Cray, AMPLab, NERSC Collaboration Targets Spark Performance on HPC Platforms

November 4, 2015

Nov. 4 — As data-centric workloads become increasingly common in scientific and industrial applications, a pressing concern is how to design large-scale data analytics stacks that simplify analysis of the resulting data. A new collaboration between Cray, researchers at UC Berkeley’s AMPLab and Berkeley Lab’s National Energy Research Scientific Computing Center (NERSC) is working to address this issue.

The need to build and study increasingly detailed models of physical phenomena has benefited from advancement in high performance computing (HPC) for decades. It has also resulted in an exponential increase in data, from simulations as well as real-world experiments. This has fundamental implications for HPC systems design, such as the need for improved algorithmic methods and the ability to exploit deeper memory/storage hierarchies and efficient methods for data interchange and representation in a scientific workflow. The modern HPC platform has to be equally capable of handling both traditional HPC workloads and the emerging class of data-centric workloads and analytics motifs.

In the commercial sector, these challenges have fueled the development of frameworks such as Hadoop and Spark and a rapidly growing body of open-source software for common data analysis and machine learning problems. These technologies are typically designed for and implemented in distributed data centers consisting of a large number of commodity processing nodes, with an emphasis on scalability, fault tolerance and productivity. In contrast, HPC environments are focused primarily on no-compromise performance of carefully optimized codes at extreme scale.

Given this scenario, how can we the derive the greatest value from adapting productivity-oriented analytics tools such as Spark to HPC environments? And how can a framework like Spark better exploit supercomputing technologies like advanced interconnects and memory hierarchies to improve performance at scale, without losing its productivity benefits?

To address these questions researchers from Cray, AMPLab and NERSC are actively examining research and performance issues in getting Spark up and running on HPC environments such as NERSC’s Edison (Cray XC30) and Cori (Cray XC40) systems. Since linear algebra algorithms underlie many of NERSC’s most pressing scientific data analysis problems, this collaboration will involve the development of novel randomized linear algebra algorithms, the implementation of these algorithms within the AMPLab stack and on Edison and Cori and the application of these algorithms to some of NERSC’s most pressing scientific data-analysis challenges, including problems in BioImaging, Neuroscience and Climate Science.

“Analytics workloads will be an increasingly important workload on our supercomputers and we are thrilled to support and participate in this key collaboration,” said Ryan Waite, senior vice president of products at Cray. “As Cray’s supercomputing platforms enable researchers and scientists to model reality ever more accurately using high-fidelity simulations, we have long seen the need for scalable, performant analytic tools to interpret the resulting data. The Berkeley Data Analytics Stack (BDAS)and Spark, in particular, are emerging as a de facto foundation of such a toolset because of their combined focus on productivity and scalable performance.”

Drawing strength from NERSC’s expertise in scientific data applications, the collaboration combines grand challenge analytical problems from NERSC, pioneering research into big data platforms and scalable randomized linear algebra methods from AMPLab and Cray’s long-standing expertise in scalable supercomputing systems. “We are looking forward to understanding and improving the systems-level behavior and performance of Spark when it is applied to challenging real-world analytics problems on some of Cray’s biggest platforms to date,” said Venkat Krishnamurthy of the Analytics Products group at Cray, who is leading Cray’s involvement in this initiative.

“The AMPLab has been a great success in terms of infrastructure development, but we are continually on the lookout for new use cases to stress-test our framework,” said Michael Mahoney, a faculty member in the University of California, Berkeley Department of Statistics and AMPLab and lead principal investigator on the project. “Spark is very good for certain data analysis computations, but typical Spark use cases haven’t stressed many of the sophisticated linear algebra computations that underlie popular machine learning algorithms. This has historically been the domain of scientific computing. We aim to bridge that gap, to the benefit of both areas.”

“There is currently a lot of momentum behind Spark in the commercial world, and we would like to explore how the scientific community can benefit from the resulting big data analytics capabilities,” said Prabhat, Data and Analytics Services Group Lead at NERSC. “Spark offers a highly productive interface for data scientists; the question in my mind is really regarding Spark’s performance and scalability. Historically, the HPC community has set a high bar for computing performance, and we are hopeful that this collaboration will lead the way in bridging the gap between big data analytics for commercial and high-performance scientific applications.”

About NERSC and Berkeley Lab

The National Energy Research Scientific Computing Center (NERSC) is the primary high-performance computing facility for scientific research sponsored by the U.S. Department of Energy’s Office of Science. Located at Lawrence Berkeley National Laboratory, the NERSC Center serves more than 6,000 scientists at national laboratories and universities researching a wide range of problems in combustion, climate modeling, fusion energy, materials science, physics, chemistry, computational biology, and other disciplines. Berkeley Lab is a U.S. Department of Energy national laboratory located in Berkeley, California. It conducts unclassified scientific research and is managed by the University of California for the U.S. DOE Office of Science.

Source: NERSC

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!

Scalable Informatics Ceases Operations

March 23, 2017

On the same day we reported on the uncertain future for HPC compiler company PathScale, we are sad to learn that another HPC vendor, Scalable Informatics, is closing its doors. Read more…

By Tiffany Trader

‘Strategies in Biomedical Data Science’ Advances IT-Research Synergies

March 23, 2017

“Strategies in Biomedical Data Science: Driving Force for Innovation” by Jay A. Etchings is both an introductory text and a field guide for anyone working with biomedical data. Read more…

By Tiffany Trader

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break ‘Speed Limit’

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory (LANL) is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Intel Ships Drives Based on 3D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3D XPoint non-volatile memory technology as it targets data-driven workloads. Intel’s new Optane solid-state drives, designated P4800X, seek to combine the attributes of memory and storage in the same device. Read more…

By George Leopold

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

Leading Solution Providers

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Share This