Intel Collaborates with CERN to Support Upgraded LHC Experiments

By Sean Thielen

November 4, 2016

Editor’s note: In this contributed feature, Sean Thielen details the technical collaboration between CERN and Intel in preparation for the center’s next major upgrade, scheduled to commence in late 2018. The joint team has deployed a two-socket Xeon-FPGA (Stratix V) proof-of-concept machine that makes use of a hybrid package design and will soon be testing a newer implementation that will leverage the Arria 10 FPGA and a faster interconnect.

Much of the media attention given to the particle accelerator experiments that happen at the European Organization for Nuclear Research, known as CERN, is focused on the Large Hadron Collider (LHC). It’s no surprise, given the LHC is the world’s largest, most complex machine, unravelling some of the toughest scientific problems by accelerating particles (protons or heavy ions) and making them collide in a gigantic 27-kilometer ring. But the work that happens immediately after particles collide in the LHC is not only critical to science, it’s also quite interesting and important from a computing and data processing perspective. After all, the creation of particles or results in the LHC is only significant if scientists can quickly isolate them from millions of inconsequential signals for further study. That means ongoing advancements in trigger and data acquisition systems are essential to fully reaping the rich potential of the LHC. And, as you can imagine, the networking and computing challenges are extreme in nearly every dimension.

Historically, CERN’s trigger and data acquisition systems have relied heavily on custom technology. For the next upgrade cycle scheduled to start around the end of 2018, however, CERN engineers and scientists were aware that the scalability limitations and costs of their custom solutions could start to limit progress. This led to a new collaborative project with Intel, through CERN openlab, focused on exploring the feasibility of complementing one of the LHC’s detectors with off-the-shelf data acquisition, data movement, and data filtering technology from Intel, including an FPGA platform. If the proof of concept is successful, it could have a significant impact on the future design and efficiency of trigger systems, including the remaining LHC detectors and other scientific instruments.

The Large Hadron Collider tunnel is located 100 meters underground on the Franco-Swiss border, near Geneva. Source: CERN.
The Large Hadron Collider tunnel is located 100 meters underground on the Franco-Swiss border, near Geneva. Source: CERN

Preparing for enormous data growth

Simply phrased, the LHC fires high-energy particle beams at each other in a 27-kilometer ring. The detectors on the LHC include tracking devices that plot the trajectory of particles following collisions, as well as calorimeters that measure their energy, which helps to narrow down their identity. Niko Neufeld, a deputy project leader at CERN who works on the LHCb experiment, likens the computing challenges for the LHC experiments to solving millions of small puzzles involving up to a billion proton-proton collisions every second to retain the most interesting ones for deeper analysis.

Given the task at hand, the near-detector “online” computing challenges at CERN have always been extreme. And when CERN upgrades the LHC and detectors from late 2018 to early 2021 as part of its regular upgrade cycle, the data rates running through the various systems will jump significantly. Neufeld provides perspective on the jump: “Network scaling needs have really grown. For example, the largest networks we currently run at CERN have total bandwidth of around 800 gigabits per second. Following the upgrade work, our networks will need to carry between 40 and 50 terabits of data per second. If you compare that to a Google data center, it may not sound impressive, but for a scientific instrument it’s a huge step in terms of bandwidth.” Neufeld said that the computing challenges have also grown quite complex. “We cannot simply scale up computing by a factor of 100 because we have whatever Moore’s law gives us… We may be able to grow our computing farms by a factor of 1 or 2, but not much more. The rest has to come from more clever processing models,” he added.

The Compact Muon Solenoid (CMS) is one of two large general-purpose detectors on the LHC. The image above captures a candidate proton-proton event as a part of the CMS search for the Higgs boson. Source: CERN
The Compact Muon Solenoid (CMS) is one of two large general-purpose detectors on the LHC. The image above captures a candidate proton-proton event as a part of the CMS search for the Higgs boson. Source: CERN

According to Neufeld, the detector teams face a host of challenges in preparing for the data-rate jump. Neufeld said that the customized trigger (hardware and software) on the front-end of the existing detector system had a long and expensive refresh cycle. “The engineering resources for ASICs [application-specific integrated circuits] and the FPGAs [Field Programmable Gate Arrays] in high energy physics are limited compared to industry, and the tight integration with the detectors makes upgrades outside of our major maintenance periods impractical,” Neufeld explained. “We thought that moving to a more software-centered approach using off-the-shelf technology could greatly reduce these limitations and expand the developer base that is available in our community. Physicists are usually knowledgeable in some programming language, however, HDL [hardware description language] is a different challenge with a very long and steep learning curve.”

Olof Bärring, a deputy group leader of computing facilities at CERN, added that cost and energy considerations were also an important part of the equation. The lab needs to continue addressing greater computing, data moving and storage challenges with a more or less flat budget and within the datacenter’s existing energy envelope.

Exploring options for three critical challenges

In 2014, the former CTO of CERN openlab, Sverre Jarp, invited Intel’s Karl Solchenbach, director Exascale Labs Europe, and Steve Pawlowski, former vice president of advanced computing solutions at Intel, to discuss CERN’s technical challenges with near-detector online computing to see if any Intel technology might be useful in addressing them. During the meeting, Neufeld presented three main challenges, and the participants worked together to map technologies to the challenges.

Challenge one: real-time or near-real-time data-processing with very short (order of 10 microseconds) latencies

The Intel team considered an Intel Xeon processor and Altera Stratix V FPGA integration along with another spatial architecture, and ended up recommending the Intel Xeon processor/FPGA configuration. Neufeld says that the reasoning was that the Intel Xeon processor/FPGA configuration should allow LHC experiments to potentially replace part, and in some cases all, of the custom electronics used in the first step of online data filtering. “That meant we would be able to use off-the-shelf hardware programmed using ‘high-level’ general purpose languages instead of HDLs, which was an important step for us,” he explained.

Challenge two: very high throughput local area networks

For data transport, Intel recommended investigating the potential of Intel Omni-Path Architecture (Intel OPA) as an alternative to deep-buffer Ethernet switching. Neufeld said the main driver for considering Intel OPA over traditional approaches was cost. “It is certainly technically possible to build the network in a more traditional way, but it has become prohibitively expensive, given our budget,” he explained.

Challenge three: the need of massive data-processing for data-reduction

For the filtering of detector data in software, the new Intel Xeon Phi processor was an attractive potential solution, given that the filtering process itself is quite parallel and individual collisions in the LHC are statistically independent.

Not your average POC

Once the CERN and Intel teams agreed that the identified off-the-shelf solutions for each challenge had potential, the hard work of proving the viability of each solution in the next-generation data acquisition environment needed to begin in earnest. In 2015, CERN and Intel decided to expand their existing collaboration through CERN openlab and signed an agreement for a joint three-year project called the High Throughput Computing Collaboration (HTCC). As of writing, the HTCC project has reached the halfway point and the core team, which includes seven CERN scientists and one Intel engineer on site, has made progress in each key challenge area.

The main CERN data center. The 110,000 processor cores and 10,000 servers hosted in its three rooms run 24/7. Source: CERN
The main CERN datacenter. The 110,000 processor cores and 10,000 servers hosted in its three rooms run 24/7. Source: CERN

Networking milestones

In the networking area, Neufeld said a big part of the challenge is simply getting access to a system big enough to test software. “To properly prepare our software, we need access to complete supercomputers. Fortunately, Intel can provide access to clusters that are up to the job,” he explained. Neufeld said that the team recently had an important success while running its software on an Intel OPA cluster with more than 500 nodes. “We have already been able to achieve a full-duplex, high-throughput transit of 70 terabits per second of data flying through the cluster, so that’s already half of what we need by the time of the upgrades.”

FPGA-related milestones

When CERN updates the LHC and the experiments from late 2018 to early 2021, its detectors will support trigger-free readouts. The LHC generates up to around 1 billion collisions per second in the experiments, and the goal is to read them all. Moving forward, a flexible software-based trigger system running in a large (up to 4000 nodes) computing farm will select the interesting collisions. In the meantime, CERN is investigating which technology options are the best fit for accelerating its algorithms. For its initial FPGA proof of concept, the HTCC team deployed a two-socket Intel Xeon processor/FPGA machine, which included the following hardware connected by the Intel QuickPath Interconnect:

  • Intel Xeon CPU E5-2680 v2
  • Altera Stratix V GX A7 FPGA with 234,720 adaptive logic modules (ALMs)

The team is particularly interested in the potential compute and power efficiency gains that are possible with using OpenCL in a combined CPU and FPGA system. With respect to the FPGA platform testing, Neufeld said that the team dealt with host of technical challenges because it wanted to do a meaningful comparison of OpenCL and to get a sense of the costs using a high-level framework. “Just for illustrative purposes, it took us two weeks to set up a new kernel using OpenCL compared to three months to complete an equivalent Verilog implementation, and we had a very skilled engineer on that job,” explained Neufeld. “In the end, I think it was a good investment because we needed to prove to the electronic engineers that the new technology actually provided a less painful way to get the results we need.”

cherenkov-angle-reconstruction-intel-cern-429x
Cherenkov angle reconstruction is used for particle identification in the detectors. Based on initial tests the Xeon/FPGA machine shows promise for processing greater numbers of photons after the LHC upgrade. Source: CERN

Following a series of test cases, such as sorting and calculating the Mandlebrot fractal, to understand the potential of the Xeon/FPGA system, the HTCC team developed an FPGA fine-tuned for the rigors of RICH (ring-imaging Cherenkov) reconstruction. Only then did it begin doing LHC-specific workload analysis.

In coming months, the HTCC team will also be testing a newer system that is built using a combined Intel Xeon CPU and FPGA in a single package. It will include the new high-performance Arria 10 FPGA from Altera as well as a faster interconnect of the CPU and FPGA.

Intel Xeon Phi processor milestones

Neufeld said that testing of the Intel Xeon Phi processor platform will be a major focus of the HTCC team for the next year or so. He noted that like everyone else, the team needs to figure out how to adapt well to the new architecture and different level of parallelism. To achieve this the HTCC team has been working with Intel engineers on benchmarking and understanding the different algorithms’ implementations using Intel analysis tools, such as Intel VTune Amplifier XE and Intel Advisor XE, as well as different performance models, such as the roofline model.

“In addition to the inclusion of bootable sockets, in-package memory and high main-memory bandwidth, what is particularly interesting with Intel Xeon Phi processors is the integrated fabric and its potential to quickly distribute workloads to where they fit best. We will test both data movement aspects on the Intel Xeon Phi processor as well as the distribution of the algorithms between the Intel Xeon and Intel Xeon Phi processor using the fabric as an interconnect,” Neufeld added.

Pushing the boundaries of precision

When asked what the progress made on the data acquisition systems for the LHC might mean for wider applications, Neufeld said the value is all about greater precision for complex experiments. “To some extent, it’s just statistics. Either you really increase the amount of data and the precision by a significant factor, or you stop doing it,” Neufeld explained. “This work should lead to an important jump in precision. For example, the LHCb experiment’s online collection and analysis system currently selects just 1 million of the 40 million bunches of protons that cross in the accelerator every second, with the others being discarded based on less-precise hardware-calculated signatures. After the LHCb upgrade, the number of collisions is set to grow yet further, and we will look at all of them in the software, in order to take the best physics out of there. And since there are actually only a couple of milliseconds to do that for each collision, it’s really quite a leap forward.”t direct discussions with the Intel development team will continue to be invaluable to getting things right on projects as the team races to meet its deadline for the start of the upgrade work.

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

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

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

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

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

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

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

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