CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

By Rob Farber

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learning. The CERN team demonstrated that AI-based models have the potential to act as orders-of-magnitude-faster replacements for computationally expensive tasks in simulation, while maintaining a remarkable level of accuracy.

Dr. Federico Carminati (Project Coordinator, CERN) points out, “This work demonstrates the potential of ‘black box’ machine-learning models in physics-based simulations.”

A poster describing this work was awarded the prize for best poster in the category ‘programming models and systems software’ at ISC’18. This recognizes the importance of the work, which was carried out by Dr. Federico Carminati, Gul Rukh Khattak, and Dr. Sofia Vallecorsa at CERN, as well as Jean-Roch Vlimant at Caltech. The work is part of a CERN openlab project in collaboration with Intel Corporation, who partially funded the endeavor through the Intel Parallel Computing Center (IPCC) program.

Widespread potential impact for simulation

The world-wide impact for High-Energy Physics (HEP) scientists could be substantial, as outlined by the CERN poster, which points out that ”Currently, most of the LHC’s worldwide distributed CPU budget — in the range of half a million CPU-years equivalent — is dedicated to simulation.” Speeding up the most time-consuming simulation tasks (e.g., high-granularity calorimeters, which are components in a detector that measure the energy of particles[i]) will help scientists better utilize these allocations. The following are comparative results obtained by the CERN team in the time to create an electron shower, once the AI model has been fully trained:

Figure 1: Comparative runtime to create an electron shower of the machine-learning method (e.g. 3d GAN) vs. the full Monte-Carlo simulation (Image courtesy CERN)

Dr. Sofia Vallecorsa points out that the CPU based runtime is important as nearly all of the Geant user base runs on CPUs. Vallecorsa is a CERN physicist who was also highlighted in the CERN article Coding has no gender.

As scientists consider future CERN experiments, Vallecorsa observes, “Given future plans to upgrade CERN’s Large Hadron Collider, dramatically increasing particle collision rates, frameworks like this have the potential to play an important role in ensuring data rates remain manageable.”

This kind of approach could help to realize similar orders-of-magnitude-faster speedups for computationally expensive simulation tasks used in a range of fields.

Vallecorsa explains that the data distributions coming from the trained machine-learning model are remarkably close to the real and simulated data.

A big change in thinking

The team demonstrated that “energy showers” detected by calorimeters can be interpreted as a 3D image[ii]. The process is illustrated in the following figure. The team adopted this approach from the machine-learning community as deep-learning convolutional neural networks are heavily utilized when working with images.

Figure 2: Schematic from the poster showing how a single particle creates an electron shower that can be viewed as an image (Courtesy CERN)

Use of GANS

The CERN team decided to train Generative Adversarial Networks (GANs) on the calorimeter images. GANs are particularly suited to act as a replacement for the expensive Monte Carlo methods used in HEP simulations as they generate realistic samples for complicated probability distributions, allow multi-modal output, can do interpolation, and are robust against missing data.

The basic idea is easy to understand: train a Generator (G) to create the calorimeter image with sufficient accuracy to trick a discriminator (D) which tries to identify artificial samples from the generator compared to real samples from the Monte Carlo simulation. G reproduces the data distribution starting from random noise. D estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. A high-level illustration of the GAN is provided below.

Figure 3: High-level view of training a GAN (image from https://medium.com/@devnag/generative-adversearial-networks-in-50-lines-of-code-pytorch-e81b79659e3f)

Even though the description is simple, 3D GANs are unfortunately not “out-of-the-box” networks, which meant the training of the model was non-trivial.

Results

After detailed validation of the trained GAN, there was “remarkable” agreement between the images from the generator and the Monte-Carlo images. This type of approach could potentially be beneficial in other fields where Monte Carlo simulation is used.

More specifically, the CERN team compared high level quantities (e.g., energy shower shapes) and detailed calorimeter response (e.g., single cell response) between the trained generator and the standard Monte Carlo. The CERN team describes the agreement, which is within a few percent, as “remarkable” in their poster.

Visually this agreement can be seen by how closely the blue (real data) and red lines (GAN generated data) overlap in the following results reported in the poster.

Figure 4: Transverse shower shape for 100-500 GeV pions. Red is the GAN data while blue represents the real data. (Image courtesy CERN)

 

Figure 5: Longitudinal shower shape for 400 GeV electron (Image courtesy CERN)

 

Figure 6: Longitudinal shower shape for 100 GeV electron (Image courtesy CERN)

Vallecorsa summarizes these results by stating, “The agreement between the images generated by our model and the Monte Carlo images has been beyond our expectations. This demonstrates that this is a promising avenue for further investigation.”

CERN openlab

The CERN team plans to test performance using FPGAs and other integrated accelerator technologies. FPGAs are known to deliver lower latency and higher inferencing performance than both CPUs and GPUs[iii]. The CERN group also intends to test several deep learning techniques in the hope of achieving a yet greater speedup with respect to Monte Carlo techniques, and ensuring this approach covers a range of detector types, which CERN believes is key to future projects.

This research is being carried out through a CERN openlab project. CERN openlab is a public-private partnership through which CERN collaborates with leading ICT companies to drive innovation in cutting-edge ICT solutions for its research community. Intel has been a partner in CERN openlab since it was first established in 2001. Dr. Alberto Di Meglio (Head of CERN openlab) observes, “At CERN, we’re always interested in exploring upcoming technologies that can help researchers to make new ground-breaking discoveries about our universe. We support this through joint R&D projects with our collaborators from industry, and by making cutting-edge technologies available for evaluation by researchers at CERN.”

Summary

The HPC modeling and simulation community now has a promising path forward to exploit the benefits of machine learning. The key, as demonstrated by CERN, is that the machine-learning-generated distribution needs to be indistinguishable from other high-fidelity methods in physics-based simulations.

The motivation is straightforward: (1) orders of magnitude faster performance, (2) efficient CPU implementations, and (3) this approach could enable the use of other new technologies such as FPGAs that may significantly improve performance.

Additional References

Rob Farber is a global technology consultant and author with an extensive background in HPC and in machine learning technology that he applies at national labs and commercial organizations on a variety of problems including challenges in high energy physics. Rob can be reached at [email protected]

[i] http://cds.cern.ch/record/2254048#

[ii] ibid

[iii] https://medium.com/syncedreview/deep-learning-in-real-time-inference-acceleration-and-continuous-training-17dac9438b0b

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!

Senegal Prepares to Take Delivery of Atos Supercomputer

January 16, 2019

In just a few months time, Senegal will be operating the second largest HPC system in sub-Saharan Africa. The Minister of Higher Education, Research and Innovation Mary Teuw Niane made the announcement on Monday (Jan. 14 Read more…

By Tiffany Trader

Google Cloud Platform Extends GPU Instance Options

January 16, 2019

If it's Nvidia GPUs you're after to power your AI/HPC/visualization workload, Google Cloud has them, now claiming "broadest GPU availability." Each of the three big public cloud vendors has by turn touted the latest and Read more…

By Tiffany Trader

A Big Data Journey While Seeking to Catalog our Universe

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterized as transforming data into insights – which is exactly wh Read more…

By James Reinders

HPE Extreme Performance Solutions

HPE Systems With Intel Omni-Path: Architected for Value and Accessible High-Performance Computing

Today’s high-performance computing (HPC) and artificial intelligence (AI) users value high performing clusters. And the higher the performance that their system can deliver, the better. Read more…

IBM Accelerated Insights

Resource Management in the Age of Artificial Intelligence

New challenges demand fresh approaches

Fueled by GPUs, big data, and rapid advances in software, the AI revolution is upon us. Read more…

STAC Floats ML Benchmark for Financial Services Workloads

January 16, 2019

STAC (Securities Technology Analysis Center) recently released an ‘exploratory’ benchmark for machine learning which it hopes will evolve into a firm benchmark or suite of benchmarking tools to compare the performanc Read more…

By John Russell

A Big Data Journey While Seeking to Catalog our Universe

January 16, 2019

It turns out, astronomers have lots of photos of the sky but seek knowledge about what the photos mean. Sound familiar? Big data problems are often characterize Read more…

By James Reinders

STAC Floats ML Benchmark for Financial Services Workloads

January 16, 2019

STAC (Securities Technology Analysis Center) recently released an ‘exploratory’ benchmark for machine learning which it hopes will evolve into a firm benchm 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’s New Global Weather Forecasting System Runs on GPUs

January 9, 2019

Anyone who has checked a forecast to decide whether or not to pack an umbrella knows that weather prediction can be a mercurial endeavor. It is a Herculean task: the constant modeling of incredibly complex systems to a high degree of accuracy at a local level within very short spans of time. Read more…

By Oliver Peckham

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

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

HPCwire Awards Highlight Supercomputing Achievements in the Sciences

January 3, 2019

In November at SC18 in Dallas, HPCwire Readers’ and Editors’ Choice awards program commemorated its 15th year of honoring achievement in HPC, with categories ranging from Best Use of AI to the Workforce Diversity Leadership Award and recipients across a wide variety of industrial and research sectors. Read more…

By the Editorial Team

White House Top Science Post Filled After Two-Year Vacancy

January 3, 2019

Half-way into Trump's term, the Senate has confirmed a director for the Office of Science and Technology Policy (OSTP), the agency that coordinates science poli Read more…

By Tiffany Trader

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

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

AMD Sets Up for Epyc Epoch

November 16, 2018

It’s been a good two weeks, AMD’s Gary Silcott and Andy Parma told me on the last day of SC18 in Dallas at the restaurant where we met to discuss their show news and recent successes. Heck, it’s been a good year. Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

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

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

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

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

Nvidia’s Jensen Huang Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, product photos, and even a beautiful image of supernovae... Read more…

By John Russell

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

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

Intel Confirms 48-Core Cascade Lake-AP for 2019

November 4, 2018

As part of the run-up to SC18, taking place in Dallas next week (Nov. 11-16), Intel is doling out info on its next-gen Cascade Lake family of Xeon processors, specifically the “Advanced Processor” version (Cascade Lake-AP), architected for high-performance computing, artificial intelligence and infrastructure-as-a-service workloads. Read more…

By Tiffany Trader

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

Microsoft to Buy Mellanox?

December 20, 2018

Networking equipment powerhouse Mellanox could be an acquisition target by Microsoft, according to a published report in an Israeli financial publication. Microsoft has reportedly gone so far as to engage Goldman Sachs to handle negotiations with Mellanox. 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

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