New Blueprint for Converging HPC, Big Data

By John Russell

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Tennessee), Satoshi Matsuoka (Tokyo Institute of Technology), William Gropp (National Center for Supercomputing Applications), and Thomas Schulthess (Swiss National Supercomputing Centre), among others, has issued a comprehensive Big Data and Extreme-Scale Computing Pathways to Convergence Report. Not surprisingly it’s a large work not easily plumbed in a single sitting.

Convergence – harmonizing computational infrastructures to accommodate HPC and big data – isn’t a new topic. Recently, big data’s close cousin, machine learning, has become part of the discussion. Moreover, the accompanying rise of cyberinfrastructure as a dominant force in science computing has complicated convergence efforts.

The central premise of this study is that a ‘data-driven’ upheaval is exacerbating divisions – technical, cultural, political, economic – in the cyberecosystem of science. The report tackles in some depth a narrower slice of the problem. Big data, say the authors, has caused or worsened two ‘paradigm splits’: 1) one between the traditional ‘HPC and High-end Data Analysis (HDA)’ and 2) another between ‘stateless networks and stateful services’ provided by end systems. The report lays out a roadmap for mending these fissures.

 

This snippet from the report’s executive summary does a nice job of summing up the challenge:

“Looking toward the future of cyberinfrastructure for science and engineering through the lens of these two bifurcations made it clear to the BDEC community that, in the era of Big Data, the most critical problems involve the logistics of wide-area, multistage workflows—the diverse patterns of when, where, and how data is to be produced, transformed, shared, and analyzed. Consequently, the challenges involved in codesigning software infrastructure for science have to be reframed to fully take account of the diversity of workflow patterns that different application communities want to create. For the HPC community, all the imposing design and development issues of creating an exascale-capable software stack remain; but the supercomputers that need this stack must now be viewed as the nodes (perhaps the most important nodes) in the very large network of computing resources required to process and explore rivers of data flooding in from multiple sources.”

There’s a lot to digest here, including a fair amount of technical guidance. Issued at the end of 2017, the report is the result of workshops held in the U.S. (2013), Japan (2014), Spain (2015), Germany (2016), and China (2017); it grew out of prior efforts of the International Exascale Software Project (IESP). Descriptions and results of the five workshops (agendas, white papers, presentations, attendee lists) are available at the BDEC site (http://www.exascale.org/bdec/).

Jack Dongarra

Commenting on the work, Dongarra said, “Computing is at a profound inflection point, economically and technically. The end of Dennard scaling and its implications for continuing semiconductor-design advances, the shift to mobile and cloud computing, the explosive growth of scientific, business, government, and consumer data and opportunities for data analytics and machine learning, and the continuing need for more-powerful computing systems to advance science and engineering are the context for the debate over the future of exascale computing and big data analysis.”

The broad hope is that the ideas presented in the report will guide community efforts. Dongarra emphasized “High-end data analytics (big data) and high-end computing (exascale) are both essential elements of an integrated computing research-and-development agenda; neither should be sacrificed or minimized to advance the other.” Shown below are typical differences in the BDEC software ecosystem.

 

There’s too much in the report to adequately cover here. Here are the report’s summary recommendations:

“Our major, global recommendation is to address the basic problem of the two paradigm splits: the HPC/HDA software ecosystem split and the wide area data logistics split. For this to be achieved, there is a need for new standards that will govern the interoperability between data and compute, based on a new, common and open Distributed Services Platform (DSP), that offers programmable access to shared processing, storage and communication resources, and that can serve as a universal foundation for the component interoperability that novel services and applications will require.

“We make five recommendations for decentralized edge and peripheral ecosystems:

  • Converge on a new hourglass architecture for a Common Distributed Service Platform (DSP).
  • Target workflow patterns for improved data logistics.
  • Design cloud stream processing capabilities for HPC.
  • Promote a scalable approach to Content Delivery/Distribution Networks.
  • Develop software libraries for common intermediate processing tasks.

“We make five actionable conclusions for centralized facilities:

  • Energy is an overarching challenge for sustainability.
  • Data reduction is a fundamental pattern.
  • Radically improved resource management is required.
  • Both centralized and decentralized systems share many common software challenges and opportunities: 
(a) Leverage HPC math libraries for HDA.
(b) More efforts for numerical library standards.
(c) New standards for shared memory parallel processing.
(d) Interoperability between programming models and data formats.
  • Machine learning is becoming an important component of scientific workloads, and HPC architectures must be adapted to accommodate this evolution.”

Link to BDEC Report: http://www.exascale.org/bdec/

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from its predecessors, including the red-hot H100 and A100 GPUs. Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. While Nvidia may not spring to mind when thinking of the quant Read more…

2024 Winter Classic: Meet the HPE Mentors

March 18, 2024

The latest installment of the 2024 Winter Classic Studio Update Show features our interview with the HPE mentor team who introduced our student teams to the joys (and potential sorrows) of the HPL (LINPACK) and accompany Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the field was normalized for boys in 1969 when the Apollo 11 missi Read more…

Apple Buys DarwinAI Deepening its AI Push According to Report

March 14, 2024

Apple has purchased Canadian AI startup DarwinAI according to a Bloomberg report today. Apparently the deal was done early this year but still hasn’t been publicly announced according to the report. Apple is preparing Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimization algorithms to iteratively refine their parameters until Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, code-named Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Survey of Rapid Training Methods for Neural Networks

March 14, 2024

Artificial neural networks are computing systems with interconnected layers that process and learn from data. During training, neural networks utilize optimizat Read more…

PASQAL Issues Roadmap to 10,000 Qubits in 2026 and Fault Tolerance in 2028

March 13, 2024

Paris-based PASQAL, a developer of neutral atom-based quantum computers, yesterday issued a roadmap for delivering systems with 10,000 physical qubits in 2026 a Read more…

India Is an AI Powerhouse Waiting to Happen, but Challenges Await

March 12, 2024

The Indian government is pushing full speed ahead to make the country an attractive technology base, especially in the hot fields of AI and semiconductors, but Read more…

Charles Tahan Exits National Quantum Coordination Office

March 12, 2024

(March 1, 2024) My first official day at the White House Office of Science and Technology Policy (OSTP) was June 15, 2020, during the depths of the COVID-19 loc Read more…

AI Bias In the Spotlight On International Women’s Day

March 11, 2024

What impact does AI bias have on women and girls? What can people do to increase female participation in the AI field? These are some of the questions the tech Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Analyst Panel Says Take the Quantum Computing Plunge Now…

November 27, 2023

Should you start exploring quantum computing? Yes, said a panel of analysts convened at Tabor Communications HPC and AI on Wall Street conference earlier this y Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Training of 1-Trillion Parameter Scientific AI Begins

November 13, 2023

A US national lab has started training a massive AI brain that could ultimately become the must-have computing resource for scientific researchers. Argonne N Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire