Petascale computing

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Petascale computing refers to computing systems capable of performing at least 1 quadrillion (10^15) floating-point operations per second (FLOPS). These systems are often called petaflops systems and represent a significant leap from traditional supercomputers in terms of raw performance, enabling them to handle vast datasets and complex computations.

Contents

Definition

Floating point operations per second (FLOPS) are one measure of computer performance. FLOPS can be recorded in different measures of precision, however the standard measure (used by the TOP500 supercomputer list) uses 64 bit (double-precision floating-point format) operations per second using the High Performance LINPACK (HPLinpack) benchmark. [1] [2]

The metric typically refers to single computing systems, although can be used to measure distributed computing systems for comparison. It can be noted that there are alternative precision measures using the LINPACK benchmarks which are not part of the standard metric/definition. [2] It has been recognized that HPLinpack may not be a good general measure of supercomputer utility in real world application, however it is the common standard for performance measurement. [3] [4]

History

The petaFLOPS barrier was first broken on 16 September 2007 by the distributed computing Folding@home project. [5] The first single petascale system, the Roadrunner, entered operation in 2008. [6] The Roadrunner, built by IBM, had a sustained performance of 1.026 petaFLOPS. The Jaguar became the second computer to break the petaFLOPS milestone, later in 2008, and reached a performance of 1.759 petaFLOPS after a 2009 update. [7]

In 2020, Fugaku became the fastest supercomputer in the world, reaching 415 petaFLOPS in June 2020. Fugaku later achieved an Rmax of 442 petaFLOPS in November of the same year.

By 2022, exascale computing had been reached with the development of Frontier, surpassing Fugaku with an Rmax of 1.102 exaFLOPS in June 2022. [8]

Artificial intelligence

Modern artificial intelligence (AI) systems require large amounts of computational power to train model parameters. OpenAI employed 25,000 Nvidia A100 GPUs to train GPT-4, using 133 trillion floating point operations. [9]

See also

Related Research Articles

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Floating point operations per second is a measure of computer performance in computing, useful in fields of scientific computations that require floating-point calculations.

<span class="mw-page-title-main">IBM Blue Gene</span> Series of supercomputers by IBM

Blue Gene was an IBM project aimed at designing supercomputers that can reach operating speeds in the petaFLOPS (PFLOPS) range, with relatively low power consumption.

MDGRAPE-3 is an ultra-high performance petascale supercomputer system developed by the Riken research institute in Japan. It is a special purpose system built for molecular dynamics simulations, especially protein structure prediction.

<span class="mw-page-title-main">TOP500</span> Database project devoted to the ranking of computers

The TOP500 project ranks and details the 500 most powerful non-distributed computer systems in the world. The project was started in 1993 and publishes an updated list of the supercomputers twice a year. The first of these updates always coincides with the International Supercomputing Conference in June, and the second is presented at the ACM/IEEE Supercomputing Conference in November. The project aims to provide a reliable basis for tracking and detecting trends in high-performance computing and bases rankings on HPL benchmarks, a portable implementation of the high-performance LINPACK benchmark written in Fortran for distributed-memory computers.

<span class="mw-page-title-main">JUGENE</span> Former supercomputer in Germany

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<span class="mw-page-title-main">Jaguar (supercomputer)</span> Cray supercomputer at Oak Ridge National Laboratory

Jaguar or OLCF-2 was a petascale supercomputer built by Cray at Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee. The massively parallel Jaguar had a peak performance of just over 1,750 teraFLOPS. It had 224,256 x86-based AMD Opteron processor cores, and operated with a version of Linux called the Cray Linux Environment. Jaguar was a Cray XT5 system, a development from the Cray XT4 supercomputer.

<span class="mw-page-title-main">Tianhe-1</span> Supercomputer

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<span class="mw-page-title-main">Exascale computing</span> Computer systems capable of one exaFLOPS

Exascale computing refers to computing systems capable of calculating at least 1018 IEEE 754 Double Precision (64-bit) operations (multiplications and/or additions) per second (exaFLOPS)"; it is a measure of supercomputer performance.

This list compares various amounts of computing power in instructions per second organized by order of magnitude in FLOPS.

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<span class="mw-page-title-main">K computer</span> Supercomputer in Kobe, Japan

The K computer – named for the Japanese word/numeral "kei" (京), meaning 10 quadrillion (1016) – was a supercomputer manufactured by Fujitsu, installed at the Riken Advanced Institute for Computational Science campus in Kobe, Hyōgo Prefecture, Japan. The K computer was based on a distributed memory architecture with over 80,000 compute nodes. It was used for a variety of applications, including climate research, disaster prevention and medical research. The K computer's operating system was based on the Linux kernel, with additional drivers designed to make use of the computer's hardware.

<span class="mw-page-title-main">History of supercomputing</span>

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The LINPACK Benchmarks are a measure of a system's floating-point computing power. Introduced by Jack Dongarra, they measure how fast a computer solves a dense n by n system of linear equations Ax = b, which is a common task in engineering.

The Graph500 is a rating of supercomputer systems, focused on data-intensive loads. The project was announced on International Supercomputing Conference in June 2010. The first list was published at the ACM/IEEE Supercomputing Conference in November 2010. New versions of the list are published twice a year. The main performance metric used to rank the supercomputers is GTEPS.

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<span class="mw-page-title-main">Fugaku (supercomputer)</span> Japanese supercomputer

Fugaku(Japanese: 富岳) is a petascale supercomputer at the Riken Center for Computational Science in Kobe, Japan. It started development in 2014 as the successor to the K computer and made its debut in 2020. It is named after an alternative name for Mount Fuji.

<span class="mw-page-title-main">Aurora (supercomputer)</span> US DOE supercomputer by Intel and Cray

Aurora is an exascale supercomputer that was sponsored by the United States Department of Energy (DOE) and designed by Intel and Cray for the Argonne National Laboratory. It was briefly the second fastest supercomputer in the world from November 2023 to June 2024.

Zettascale computing refers to computing systems capable of calculating at least "1021 IEEE 754 Double Precision (64-bit) operations (multiplications and/or additions) per second (zettaFLOPS)". It is a measure of supercomputer performance, and as of July 2022 is a hypothetical performance barrier. A zettascale computer system could generate more single floating point data in one second than was stored by the total digital means on Earth in the first quarter of 2011.

References

  1. "FREQUENTLY ASKED QUESTIONS". www.top500.org. Retrieved 23 June 2020.
  2. 1 2 Kogge, Peter, ed. (1 May 2008). ExaScale Computing Study: Technology Challenges in Achieving Exascale Systems (PDF). United States Government. Retrieved 28 September 2008.
  3. Bourzac, Katherine (November 2017). "Supercomputing poised for a massive speed boost". Nature. 551 (7682): 554–556. doi: 10.1038/d41586-017-07523-y . Retrieved 3 June 2022.
  4. Reed, Daniel; Dongarra, Jack. "Exascale Computing and Big Data: The Next Frontier" (PDF). Retrieved 3 June 2022.
  5. Michael Gross (2012). "Folding research recruits unconventional help". Current Biology. 22 (2): R35–R38. doi: 10.1016/j.cub.2012.01.008 . PMID   22389910.
  6. National Research Council (U.S.) (2008). The potential impact of high-end capability computing on four illustrative fields of science and engineering. The National Academies. p. 11. ISBN   978-0-309-12485-0.
  7. National Center for Computational Sciences (NCCS) (2010). "World's Most Powerful Supercomputer for Science!". NCCS. Archived from the original on 2009-11-27. Retrieved 2010-06-26.
  8. "June 2022 | TOP500". www.top500.org. Retrieved 2024-11-21.
  9. Minde, Tor Björn (2023-10-08). "Generative AI does not run on thin air". RISE. Retrieved 2024-03-29.