CERN openlab Explores New CPU/FPGA Processing Solutions

By Linda Barney

April 14, 2017

Editor’s note: In this contributed feature, Linda Barney describes the ongoing technical collaboration between CERN and Intel to develop a co-packaged Xeon/FPGA processor.

At CERN, the European Organization for Nuclear Research, physicists and engineers are probing the fundamental structure of the universe. The Large Hadron Collider (LHC), which began working in 2008, is the world’s largest and most powerful particle accelerator; it is housed in an underground tunnel at CERN. Niko Neufeld is a deputy project leader at CERN who works on the Large Hadron Collider beauty (LHCb) experiment, which explores what happened after the Big Bang that allowed matter to survive and build the Universe we inhabit today.

“CERN experiments produce an enormous amount of data with forty million proton collisions every second, which leads to primary data rates of terabits per second,” says Neufeld when speaking on a recent FPGA vs. CPU panel. “This is an enormous amount of data and there are a number of technical challenges in our work. We use a number of processing solutions including central processing units (CPUs), field-programmable gate arrays (FPGAs), and graphic processing units (GPUs), but each of these solutions have some limitations. We are collaborating with Intel in experimenting with a co-packaged Intel Xeon processor plus FPGA Quick Path Interconnect (QPI) processor in our LHCb research to try to determine which technology provides the best results.”

CERN collaborates with leading ICT companies and other research institutes through a unique public-private partnership known as ‘CERN openlab’. Its goal is to accelerate the development of cutting-edge solutions for the worldwide LHC community and wider scientific research. Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems.

Figure 1. CERN researchers shown in the Large Hadron Collider tunnel in front of the LHCb detector. Courtesy of CERN (courtesty CERN).

Introducing the co-packaged Intel CPU / FPGA Processor

Today the CPU and FPGA are used as discrete chips in a solution – with an Intel Xeon processor and an FPGA which is typically attached via a PCIe interconnect to the CPU. And the development environment is also discrete using independent development tools from Intel and tools such as OpenCL and C++. Intel is working toward a common workflow and development flow to better integrate FPGAs.

“FPGA typically uses a higher level machine abstraction language (such as Verilog and VHDL) which have a painful low-level hardware programming model for most people. As a next step, Intel has a solution that co-packages the CPU and FPGA in the same Multichip Chip Product (MCP) package to deliver higher performance and lower latency than a discrete solution,” states Bill Jenkins, Intel Senior AI Marketing Manager. The Intel MCP is supported by a cross-platform development framework like OpenCL that can be used to develop applications for both the CPU and FPGA. The Intel solution includes a fully unified intellectual property (IP) and development suite, including languages, libraries and development environments. The roadmap to a unified development flow leverages common tools and libraries to support both FPGA and Intel Xeon processor + FPGA systems along with an expansive ecosystem network of Intel and vendors working on independent development tools for demanding workloads such as HPC, imaging identification, security and big data.

Abstracting away FPGA Coding

Intel is building an abstraction layer (as part of the product containing the Intel CPU and FPGA in the same MCP package), called the Orchestration Software layer. This layer and the higher level IP and software models help make development less complex so that developers don’t need to code specifically to the FPGA. The FPGA-enabled Orchestration software layer abstracts away the API to communicate with the FPGA as shown in the following example.

Figure 2. Example of Intel implementation of user IP implemented into FPGA via an abstraction Orchestration software layer

There is a cloud-based library of functions and end-user IP that have been pre-compiled and built that is loaded into the FPGA at runtime. The user first launches a workload from the host and it goes into the Orchestration software which pushes a function into the FPGA. This produces a bitstream that is pre-compiled on the FPGA to bring the data in—it is almost like a fixed architecture I/O interface.

In the example scenario, users simply download the image from the abstraction Orchestration software layer to the FPGA and it is ready to run without compilation. “With the abstraction Orchestration software layer,” Jenkins explained, “Intel is abstracting away all the difficulties of FPGA programming using machine language tools while enabling all the higher level Intel frameworks including the Intel Trusted Analytics Platform (TAP) and Intel Scalable System Framework (SSI) and tying the FPGA into the frameworks. Intel is developing this approach for a variety of markets including visual understanding, analytics, enterprise, Network Function Virtualization (NFV), VPN, genomics, HPC and storage.”

Large Hadron Collider High-Energy Physics Research at CERN

Neufeld indicates that the experiments at CERN — through what they refer to as ‘online computing’ — require a first-level data-filtering to reduce the data to an amount that can be stored and processed on more traditional processing units such as Intel Xeon processors. Figure 3 shows a schematic view of the future LHCb readout system. At the top level, there is a detector and optical fiber links, which transfer data out of the detector. CERN uses FPGAs to acquire data from the detector. There are also large switching fabrics, as well as clusters of processing elements including CPUs, FPGAs, and GPUs to reduce the amount of data. One of the questions the CERN team is testing is “Which technologies should we use and which provide the best performance and lowest energy usage results?”

Figure 3. Schematic diagram showing future LHCb first-level data-filtering system. Courtesy of CERN.

CERN Tests Complex Cherenkov Angle Reconstruction Calculation

CERN has extensive experience using FPGAs in their research work. “We typically use FPGAs in our research to run algorithms looking for simple integer signatures, or for other less complicated calculations. When we heard about the Intel Xeon / FPGA combined processor, we chose a test using a complex algorithm to do a Cherenkov angle reconstruction of light emission in a particle detector, which is not typically performed on an FPGA. This involves tracing a light particle — photon — through a complex arrangement of optical reflection and deflection systems. Our test case used a rich PID algorithm to calculate the Cherenkov angle for each track and detection point. This is a complex mathematical calculation that involves hyperbolic functions, roots, square roots, etc., as shown in Figure 4. It is one of the most costly calculations done in online reconstruction,” states Neufeld.

Figure 4. Test case running Rich PID algorithm to calculate Cherenkov angle. Courtesy of CERN.

Coding the Cherenkov Angle Reconstruction in Verilog versus OpenCL

The CERN team first implemented the Cherenkov angle reconstruction by coding it in the Verilog HDL. The team wrote a 748 clock-cycle long pipeline in Verilog, along with additional blocks developed for the test including: cubic root, complex square root, rotational matrix, and cross/scalar product. It was a lengthy task doing this coding in Verilog with 3,400 lines of code. With all test benches, the implementation took 2.5 months.

Next, the team recoded the Cherenkov angle code using the OpenCL and the BSP (board support package) designed to work across a variety of hardware platforms. Because OpenCL is an abstraction language, it required only 250 lines of code and took two weeks of coding. Not only was coding in OpenCL much faster but the performance results were similar. Figure 5 shows the results of the Verilog versus OpenCL implementation.

Figure 5. Result of Verilog (CQRT) versus OpenCL (RICH) code and performance. Courtesy of CERN.

CERN Compares Co-packaged Intel Xeon – FPGA Processor against Nallatech PCIe Stratix V FPGA Board

To test performance of the Verilog code, the CERN team used a commercially available Stratix V GXA7 FPGA board / Nallatech 385 board for testing. They achieved an acceleration of a factor up to six with the Stratix – Nallatech PCIe board. However, they found a bottleneck in data transfer—they could not keep the pipeline busy because the PCIe card was limited to an eight-lane interface. Next, the CERN team did tests with the Cherenkov angle code comparing a Nallatech FPGA Board with the co-packaged Intel Xeon/FPGA QPI processor.

Finally, the CERN team tested an Intel Xeon CPU, PCIe Stratix V FPGA and Intel Xeon processor/Stratix V QPI (where only the interconnect was different). As shown in Figure 6, there was a factor of 9 speed up for the PCIe Stratix V FPGA and a 26 factor speed up for the Intel Xeon processor/Stratix V QPI with the faster interconnect.

Figure 6. Test results from the CERN team comparing Intel Xeon CPU, PCIe Stratix V FPGA and Intel Xeon processor/FPGA QPI. Courtesy of CERN.

CERN Plans to do Future Testing using co-packaged Intel Xeon/ Intel Arria10 FPGA Processor

“Our CERN team found the results of using the co-packaged Intel Xeon processor/Stratix V QPI processors to be very encouraging. In addition, we find the programming model with OpenCL attractive and it will be mandatory for the High-Energy Physics (HEP) field. Intel will be launching a co-packaged Intel Xeon processor / Intel Arria 10 FPGA processor in the future. We want to do other experiments with the co-packaged Intel Xeon processor/ Arria 10 FPGA. We expect that the high-bandwidth interconnect and modern Arria 10 FPGA card will provide high performance and performance per Joule for HEP algorithms,” states Neufeld.

Linda Barney is the founder and owner of Barney and Associates, a technical/marketing writing, training and web design firm in Beaverton, OR.

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!

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody would like more juice to run compute-intensive HPC simulatio Read more…

By Alex Woodie

Is Data Science the Fourth Pillar of the Scientific Method?

April 18, 2019

Nvidia CEO Jensen Huang revived a decade-old debate last month when he said that modern data science (AI plus HPC) has become the fourth pillar of the scientific method. While some disagree with the notion that statistic Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing the bounds of what's possible in business and science, in w Read more…

By Alex Woodie with Doug Black and Tiffany Trader

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

Bridging HPC and Cloud Native Development with Kubernetes

The HPC community has historically developed its own specialized software stack including schedulers, filesystems, developer tools, container technologies tuned for performance and large-scale on-premises deployments. Read more…

Google Open Sources TensorFlow Version of MorphNet DL Tool

April 18, 2019

Designing optimum deep neural networks remains a non-trivial exercise. “Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expens Read more…

By John Russell

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the Read more…

By HPCwire Editorial Team

Intel Gold U-Series SKUs Reveal Single Socket Intentions

April 18, 2019

Intel plans to jump into the single socket market with a portion of its just announced Cascade Lake microprocessor line according to one media report. This isn Read more…

By John Russell

BSC Researchers Shrink Floating Point Formats to Accelerate Deep Neural Network Training

April 15, 2019

Sometimes calculating solutions as precisely as a computer can wastes more CPU resources than is necessary. A case in point is with deep learning. In early stag Read more…

By Ken Strandberg

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

Nvidia Doubles Down on Medical AI

April 9, 2019

Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery. The GPU leader said Monday it will collaborate with the American College of Radiology (ACR) to provide clinicians with its Clara AI tool kit. The partnership would allow radiologists to leverage AI techniques for diagnostic imaging using their own clinical data. Read more…

By George Leopold

Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions

April 9, 2019

With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workfl 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

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

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

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

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

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

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
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

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

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

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

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

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. 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