FLOPS

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Floating point operations per second (FLOPS, flops or flop/s) is a measure of computer performance in computing, useful in fields of scientific computations that require floating-point calculations. [1] For such cases, it is a more accurate measure than measuring instructions per second.

Contents

Floating-point arithmetic

Multipliers for flops
NameUnitValue
kiloFLOPSkFLOPS103
megaFLOPSMFLOPS106
gigaFLOPSGFLOPS109
teraFLOPSTFLOPS1012
petaFLOPSPFLOPS1015
exaFLOPSEFLOPS1018
zettaFLOPSZFLOPS1021
yottaFLOPSYFLOPS1024
ronnaFLOPSRFLOPS1027
quettaFLOPSQFLOPS1030

Floating-point arithmetic is needed for very large or very small real numbers, or computations that require a large dynamic range. Floating-point representation is similar to scientific notation, except everything is carried out in base two, rather than base ten. The encoding scheme stores the sign, the exponent (in base two for Cray and VAX, base two or ten for IEEE floating point formats, and base 16 for IBM Floating Point Architecture) and the significand (number after the radix point). While several similar formats are in use, the most common is ANSI/IEEE Std. 754-1985. This standard defines the format for 32-bit numbers called single precision, as well as 64-bit numbers called double precision and longer numbers called extended precision (used for intermediate results). Floating-point representations can support a much wider range of values than fixed-point, with the ability to represent very small numbers and very large numbers. [2]

Dynamic range and precision

The exponentiation inherent in floating-point computation assures a much larger dynamic range – the largest and smallest numbers that can be represented – which is especially important when processing data sets where some of the data may have extremely large range of numerical values or where the range may be unpredictable. As such, floating-point processors are ideally suited for computationally intensive applications. [3]

Computational performance

FLOPS and MIPS are units of measure for the numerical computing performance of a computer. Floating-point operations are typically used in fields such as scientific computational research, as well as in machine learning. However, before the late 1980s floating-point hardware (it's possible to implement FP arithmetic in software over any integer hardware) was typically an optional feature, and computers that had it were said to be "scientific computers", or to have "scientific computation" capability. Thus the unit MIPS was useful to measure integer performance of any computer, including those without such a capability, and to account for architecture differences, similar MOPS (million operations per second) was used as early as 1970 [4] as well. Note that besides integer (or fixed-point) arithmetics, examples of integer operation include data movement (A to B) or value testing (If A = B, then C). That's why MIPS as a performance benchmark is adequate when a computer is used in database queries, word processing, spreadsheets, or to run multiple virtual operating systems. [5] [6] In 1974 David Kuck coined the terms flops and megaflops for the description of supercomputer performance of the day by the number of floating-point calculations they performed per second. [7] This was much better than using the prevalent MIPS to compare computers as this statistic usually had little bearing on the arithmetic capability of the machine on scientific tasks.

FLOPS by the largest supercomputer over time Supercomputer Power (FLOPS), OWID.svg
FLOPS by the largest supercomputer over time

FLOPS on an HPC-system can be calculated using this equation: [8]

This can be simplified to the most common case: a computer that has exactly 1 CPU:

FLOPS can be recorded in different measures of precision, for example, the TOP500 supercomputer list ranks computers by 64 bit (double-precision floating-point format) operations per second, abbreviated to FP64. [9] Similar measures are available for 32-bit (FP32) and 16-bit (FP16) operations.

Floating-point operations per clock cycle for various processors

Floating-point operations per clock cycle per core [10]
Microarchitecture Instruction set architecture FP64FP32FP16
Intel CPU
Intel 80486 x87 (32-bit)?0.128 [11] ?
 x87 (32-bit)?0.5 [11] ?
 MMX (64-bit)?1 [12] ?
Intel P6 Pentium III SSE (64-bit)?2 [12] ?
Intel NetBurst Pentium 4 (Willamette, Northwood) SSE2 (64-bit)24?
Intel P6 Pentium M SSE2 (64-bit)12?
 SSE3 (64-bit)24?
48 ?
Intel Atom (Bonnell, Saltwell, Silvermont and Goldmont) SSE3 (128-bit)24 ?
Intel Sandy Bridge (Sandy Bridge, Ivy Bridge) AVX (256-bit)8160
 AVX2 & FMA (256-bit)16320
Intel Xeon Phi (Knights Corner) IMCI (512-bit)16320
 AVX-512 & FMA (512-bit)32640
AMD CPU
AMD Bobcat AMD64 (64-bit)240
480
AMD K10 SSE4/4a (128-bit)480
AMD Bulldozer [13] (Piledriver, Steamroller, Excavator)
  • AVX (128-bit) (Bulldozer, Steamroller)
  • AVX2 (128-bit) (Excavator)
  • FMA3 (Bulldozer) [14]
  • FMA3/4 (Piledriver, Excavator)
480
 AVX2 & FMA (128-bit, 256-bit decoding) [18] 8160
 AVX2 & FMA (256-bit)16320
ARM CPU
ARM Cortex-A7, A9, A15 ARMv7 180
ARM Cortex-A32, A35 ARMv8 280
ARM Cortex-A53, A55, A57, [13] A72, A73, A75 ARMv8 480
ARM Cortex-A76, A77, A78 ARMv8 8160
ARM Cortex-X1 ARMv8 1632?
Qualcomm Krait ARMv8 180
Qualcomm Kryo (1xx - 3xx) ARMv8 280
Qualcomm Kryo (4xx - 5xx) ARMv8 8160
Samsung Exynos M1 and M2 ARMv8 280
Samsung Exynos M3 and M4 ARMv8 3120
IBM PowerPC A2 (Blue Gene/Q) ?88 (as FP64)0
Hitachi SH-4 [20] [21] SH-4 170
Nvidia GPU
Nvidia Curie (GeForce 6 series and GeForce 7 series) PTX ?8?
Nvidia Tesla 2.0 (GeForce GTX 260–295) PTX ?2?
Nvidia Fermi (only GeForce GTX 465–480, 560 Ti, 570–590) PTX 1/4 (locked by driver, 1 in hardware)20
Nvidia Fermi (only Quadro 600–2000) PTX 1/820
Nvidia Fermi (only Quadro 4000–7000, Tesla) PTX 120
Nvidia Kepler (GeForce (except Titan and Titan Black), Quadro (except K6000), Tesla K10) PTX 1/12 (for GK110: locked by driver, 2/3 in hardware)20
Nvidia Kepler (GeForce GTX Titan and Titan Black, Quadro K6000, Tesla (except K10)) PTX 2/320
  • Nvidia Maxwell
  • Nvidia Pascal (all except Quadro GP100 and Tesla P100)
 PTX 1/1621/32
Nvidia Pascal (only Quadro GP100 and Tesla P100) PTX 124
Nvidia Volta [22] PTX 12 (FP32) + 2 (INT32)16
Nvidia Turing (only GeForce 16XX) PTX 1/162 (FP32) + 2 (INT32)4
Nvidia Turing (all except GeForce 16XX) PTX 1/162 (FP32) + 2 (INT32)16
Nvidia Ampere [23] [24] (only Tesla A100/A30) PTX 22 (FP32) + 2 (INT32)32
Nvidia Ampere (all GeForce and Quadro, Tesla A40/A10) PTX 1/322 (FP32) + 0 (INT32) or 1 (FP32) + 1 (INT32)8
AMD GPU
AMD TeraScale 1 (Radeon HD 4000 series) TeraScale 1 0.42?
AMD TeraScale 2 (Radeon HD 5000 series) TeraScale 2 12?
AMD TeraScale 3 (Radeon HD 6000 series) TeraScale 3 14?
AMD GCN (only Radeon Pro W 8100–9100) GCN 12 ?
AMD GCN (all except Radeon Pro W 8100–9100, Vega 10–20) GCN 1/824
AMD GCN Vega 10 GCN 1/824
AMD GCN Vega 20 (only Radeon VII) GCN 1/2 (locked by driver, 1 in hardware)24
AMD GCN Vega 20 (only Radeon Instinct MI50 / MI60 and Radeon Pro VII) GCN 124
 RDNA 1/824
AMD RDNA3 RDNA 1/8?48?
AMD CDNA CDNA 14 (Tensor) [27] 16
AMD CDNA 2 CDNA 2 4 (Tensor)4 (Tensor)16
Intel GPU
Intel Xe-LP (Iris Xe MAX) [28] Xe1/2?24
Intel Xe-HPG (Arc Alchemist) [28] Xe0216
Intel Xe-HPC (Ponte Vecchio) [29] Xe2232
Qualcomm GPU
Qualcomm Adreno 5x0 Adreno 5xx124
Qualcomm Adreno 6x0 Adreno 6xx124
Graphcore
Graphcore Colossus GC2 [30] [31]  ?01664
  • Graphcore Colossus GC200 Mk2 [32]
  • Graphcore Bow-2000 [33]
 ?032128
Supercomputer
ENIAC @ 100 kHz in 19450.004 [34] (~0.00000003 FLOPS/W)
48-bit processor @ 208 kHz in CDC 1604 in 1960
60-bit processor @ 10 MHz in CDC 6600 in 19640.3 (FP60)
60-bit processor @ 10 MHz in CDC 7600 in 19671.0 (FP60)
Cray-1 @ 80 MHz in 19762 (700 FLOPS/W)
CDC Cyber 205 @ 50 MHz in 1981

FORTRAN compiler (ANSI 77 with vector extensions)

816
Transputer IMS T800-20 @ 20 MHz in 19870.08 [35]
Parallella E16 @ 1000 MHz in 20122 [36] (5.0 GFLOPS/W) [37]
Parallella E64 @ 800 MHz in 20122 [38] (50.0 GFLOPS/W) [37]
Microarchitecture Instruction set architecture FP64FP32FP16

Performance records

Single computer records

In June 1997, Intel's ASCI Red was the world's first computer to achieve one teraFLOPS and beyond. Sandia director Bill Camp said that ASCI Red had the best reliability of any supercomputer ever built, and "was supercomputing's high-water mark in longevity, price, and performance". [39]

NEC's SX-9 supercomputer was the world's first vector processor to exceed 100 gigaFLOPS per single core.

In June 2006, a new computer was announced by Japanese research institute RIKEN, the MDGRAPE-3. The computer's performance tops out at one petaFLOPS, almost two times faster than the Blue Gene/L, but MDGRAPE-3 is not a general purpose computer, which is why it does not appear in the Top500.org list. It has special-purpose pipelines for simulating molecular dynamics.

By 2007, Intel Corporation unveiled the experimental multi-core POLARIS chip, which achieves 1 teraFLOPS at 3.13 GHz. The 80-core chip can raise this result to 2 teraFLOPS at 6.26 GHz, although the thermal dissipation at this frequency exceeds 190 watts. [40]

In June 2007, Top500.org reported the fastest computer in the world to be the IBM Blue Gene/L supercomputer, measuring a peak of 596 teraFLOPS. [41] The Cray XT4 hit second place with 101.7 teraFLOPS.

On June 26, 2007, IBM announced the second generation of its top supercomputer, dubbed Blue Gene/P and designed to continuously operate at speeds exceeding one petaFLOPS, faster than the Blue Gene/L. When configured to do so, it can reach speeds in excess of three petaFLOPS. [42]

On October 25, 2007, NEC Corporation of Japan issued a press release announcing its SX series model SX-9, [43] claiming it to be the world's fastest vector supercomputer. The SX-9 features the first CPU capable of a peak vector performance of 102.4 gigaFLOPS per single core.

On February 4, 2008, the NSF and the University of Texas at Austin opened full scale research runs on an AMD, Sun supercomputer named Ranger, [44] the most powerful supercomputing system in the world for open science research, which operates at sustained speed of 0.5 petaFLOPS.

On May 25, 2008, an American supercomputer built by IBM, named 'Roadrunner', reached the computing milestone of one petaFLOPS. It headed the June 2008 and November 2008 TOP500 list of the most powerful supercomputers (excluding grid computers). [45] [46] The computer is located at Los Alamos National Laboratory in New Mexico. The computer's name refers to the New Mexico state bird, the greater roadrunner (Geococcyx californianus). [47]

In June 2008, AMD released ATI Radeon HD 4800 series, which are reported to be the first GPUs to achieve one teraFLOPS. On August 12, 2008, AMD released the ATI Radeon HD 4870X2 graphics card with two Radeon R770 GPUs totaling 2.4 teraFLOPS.

In November 2008, an upgrade to the Cray Jaguar supercomputer at the Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) raised the system's computing power to a peak 1.64 petaFLOPS, making Jaguar the world's first petaFLOPS system dedicated to open research. In early 2009 the supercomputer was named after a mythical creature, Kraken. Kraken was declared the world's fastest university-managed supercomputer and sixth fastest overall in the 2009 TOP500 list. In 2010 Kraken was upgraded and can operate faster and is more powerful.

In 2009, the Cray Jaguar performed at 1.75 petaFLOPS, beating the IBM Roadrunner for the number one spot on the TOP500 list. [48]

In October 2010, China unveiled the Tianhe-1, a supercomputer that operates at a peak computing rate of 2.5 petaFLOPS. [49] [50]

As of 2010 the fastest PC processor reached 109 gigaFLOPS (Intel Core i7 980 XE) [51] in double precision calculations. GPUs are considerably more powerful. For example, Nvidia Tesla C2050 GPU computing processors perform around 515 gigaFLOPS [52] in double precision calculations, and the AMD FireStream 9270 peaks at 240 gigaFLOPS. [53]

In November 2011, it was announced that Japan had achieved 10.51 petaFLOPS with its K computer. [54] It has 88,128 SPARC64 VIIIfx processors in 864 racks, with theoretical performance of 11.28 petaFLOPS. It is named after the Japanese word "kei", which stands for 10 quadrillion, [55] corresponding to the target speed of 10 petaFLOPS.

On November 15, 2011, Intel demonstrated a single x86-based processor, code-named "Knights Corner", sustaining more than a teraFLOPS on a wide range of DGEMM operations. Intel emphasized during the demonstration that this was a sustained teraFLOPS (not "raw teraFLOPS" used by others to get higher but less meaningful numbers), and that it was the first general purpose processor to ever cross a teraFLOPS. [56] [57]

On June 18, 2012, IBM's Sequoia supercomputer system, based at the U.S. Lawrence Livermore National Laboratory (LLNL), reached 16 petaFLOPS, setting the world record and claiming first place in the latest TOP500 list. [58]

On November 12, 2012, the TOP500 list certified Titan as the world's fastest supercomputer per the LINPACK benchmark, at 17.59 petaFLOPS. [59] [60] It was developed by Cray Inc. at the Oak Ridge National Laboratory and combines AMD Opteron processors with "Kepler" NVIDIA Tesla graphics processing unit (GPU) technologies. [61] [62]

On June 10, 2013, China's Tianhe-2 was ranked the world's fastest with 33.86 petaFLOPS. [63]

On June 20, 2016, China's Sunway TaihuLight was ranked the world's fastest with 93 petaFLOPS on the LINPACK benchmark (out of 125 peak petaFLOPS). The system, which is almost exclusively based on technology developed in China, is installed at the National Supercomputing Center in Wuxi, and represents more performance than the next five most powerful systems on the TOP500 list combined. [64]

In June 2019, Summit, an IBM-built supercomputer now running at the Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL), captured the number one spot with a performance of 148.6 petaFLOPS on High Performance Linpack (HPL), the benchmark used to rank the TOP500 list. Summit has 4,356 nodes, each one equipped with two 22-core Power9 CPUs, and six NVIDIA Tesla V100 GPUs. [65]

Distributed computing records

Distributed computing uses the Internet to link personal computers to achieve more FLOPS:

Cost of computing

Hardware costs

DateApproximate USD per GFLOPSPlatform providing the lowest cost per GFLOPSComments
Unadjusted2022 [76]
1945$130 trillion$2 quadrillion ENIAC: $487,000 in 1945 and $7,916,000 in 2022.$487,000 / 0.0000000385  GFLOPS . First-generation (vacuum tube-based) electronic digital computer.
1961$20 billion$196 billionA basic installation of IBM 7030 Stretch had a cost at the time of US$7.78 million each.The IBM 7030 Stretch performs one floating-point multiply every 2.4  microseconds . [77] Second-generation (transistor-based) computer.
1984$20,000,000$100,000,000 Cray X-MP/48$15,000,000 / 0.8 GFLOPS. Third-generation (integrated circuit-based) computer.
1997$30,000$55,000Two 16-processor Beowulf clusters with Pentium Pro microprocessors [78]
April 2000$1,000$2,000 Bunyip Beowulf cluster Bunyip was the first sub-US$1/MFLOPS computing technology. It won the Gordon Bell Prize in 2000.
May 2000$640$1,000 KLAT2 KLAT2 was the first computing technology which scaled to large applications while staying under US$1/MFLOPS. [79]
August 2003$90$100KASY0KASY0 was the first sub-US$100/GFLOPS computing technology. [80]
August 2007$50$70MicrowulfAs of August 2007, this 26 GFLOPS "personal" Beowulf cluster can be built for $1256. [81]
March 2011$1.80$2HPU4ScienceThis $30,000 cluster was built using only commercially available "gamer" grade hardware. [82]
August 2012$0.75$1Quad AMD Radeon 7970 SystemA quad AMD Radeon 7970 desktop computer reaching 16 TFLOPS of single-precision, 4 TFLOPS of double-precision computing performance. Total system cost was $3000; built using only commercially available hardware. [83]
June 2013$0.22$0.3 Sony PlayStation 4 The Sony PlayStation 4 is listed as having a peak performance of 1.84  TFLOPS , at a price of $400 [84]
November 2013$0.16$0.2 AMD Sempron 145 & GeForce GTX 760 systemBuilt using commercially available parts, a system using one AMD Sempron 145 and three Nvidia GeForce GTX 760 reaches a total of 6.771 TFLOPS for a total cost of US$1,090.66. [85]
December 2013$0.12$0.15 Pentium G550 & Radeon R9 290 systemBuilt using commercially available parts. Intel Pentium G550 and AMD Radeon R9 290 tops out at 4.848 TFLOPS grand total of US$681.84. [86]
January 2015$0.08$0.1 Celeron G1830 & Radeon R9 295X2 systemBuilt using commercially available parts. Intel Celeron G1830 and AMD Radeon R9 295X2 tops out at over 11.5 TFLOPS at a grand total of US$902.57. [87] [88]
June 2017$0.06$0.07 AMD Ryzen 7 1700 & AMD Radeon Vega Frontier Edition systemBuilt using commercially available parts. AMD Ryzen 7 1700 CPU combined with AMD Radeon Vega FE cards in CrossFire tops out at over 50 TFLOPS at just under US$3,000 for the complete system. [89]
October 2017$0.03$0.04 Intel Celeron G3930 & AMD RX Vega 64 systemBuilt using commercially available parts. Three AMD RX Vega 64 graphics cards provide just over 75 TFLOPS half precision (38 TFLOPS SP or 2.6 TFLOPS DP when combined with the CPU) at ~$2,050 for the complete system. [90]
November 2020$0.03$0.03 AMD Ryzen 3600 & 3× NVIDIA RTX 3080 systemAMD Ryzen 3600 @ 484 GFLOPS & $199.99

3× NVIDIA RTX 3080 @ 29,770 GFLOPS each & $699.99

Total system GFLOPS = 89,794 / TFLOPS= 89.2794

Total system cost incl. realistic but low cost parts; matched with other example = $2839 [91]

US$/GFLOP = $0.0314

November 2020$0.04$0.04 PlayStation 5 The Sony PlayStation 5 Digital Edition is listed as having a peak performance of 10.28 TFLOPS (20.58 TFLOPS at half precision) at a retail price of $399. [92]
November 2020$0.04$0.04 Xbox Series X Microsoft's Xbox Series X is listed as having a peak performance of 12.15 TFLOPS (24.30 TFLOPS at half precision) at a retail price of $499. [93]
September 2022$0.02$0.02 RTX 4090 Nvidia's RTX 4090 is listed as having a peak performance of 82.6 TFLOPS (1.32 PFLOPS at 8-bit precision) at a retail price of $1599. [94]
May 2023$0.01$0.01 Radeon RX 7600 AMD's RX 7600 is listed as having a peak performance of 21.5 TFLOPS at a retail price of $269. [95]


See also

Related Research Articles

<span class="mw-page-title-main">Supercomputer</span> Type of extremely powerful computer

A supercomputer is a type of computer with a high level of performance as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second (FLOPS) instead of million instructions per second (MIPS). Since 2017, supercomputers have existed, which can perform over 1017 FLOPS (a hundred quadrillion FLOPS, 100 petaFLOPS or 100 PFLOPS). For comparison, a desktop computer has performance in the range of hundreds of gigaFLOPS (1011) to tens of teraFLOPS (1013). Since November 2017, all of the world's fastest 500 supercomputers run on Linux-based operating systems. Additional research is being conducted in the United States, the European Union, Taiwan, Japan, and China to build faster, more powerful and technologically superior exascale supercomputers.

Cray Inc., a subsidiary of Hewlett Packard Enterprise, is an American supercomputer manufacturer headquartered in Seattle, Washington. It also manufactures systems for data storage and analytics. Several Cray supercomputer systems are listed in the TOP500, which ranks the most powerful supercomputers in the world.

<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">PlayStation 3 cluster</span> Supercomputer platform

A PlayStation 3 cluster is a distributed system computer composed primarily of PlayStation 3 video game consoles.

In computing, performance per watt is a measure of the energy efficiency of a particular computer architecture or computer hardware. Literally, it measures the rate of computation that can be delivered by a computer for every watt of power consumed. This rate is typically measured by performance on the LINPACK benchmark when trying to compare between computing systems: an example using this is the Green500 list of supercomputers. Performance per watt has been suggested to be a more sustainable measure of computing than Moore's Law.

The Green500 is a biannual ranking of supercomputers, from the TOP500 list of supercomputers, in terms of energy efficiency. The list measures performance per watt using the TOP500 measure of high performance LINPACK benchmarks at double-precision floating-point format.

The National Center for Computational Sciences (NCCS) is a United States Department of Energy (DOE) Leadership Computing Facility that houses the Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility charged with helping researchers solve challenging scientific problems of global interest with a combination of leading high-performance computing (HPC) resources and international expertise in scientific computing.

<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

Tianhe-I, Tianhe-1, or TH-1 is a supercomputer capable of an Rmax of 2.5 peta FLOPS. Located at the National Supercomputing Center of Tianjin, China, it was the fastest computer in the world from October 2010 to June 2011 and was one of the few petascale supercomputers in the world.

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.

SAGA-220 is a supercomputer built by the Indian Space Research Organisation (ISRO).

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

The history of supercomputing goes back to the 1960s when a series of computers at Control Data Corporation (CDC) were designed by Seymour Cray to use innovative designs and parallelism to achieve superior computational peak performance. The CDC 6600, released in 1964, is generally considered the first supercomputer. However, some earlier computers were considered supercomputers for their day such as the 1954 IBM NORC in the 1950s, and in the early 1960s, the UNIVAC LARC (1960), the IBM 7030 Stretch (1962), and the Manchester Atlas (1962), all of which were of comparable power.

<span class="mw-page-title-main">Supercomputing in Europe</span> Overview of supercomputing in Europe

Several centers for supercomputing exist across Europe, and distributed access to them is coordinated by European initiatives to facilitate high-performance computing. One such initiative, the HPC Europa project, fits within the Distributed European Infrastructure for Supercomputing Applications (DEISA), which was formed in 2002 as a consortium of eleven supercomputing centers from seven European countries. Operating within the CORDIS framework, HPC Europa aims to provide access to supercomputers across Europe.

<span class="mw-page-title-main">Xeon Phi</span> Series of x86 manycore processors from Intel

Xeon Phi was a series of x86 manycore processors designed and made by Intel. It was intended for use in supercomputers, servers, and high-end workstations. Its architecture allowed use of standard programming languages and application programming interfaces (APIs) such as OpenMP.

<span class="mw-page-title-main">Titan (supercomputer)</span> American supercomputer

Titan or OLCF-3 was a supercomputer built by Cray at Oak Ridge National Laboratory for use in a variety of science projects. Titan was an upgrade of Jaguar, a previous supercomputer at Oak Ridge, that uses graphics processing units (GPUs) in addition to conventional central processing units (CPUs). Titan was the first such hybrid to perform over 10 petaFLOPS. The upgrade began in October 2011, commenced stability testing in October 2012 and it became available to researchers in early 2013. The initial cost of the upgrade was US$60 million, funded primarily by the United States Department of Energy.

XK7 is a supercomputing platform, produced by Cray, launched on October 29, 2012. XK7 is the second platform from Cray to use a combination of central processing units ("CPUs") and graphical processing units ("GPUs") for computing; the hybrid architecture requires a different approach to programming to that of CPU-only supercomputers. Laboratories that host XK7 machines host workshops to train researchers in the new programming languages needed for XK7 machines. The platform is used in Titan, the world's second fastest supercomputer in the November 2013 list as ranked by the TOP500 organization. Other customers include the Swiss National Supercomputing Centre which has a 272 node machine and Blue Waters has a machine that has Cray XE6 and XK7 nodes that performs at approximately 1 petaFLOPS (1015 floating-point operations per second).

<span class="mw-page-title-main">Cray XC40</span> Supercomputer manufactured by Cray

The Cray XC40 is a massively parallel multiprocessor supercomputer manufactured by Cray. It consists of Intel Haswell Xeon processors, with optional Nvidia Tesla or Intel Xeon Phi accelerators, connected together by Cray's proprietary "Aries" interconnect, stored in air-cooled or liquid-cooled cabinets. The XC series supercomputers are available with the Cray DataWarp applications I/O accelerator technology.

<span class="mw-page-title-main">Summit (supercomputer)</span> Supercomputer developed by IBM

Summit or OLCF-4 is a supercomputer developed by IBM for use at Oak Ridge Leadership Computing Facility (OLCF), a facility at the Oak Ridge National Laboratory, capable of 200 petaFLOPS thus making it the 5th fastest supercomputer in the world after Frontier (OLCF-5), Fugaku, LUMI, and Leonardo, with Frontier being the fastest. It held the number 1 position from November 2018 to June 2020. Its current LINPACK benchmark is clocked at 148.6 petaFLOPS.

<span class="mw-page-title-main">Aurora (supercomputer)</span> Planned supercomputer

Aurora is a supercomputer that was sponsored by the United States Department of Energy (DOE) and designed by Intel and Cray for the Argonne National Laboratory. It has been the second fastest supercomputer in the world since 2023. It is expected that after optimizing its performance it will exceed 2 ExaFLOPS, making it the fastest computer ever.

References

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  2. ↑ Floating Point Retrieved on December 25, 2009.
  3. ↑ Summary: Fixed-point (integer) vs floating-point Retrieved on December 25, 2009.
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  6. ↑ Data manipulation and math calculation. Retrieved on December 25, 2009.
  7. ↑ Kuck, D. J. (1974). Computer System Capacity Fundamentals. U.S. Department of Commerce, National Bureau of Standards.
  8. ↑ "Nodes, Sockets, Cores and FLOPS, Oh, My" by Dr. Mark R. Fernandez, Ph.D.
  9. ↑ "FREQUENTLY ASKED QUESTIONS". top500.org. Retrieved June 23, 2020.
  10. ↑ "Floating-Point Operations Per Second (FLOPS)".
  11. 1 2 "home.iae.nl".
  12. 1 2 "Computing Power throughout History". alternatewars.com. Retrieved February 13, 2021.
  13. 1 2 3 4 5 Dolbeau, Romain (2017). "Theoretical Peak FLOPS per instruction set: a tutorial". Journal of Supercomputing. 74 (3): 1341–1377. doi:10.1007/s11227-017-2177-5. S2CID   3540951.
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