Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

By John Russell

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking system for deep learning, [and] provides software infrastructure to utilize the most powerful supercomputers for extreme-scale workloads.” The researchers used CSCS Piz Daint supercomputer in developing the benchmark, have made the code freely available on GitHub, and last week published a detailed analysis of their approach (A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning)[i].

“Deep500 [is] the first customizable bench- marking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techniques,” write the researchers. “The key idea behind Deep500 is its modular design, where deep learning is factorized into four distinct levels: operators, network processing, training, and distributed training. Our evaluation illustrates that Deep500 is customizable (enables combining and benchmarking different deep learning codes) and fair (uses carefully selected metrics). Moreover, Deep500 is fast (incurs negligible overheads), verifiable (offers infrastructure to analyze correctness), and reproducible.”

The paper is fascinating not only for it hands-on analysis of DL benchmarking challenges and how-to-use Deep500 elements but also for its comparison of Deep500 with existing benchmarks such as MLPerf. Posting the work fulfills a promise made by ETH researchers Tal Ben-Nun and Torsten Hoefler at SC18 at the Deep500 BOF (see HPCwire article, The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning). Presumably the next step will be actively soliciting feedback from the community and enticing users to try out the new tool set.

Ben-Nun and Hoefler told HPCwire in an email today, “We developed the modular benchmarking approach as a basis for a reproducible measurement infrastructure. It will be used to establish the competition on various levels. Our main focus now is looking for scientific problems to train for the competition, and any input from the community is welcome. You can contact us at [email protected]

Among other things, that sounds like plans for a Deep500 list (à la Top500) are firming up; one wonders when, SC19 perhaps?

Given the rapid adoption of DL in HPC, efforts to create reliable, meaningful DL benchmarking tools have been ratcheting up. Deep500 is the only system, say the authors, that focuses on performance, accuracy, and convergence, while simultaneously offering a wide spectrum of metrics and criteria for benchmarking, enabling customizability of design, and considering a diversity of workloads (benchmark comparison table below,  click to enlarge).

Ben-Nun and colleagues do a nice job capturing the challenge of attempting to build reasonable DL benchmarking tools.

Excerpt: “Recent years saw an unprecedented growth in the number of approaches, schemes, algorithms, applications, platforms, and frameworks for DL. First, DL computations can aim at inference or training. Second, hardware platforms can vary significantly, including CPUs, GPUs, or FPGAs. Third, operators can be computed using different methods, e.g., im2col or Winograd in convolutions. Next, DL functionalities have been deployed in a variety of frameworks, such as TensorFlow or Caffe. These functionalities may incorporate many parallel and distributed optimizations, such as data, model, and pipeline parallelism. Finally, DL workloads are executed in wildly varying environments, such as mobile phones, multi-GPU clusters, or large-scale supercomputers.”

No single metric, for example, is adequate note the researchers: “On one hand, some metrics may simply be too detailed, for example the number of cache misses in 2D convolution implemented in TensorFlow or Caffe2. Due to the sheer complexity of such frameworks, this metric would probably not provide useful insights in potential performance regressions. On the other hand, other metrics may be too generic, for example simple runtime does not offer any meaningful details and does not relate to accuracy. Thus, one must select metrics that find the right balance between accuracy and genericness. In Deep500, we offer carefully selected metrics, considering performance, correctness, and convergence in shared- as well as distributed-memory environments.”

Deep500 is based on the following five pillars (description take from the paper):

  • “Customizability indicates that Deep500 enables benchmarking of arbitrary combinations of DL elements, such as various frameworks running on different platforms, and executing custom algorithms. To achieve this, we design Deep500 to be a meta-framework that can be straightforwardly extended to benchmark any DL code. Table I illustrates how various DL frameworks, libraries, and frontends can be integrated in Deep500 to enable easier and faster DL programming.
  • “Metrics indicates that Deep500 embraces a complex nature of DL that, unlike benchmarks such as Top500, makes a single number such as FLOPS an insufficient measure. To this end, we propose metrics that consider the accuracy-related aspects of DL (e.g., time required to ensure a specific test-set accuracy) and performance-related issues (e.g., communication volume).
  • “Performance means that Deep500 is the first DL benchmarking infrastructure that can be integrated with parallel and distributed DL codes.
  • “Validation indicates that Deep500 provides infrastructure to ensure correctness of aspects such as convergence.
  • “Reproducibility as specified in recent HPC initiatives[ii]to help developing reproducible DL codes.”

The core enabler in Deep500, write the researchers, is the modular design that groups all the required functionalities into four levels: 1 Operators; 2 Network Processing; 3 Training; and 4 Distributed Training. Each level provides relevant abstractions, interfaces, reference implementations, validation procedures, and metrics. “We illustrate levels and their relationships in Fig. 1 (shown higher in article) and the full design of the Deep500 meta-framework is shown in Fig. 3 (an eye test for sure but worth examining, click to enlarge).”

The researchers emphasize that, “The Deep500 meta-framework is a benchmarking environment, and as such it is not meant to be a DL framework that provides optimized implementations of its own. Rather, Deep500 assumes high-performance frameworks exist. By abstracting the high-level aspects of DL (e.g., data loading) in a platform-agnostic manner, Deep500 enables the measurement and development of various metrics (performance, accuracy) in the different contexts of DL and distributed DL.

“By taking the white-box approach, the user roles that Deep500 enables can be of a benchmark evaluator, or of an experimental scientist. In the former, one might use Deep500 and the various built-in metrics to choose hardware (or soft- ware) that performs best given a target workload. The latter role can use metrics and automatic integration with existing frameworks in order to empirically evaluate new operators, training algorithms, or communication schemes for DL. Since Deep500 provides reference code for nearly every concept, new methods can be validated against existing verified (yet slow) implementations.”

Deep500 is the only system that focuses on performance, accuracy, and convergence, while simultaneously offering a wide spectrum of metrics and criteria for benchmarking, contend the authors. I will be interesting to monitor quickly the new benchmark gets tested. There is a fair amount of detail in the paper which is nevertheless a reasonably quick read and good resource.

NOTES

[i]A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning, Tal Ben-Nun, Maciej Besta, Simon Huber, Alexandros Nikolaos Ziogas, Daniel Peter, Torsten Hoefler, https://arxiv.org/pdf/1901.10183.pdf

[ii]T. Hoefler and R. Belli, “Scientific benchmarking of parallel computing systems,” in SC, 2015.

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!

Researchers Use Supercomputing to Study Links Between Hurricanes and Climate Change

July 19, 2019

As climate change looms, researchers are scrambling to answer the question of how a warming planet will affect the frequency and severity of already-deadly hurricanes. Now, a team of researchers from the University of Il Read more…

By Oliver Peckham

San Diego Supercomputer Center to Welcome ‘Expanse’ Supercomputer in 2020

July 18, 2019

With a $10 million dollar award from the National Science Foundation, San Diego Supercomputer Center (SDSC) at the University of California San Diego is procuring a new supercomputer, called Expanse, to be deployed next Read more…

By Staff report

Informing Designs of Safer, More Efficient Aircraft with Exascale Computing

July 18, 2019

During the process of designing an aircraft, aeronautical engineers must perform predictive simulations to understand how airflow around the plane impacts flight characteristics. However, modeling the complexities and su Read more…

By Rob Johnson

HPE Extreme Performance Solutions

Bring the Combined Power of HPC and AI to Your Business Transformation

A growing number of commercial businesses are implementing HPC solutions to derive actionable business insights, to run higher performance applications and to gain a competitive advantage. Read more…

IBM Accelerated Insights

Smarter Technology Revs Up Red Bull Racing

In 21st century business, companies that effectively leverage their information resources – thrive. As it turns out, the same is true in Formula One racing. Read more…

How Fast is Your Rubik Solver; This One’s Probably Faster

July 18, 2019

In the race to solve Rubik’s Cube, the time-to-finish keeps shrinking. This year Philipp Weyer from Germany won the 10th World Cube Association (WCA) Championship held in Melbourne, Australia, with a 6.74-second perfo Read more…

By John Russell

Informing Designs of Safer, More Efficient Aircraft with Exascale Computing

July 18, 2019

During the process of designing an aircraft, aeronautical engineers must perform predictive simulations to understand how airflow around the plane impacts fligh Read more…

By Rob Johnson

Intel Debuts Pohoiki Beach, Its 8M Neuron Neuromorphic Development System

July 17, 2019

Neuromorphic computing has received less fanfare of late than quantum computing whose mystery has captured public attention and which seems to have generated mo Read more…

By John Russell

Goonhilly Unveils New Immersion-Cooled Platform, Doubles Down on Sustainability Mission

July 16, 2019

Goonhilly Earth Station has opened its new datacenter – an enhancement to its existing tier 3 facility – in Cornwall, England, touting an ambitious commitme Read more…

By Oliver Peckham

ISC19 Cluster Competition: Application Results, Finally!

July 15, 2019

Our exhaustive coverage of the ISC19 Student Cluster Competition continues as we discuss the application scores below. While the scores were typically high, som Read more…

By Dan Olds

Nvidia Expands DGX-Ready AI Program to 19 Countries

July 11, 2019

Nvidia’s DGX-Ready Data Center Program, announced in January and designed to provide colo and public cloud-like options to access the company’s GPU-powered Read more…

By Doug Black

Argonne Team Makes Record Globus File Transfer

July 10, 2019

A team of scientists at Argonne National Laboratory has broken a data transfer record by moving a staggering 2.9 petabytes of data for a research project.  The data – from three large cosmological simulations – was generated and stored on the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF)... Read more…

By Oliver Peckham

Nvidia, Google Tie in Second MLPerf Training ‘At-Scale’ Round

July 10, 2019

Results for the second round of the AI benchmarking suite known as MLPerf were published today with Google Cloud and Nvidia each picking up three wins in the at Read more…

By Tiffany Trader

Applied Materials Embedding New Memory Technologies in Chips

July 9, 2019

Applied Materials, the $17 billion Santa Clara-based materials engineering company for the semiconductor industry, today announced manufacturing systems enablin Read more…

By Doug Black

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

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

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... 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

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

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

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

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

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

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

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep 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

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