securities Technology Analysis Center (STAC) issued a report Friday comparing the performance of Intel's Cascade Lake processors with previous-gen Skylake u Read more…

By Tiffany Trader

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

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

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

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

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

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

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

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

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 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

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

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

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

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