Cray Details Its Cluster Supercomputing Strategy

By Tiffany Trader

July 28, 2015

When iconic American supercomputer maker Cray purchased 20-year-old HPC cluster vendor Appro in late 2012, Cray CEO Peter Ungaro referred to Appro’s principal IP as “one of the most advanced industry clusters in the world.” At the time HPCwire reported that Cray would benefit from the product line and a bigger sales team from Appro, and Appro would benefit from Cray’s overseas connections.

Nearly three years have passed, and Cray can now claim a product portfolio that spans the cluster-supercomputer divide with its Appro-derived CS “cluster supercomputer” series, designed to handle a broad range of medium- to large-scale simulation and data analytics workloads, and its XC- and next-generation Shasta lines, based on Cray’s vision of adaptive supercomputing, engineered to provide both extreme scalability and sustained performance.

The collection of sites that have deployed Cray CS cluster supercomputers, alone or in tandem with the company’s tightly-coupled XC supercomputer products, includes the Swiss National Supercomputing Center (CSCS), the Department of Defense High Performance Computing Modernization Program, Lawrence Livermore National Laboratory, the University of Tsukuba (Japan), Mississippi State University, the University of Tennessee, the Railway Technical Research Institute (Japan), and San Diego Supercomputer Center.

As a recent Cray-IDC webinar and related white paper convey, the cluster computing ecosystem is facing challenges relating to heterogeneity of processor types and increased data-centricity. On account of their sheer scale and increased complexity, cluster supercomputers, defined by IDC as clusters that sell for more than $500,000, tend to up the difficulty level substantially. Consider that, according to IDC reports, the average cluster supercomputer in 2013 (with 389 nodes) has about 22 times more nodes than its smaller cousins (with an average of 17.9 nodes). Specific challenges faced by these über-clusters include scaling systems software and applications; reliability/resilience; data movement; and power and cooling expenses.

Cray technology adapted from supercomputing slide 2015

Cray and IDC review these challenges and examine some of the ways that Cray has borrowed from its flagship supercomputing line to meet the requirements of its cluster customers.

In the IDC portion of the webinar, covered in an previous HPCwire article, Research Vice President of High Performance Computing at IDC Steve Conway made the point that clusters are driving growth in both HPC and HPDA markets. John Lee, Cray’s vice president of product management, Cray Cluster Solutions, says that Cray’s vision does not put an artificial wall between these, but sees these two complimentary workflows blending into a single paradigm. “Cray’s vision,” he says “is to develop a market leading solution in the areas of compute, store and analyze, to deliver fast solutions to both large math problems and data problems.”

As of the recent TOP500 list, Cray ranked number one in the top 50 with 17 systems and in the top 100 with 31 machines. In the entire list, Cray is number three with 71 systems, behind HP and IBM.

Lee says that while most people continue to associate Cray with “big iron” supercomputers, and while these do make up the majority of its TOP500 share, Cray also lays claim to a lot of “medium iron.” The company has 22 clusters on the recent list, which is 31 percent of its total system allotment. Lee calls out two systems in particular (numbers 13 and 14, CS-Storm clusters) which reflect Cray’s ability to leverage its supercomputing technologies in building very large production systems.

The systems highlighted in blue (below) denote new Cray-built entrants to the list, but as Lee emphasizes, there are a number of smaller clusters (not on the list) that Cray has delivered that vary in complexity and size and still benefit from Cray’s elite line.

Cray cluster leadership slide 2015

Lee says that Cray’s portfolio of two compute products is designed to offer different tools for different problems but with significant technology cross-over.

“While these are two distinct products addressing different market segments, there are lots of technology cross-over where it makes sense,” he states. “For instance, our CS cluster line is leveraged heavily in our data analytics and storage products while supercomputing technologies, developed for our XC series, like innovative packaging and cooling, highly efficient power distribution to the rack, high-speed signal integrity design and comprehensive software tools, are all infused into our cluster systems.”

As system complexity and size increases, Cray is selectively migrating technologies from its supercomputing line to tackle some of the most pressing challenges of large-scale clusters, such as the need to exploit extreme parallelism, the need for greater system resiliency, the need for creative and efficient ways to power and cool the system, and the need for a comprehensive high-performance computing stack that can run at scale and hide programming complexity.

Lee acknowledges that Cray does not have the answers to all the problems facing the high performance computing today, but says the company is making large investments of both money and resources to tackle these problems.

Adaptive Supercomputing

Cray launched its adaptive supercomputing strategy in 2004 to take advantage of different processor architectures for different problems. This had led to its supporting accelerators — GPUs and Xeon Phi parts — on all of its systems. On the current TOP500 list, Cray has the highest share of accelerated systems with 53 such machines.

Lee upholds CS-Storm as an example of a hybrid system that is scalable and power-efficient. Storm is a CS series system with 8 GPU nodes in a 2U chassis optimized for GPU applications. The design supports 176 NVIDIA Tesla K40 or K80 GPUs in a rack offering a potential 329 GPU teraflops per (K80-filled) rack, making it possible to realize 1 petaflops in just three racks.

The power and cooling architecture was designed to ensure that the accelerators run at their maximum performance without power capping or thermal throttling. Innovations in design borrowed from Cray’s flagship XC line include high signal integrity between the host processor and each of the GPUs to ensure reliable error free operation of the GPUs during their heaviest workload. Lee notes that software tools make it easier for customers to extract data level parallelism from their application to take advantage of these manycore architectures. He adds that the name “Storm” heralds from the late 1990s Red Storm project, which marked Cray’s transition to commodity processors.

An example of real-world scaling on GPU nodes can be seen in the case of an oil and gas application called SPECFEM3D, a seismology community code. According to data provided by BP and Princeton, SPECFEM3D has near linear scaling going from 18 minutes on a single GPU to 1.5 minutes across 16 GPUs.

“While not all applications scale this well, for those that do have strong scaling characteristics, CS-Storm can be a very powerful tool,” observes Lee.

Moving on to system resiliency, Lee notes that it is no longer a nice to have feature but a necessity, and that’s in large part because the democratization of supercomputing by clusters has resulted in more non-traditional HPC customers using cluster supercomputers. According to IDC figures, cluster adoption has increased from 65 percent in 2008 to over 80 percent in 2013.

“More mission critical applications are being run on these systems and wider adoption has resulted in increased demand for higher productivity. Sadly the industry trends have been moving in the opposite direction and there are several factors driving this trend,” explains Lee.

“First, as supercomputers have become more economical with increased adoption of affordable commodity clusters, our customers are fielding larger and larger machines. As systems get larger, overall reliability of the system decreases. The second factor that is contributing to the system downtime is individual nodes getting less reliable. This is a byproduct of today’s compute ecosystem. Servers today are vastly different than the servers of yesterday. The server market is being heavily influenced by the hyperscale customers that are pressuring suppliers to drive down costs at the expense of quality and reliability. Hyperscale customers are more tolerant of node level failures because they address that problem at the software layer,” he continues.

“The HPC cluster market has leveraged the larger server ecosystems to drive down cost and these market trends have impacted the overall quality of the systems that we can build. This problem is exacerbated by the fact that the individual nodes in an HPC cluster are becoming more and more powerful. Each node is being asked to do more and this is especially true with hybrid nodes. In some cases each node has one, two, four or even eight accelerators connected to a single host. In those cases, losing a single node means not only losing the host processors but losing all the accelerators and the compute power they deliver.”

Lee goes on to compare the cloud reliability model with clusters. In the hyperscale or cloud reliability model, emphasis is on cost reduction and failure is an every day or every moment occurrence. When a server fails, intelligent software restarts the job on another server. Server failure does not result in much work lost. But in a classic HPC workload environment, many servers are being used to run a single job. Depending on the size of the job and number of nodes, the mean time between failures can be less than a day or perhaps hours. “The reliability of the job is directly proportional to the reliability of your individual servers,” says Lee. “In this case, the loss of one server of course results in the loss of the entire job.”

Reliable systems are engineered from the ground up, the Cray rep observes, from both a micro and macro level. At the micro level it starts with the compute nodes since compute nodes make up the majority of the system and have the biggest impact on reliability. And then there is a holistic approach for the peripherals in order to have a reliable system.

Cray reliable clusters slide 2015

Cray made a decision to go with a strong motherboard partner matched to the needs of demanding HPC applications. Cray says that when it went with a motherboard from an overseas vendor, it found them to be lacking. Since 2012, the Cray cluster product group has been working with Intel to codesign boards that are purposely built for HPC. These are half-width, high reliability boards with a feature set to address specific customer needs.

According to a study from UC Berkeley, single server component failures break down as follows: hard drive at 47 percent, fans at 33 percent, power supplies at 13 percent. Cray engineered its systems to run diskless to eliminate the single highest failing component, and then it engineered built-in redundancy for both fans and power supplies to increase overall system reliability. The remaining 7 percent, which can be attributed to memory, board and processor failures, Cray minimizes with the use of high-quality boards and factory-burn-in tests.

The Soft Side of Big Iron

“What makes our system what it is has just as much to do with our software than our hardware says,” says Lee emphatically, and the company actually has more software engineers than hardware engineers. For customers who manage their own stack, like SDSC and LLNL, Cray can and does ship systems without a software stack, but for those who want a more turnkey solution, Cray ships systems with a Cray HPC software stack, consisting of Cray’s cluster management software framework and other stack tools.

Cray software ecosystem slide 2015

Another prominent example of Cray’s portfolio synergy includes the Cray Programming Environment, which features mature vectorizing compilers designed to improve the performance and ease of programming of clusters. Cray reports this compiler capability is especially important for efficiently exploiting NVIDIA GPGPU accelerators and Intel Xeon Phi coprocessors.

Cray Programming Environment slide 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!

What’s New in HPC Research: Wind Farms, Gravitational Lenses, Web Portals & More

February 19, 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

Insights from Optimized Codes on Cineca’s Marconi

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from the nanoscale to the astronomic, from calculating quantum effe Read more…

By Ken Strandberg

What Will IBM’s AI Debater Learn from Its Loss?

February 14, 2019

The utility of IBM’s latest man-versus-machine gambit is debatable. At the very least its Project Debater got us thinking about the potential uses of artificial intelligence as a way of helping humans sift through al Read more…

By George Leopold

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

Medical Research Powered by Data

“We’re all the same, but we’re unique as well. In that uniqueness lies all of the answers….”

  • Mark Tykocinski, MD, Provost, Executive Vice President for Academic Affairs, Thomas Jefferson University

Getting the answers to what causes some people to develop diseases and not others is driving the groundbreaking medical research being conducted by the Computational Medicine Center at Thomas Jefferson University in Philadelphia. Read more…

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 of bankruptcy proceedings. According to Dutch news site Drimb Read more…

By Tiffany Trader

Insights from Optimized Codes on Cineca’s Marconi

February 15, 2019

What can you do with 381,392 CPU cores? For Cineca, it means enabling computational scientists to expand a large part of the world’s body of knowledge from th Read more…

By Ken Strandberg

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

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

Iowa ‘Grows Its Own’ to Fill the HPC Workforce Pipeline

February 13, 2019

The global workforce that supports advanced computing, scientific software and high-speed research networks is relatively small when you stop to consider the magnitude of the transformative discoveries it empowers. Technical conferences provide a forum where specialists convene to learn about the latest innovations and schedule face-time with colleagues from other institutions. Read more…

By Elizabeth Leake, STEM-Trek

Trump Signs Executive Order Launching U.S. AI Initiative

February 11, 2019

U.S. President Donald Trump issued an Executive Order (EO) today launching a U.S Artificial Intelligence Initiative. The new initiative - Maintaining American L Read more…

By John Russell

Celebrating Women in Science: Meet Four Women Leading the Way in HPC

February 11, 2019

One only needs to look around at virtually any CS/tech conference to realize that women are underrepresented, and that holds true of HPC. SC hosts over 13,000 H Read more…

By AJ Lauer

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

Assessing Government Shutdown’s Impact on HPC

February 6, 2019

After a 35-day federal government shutdown, the longest in U.S. history, government agencies are taking stock of the damage -- and girding for a potential secon 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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