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Wikipedia

FLOPS

January 01, 1970

For other uses, see Flop.
"Operations per second" redirects here. Not to be confused with Instructions per second.

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.[citation needed]

Floating-point arithmetic edit

Multipliers for flops
Name Unit Value
kiloFLOPS kFLOPS 103
megaFLOPS MFLOPS 106
gigaFLOPS GFLOPS 109
teraFLOPS TFLOPS 1012
petaFLOPS PFLOPS 1015
exaFLOPS EFLOPS 1018
zettaFLOPS ZFLOPS 1021
yottaFLOPS YFLOPS 1024
ronnaFLOPS RFLOPS 1027
quettaFLOPS QFLOPS 1030

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 edit

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 edit

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

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 edit

Floating-point operations per clock cycle per core[10]
Microarchitecture Instruction set architecture FP64 FP32 FP16
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) 2 4 ?
Intel P6 Pentium M SSE2 (64-bit) 1 2 ?
SSE3 (64-bit) 2 4 ?
4 8 ?
Intel Atom (Bonnell, Saltwell, Silvermont and Goldmont) SSE3 (128-bit) 2 4 ?
Intel Sandy Bridge (Sandy Bridge, Ivy Bridge) AVX (256-bit) 8 16 0
AVX2 & FMA (256-bit) 16 32 0
Intel Xeon Phi (Knights Corner) IMCI (512-bit) 16 32 0
AVX-512 & FMA (512-bit) 32 64 0
AMD CPU
AMD Bobcat AMD64 (64-bit) 2 4 0
4 8 0
AMD K10 SSE4/4a (128-bit) 4 8 0
AMD Bulldozer[13] (Piledriver, Steamroller, Excavator)
  • AVX (128-bit) (Bulldozer, Steamroller)
  • AVX2 (128-bit) (Excavator)
  • FMA3 (Bulldozer)[14]
  • FMA3/4 (Piledriver, Excavator)
 4 8 0
AVX2 & FMA (128-bit, 256-bit decoding)[18] 8 16 0
AVX2 & FMA (256-bit) 16 32 0
ARM CPU
ARM Cortex-A7, A9, A15 ARMv7 1 8 0
ARM Cortex-A32, A35 ARMv8 2 8 0
ARM Cortex-A53, A55, A57,[13] A72, A73, A75 ARMv8 4 8 0
ARM Cortex-A76, A77, A78 ARMv8 8 16 0
ARM Cortex-X1 ARMv8 16 32 ?
Qualcomm Krait ARMv8 1 8 0
Qualcomm Kryo (1xx - 3xx) ARMv8 2 8 0
Qualcomm Kryo (4xx - 5xx) ARMv8 8 16 0
Samsung Exynos M1 and M2 ARMv8 2 8 0
Samsung Exynos M3 and M4 ARMv8 3 12 0
IBM PowerPC A2 (Blue Gene/Q) ? 8 8 (as FP64) 0
Hitachi SH-4[20][21] SH-4 1 7 0
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) 2 0
Nvidia Fermi (only Quadro 600–2000) PTX 1/8 2 0
Nvidia Fermi (only Quadro 4000–7000, Tesla) PTX 1 2 0
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) 2 0
Nvidia Kepler (GeForce GTX Titan and Titan Black, Quadro K6000, Tesla (except K10)) PTX 2/3 2 0
  • Nvidia Maxwell
  • Nvidia Pascal (all except Quadro GP100 and Tesla P100)
 PTX 1/16 2 1/32
Nvidia Pascal (only Quadro GP100 and Tesla P100) PTX 1 2 4
Nvidia Volta[22] PTX 1 2 (FP32) + 2 (INT32) 16
Nvidia Turing (only GeForce 16XX) PTX 1/16 2 (FP32) + 2 (INT32) 4
Nvidia Turing (all except GeForce 16XX) PTX 1/16 2 (FP32) + 2 (INT32) 16
Nvidia Ampere[23][24] (only Tesla A100/A30) PTX 2 2 (FP32) + 2 (INT32) 32
Nvidia Ampere (all GeForce and Quadro, Tesla A40/A10) PTX 1/32 2 (FP32) + 0 (INT32) or 1 (FP32) + 1 (INT32) 8
AMD GPU
AMD TeraScale 1 (Radeon HD 4000 series) TeraScale 1 0.4 2 ?
AMD TeraScale 2 (Radeon HD 5000 series) TeraScale 2 1 2 ?
AMD TeraScale 3 (Radeon HD 6000 series) TeraScale 3 1 4 ?
AMD GCN (only Radeon Pro W 8100–9100) GCN 1 2 ?
AMD GCN (all except Radeon Pro W 8100–9100, Vega 10–20) GCN 1/8 2 4
AMD GCN Vega 10 GCN 1/8 2 4
AMD GCN Vega 20 (only Radeon VII) GCN 1/2 (locked by driver, 1 in hardware) 2 4
AMD GCN Vega 20 (only Radeon Instinct MI50 / MI60 and Radeon Pro VII) GCN 1 2 4
RDNA 1/8 2 4
AMD RDNA3 RDNA 1/8? 4 8?
AMD CDNA CDNA 1 4 (Tensor)[27] 16
AMD CDNA 2 CDNA 2 4 (Tensor) 4 (Tensor) 16
Intel GPU
Intel Xe-LP (Iris Xe MAX)[28] Xe 1/2? 2 4
Intel Xe-HPG (Arc Alchemist)[28] Xe 0 2 16
Intel Xe-HPC (Ponte Vecchio)[29] Xe 2 2 32
Qualcomm GPU
Qualcomm Adreno 5x0 Adreno 5xx 1 2 4
Qualcomm Adreno 6x0 Adreno 6xx 1 2 4
Graphcore
Graphcore Colossus GC2[30][31] ? 0 16 64
  • Graphcore Colossus GC200 Mk2[32]
  • Graphcore Bow-2000[33]
 ? 0 32 128
Supercomputer
ENIAC @ 100 kHz in 1945 0.004[34] (~0.00000003 FLOPS/W)
48-bit processor @ 208 kHz in CDC 1604 in 1960
60-bit processor @ 10 MHz in CDC 6600 in 1964 0.3 (FP60)
60-bit processor @ 10 MHz in CDC 7600 in 1967 1.0 (FP60)
Cray-1 @ 80 MHz in 1976 2 (700 FLOPS/W)
CDC Cyber 205 @ 50 MHz in 1981

FORTRAN compiler (ANSI 77 with vector extensions)

8 16
Transputer IMS T800-20 @ 20 MHz in 1987 0.08[35]
Parallella E16 @ 1000 MHz in 2012 2[36] (5.0 GFLOPS/W)[37]
Parallella E64 @ 800 MHz in 2012 2[38] (50.0 GFLOPS/W)[37]
Microarchitecture Instruction set architecture FP64 FP32 FP16

Performance records edit

Single computer records edit

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 was installed at the National Supercomputing Center in Wuxi, and represented more performance than the next five most powerful systems on the TOP500 list did at the time 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]

In June 2022, the United States' Frontier is the most powerful supercomputer on TOP500, reaching 1102 petaFlops (1.102 exaFlops) on the LINPACK benchmarks. [66]

Distributed computing records edit

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

  • As of April 2020, the Folding@home network has over 2.3 exaFLOPS of total computing power.[67][68][69][70] It is the most powerful distributed computer network, being the first ever to break 1 exaFLOPS of total computing power. This level of performance is primarily enabled by the cumulative effort of a vast array of powerful GPU and CPU units.[71]
  • As of December 2020, the entire BOINC network averages about 31 petaFLOPS.[72]
  • As of June 2018, SETI@home, employing the BOINC software platform, averages 896 teraFLOPS.[73]
  • As of June 2018, Einstein@Home, a project using the BOINC network, is crunching at 3 petaFLOPS.[74]
  • As of June 2018, MilkyWay@home, using the BOINC infrastructure, computes at 847 teraFLOPS.[75]
  • As of June 2020, GIMPS, searching for Mersenne primes, is sustaining 1,354 teraFLOPS.[76]

Cost of computing edit

Hardware costs edit

Date Approximate USD per GFLOPS Platform providing the lowest cost per GFLOPS Comments
Unadjusted 2023[77]
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 $204 billion A 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.[78] 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 $57,000 Two 16-processor Beowulf clusters with Pentium Pro microprocessors[79]
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.[80]
August 2003 $90 $100 KASY0 KASY0 was the first sub-US$100/GFLOPS computing technology.[81]
August 2007 $50 $70 Microwulf As of August 2007, this 26 GFLOPS "personal" Beowulf cluster can be built for $1256.[82]
March 2011 $1.80 $2 HPU4Science This $30,000 cluster was built using only commercially available "gamer" grade hardware.[83]
August 2012 $0.75 $1 Quad AMD Radeon 7970 System A 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.[84]
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[85]
November 2013 $0.16 $0.21 AMD Sempron 145 & GeForce GTX 760 system Built 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.[86]
December 2013 $0.12 $0.16 Pentium G550 & Radeon R9 290 system Built using commercially available parts. Intel Pentium G550 and AMD Radeon R9 290 tops out at 4.848 TFLOPS grand total of US$681.84.[87]
January 2015 $0.08 $0.1 Celeron G1830 & Radeon R9 295X2 system Built 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.[88][89]
June 2017 $0.06 $0.07 AMD Ryzen 7 1700 & AMD Radeon Vega Frontier Edition system Built 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.[90]
October 2017 $0.03 $0.04 Intel Celeron G3930 & AMD RX Vega 64 system Built 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.[91]
November 2020 $0.03 $0.03 AMD Ryzen 3600 & 3× NVIDIA RTX 3080 system AMD 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[92]

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.[93]
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.[94]
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.[95]
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.[96]


See also edit

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January 01, 1970
flops, other, uses, flop, operations, second, redirects, here, confused, with, instructions, second, floating, point, operations, second, flops, flop, measure, computer, performance, computing, useful, fields, scientific, computations, that, require, floating,. For other uses see Flop Operations per second redirects here Not to be confused with Instructions per second 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 citation needed Contents 1 Floating point arithmetic 1 1 Dynamic range and precision 1 2 Computational performance 2 Floating point operations per clock cycle for various processors 3 Performance records 3 1 Single computer records 3 2 Distributed computing records 4 Cost of computing 4 1 Hardware costs 5 See also 6 ReferencesFloating point arithmetic editMultipliers for flops Name Unit Value kiloFLOPS kFLOPS 103 megaFLOPS MFLOPS 106 gigaFLOPS GFLOPS 109 teraFLOPS TFLOPS 1012 petaFLOPS PFLOPS 1015 exaFLOPS EFLOPS 1018 zettaFLOPS ZFLOPS 1021 yottaFLOPS YFLOPS 1024 ronnaFLOPS RFLOPS 1027 quettaFLOPS QFLOPS 1030 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 edit 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 edit 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 nbsp FLOPS by the largest supercomputer over time FLOPS on an HPC system can be calculated using this equation 8 FLOPS racks nodes rack sockets node cores socket cycles second FLOPs cycle displaystyle text FLOPS text racks times frac text nodes text rack times frac text sockets text node times frac text cores text socket times frac text cycles text second times frac text FLOPs text cycle nbsp This can be simplified to the most common case a computer that has exactly 1 CPU FLOPS cores cycles second FLOPs cycle displaystyle text FLOPS text cores times frac text cycles text second times frac text FLOPs text cycle nbsp 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 editFloating point operations per clock cycle per core 10 Microarchitecture Instruction set architecture FP64 FP32 FP16 Intel CPU Intel 80486 x87 32 bit 0 128 11 Intel P5 Pentium Intel P6 Pentium Pro x87 32 bit 0 5 11 Intel P5 Pentium MMX Intel P6 Pentium II MMX 64 bit 1 12 Intel P6 Pentium III SSE 64 bit 2 12 Intel NetBurst Pentium 4 Willamette Northwood SSE2 64 bit 2 4 Intel P6 Pentium M SSE2 64 bit 1 2 Intel NetBurst Pentium 4 Prescott Cedar Mill Intel NetBurst Pentium D Smithfield Presler Intel P6 Core Yonah SSE3 64 bit 2 4 Intel Core Merom Penryn Intel Nehalem 13 Nehalem Westmere SSSE3 128 bit SSE4 128 bit 4 8 Intel Atom Bonnell Saltwell Silvermont and Goldmont SSE3 128 bit 2 4 Intel Sandy Bridge Sandy Bridge Ivy Bridge AVX 256 bit 8 16 0 Intel Haswell 13 Haswell Devil s Canyon Broadwell Intel Skylake Skylake Kaby Lake Coffee Lake Comet Lake Whiskey Lake Amber Lake AVX2 amp FMA 256 bit 16 32 0 Intel Xeon Phi Knights Corner IMCI 512 bit 16 32 0 Intel Skylake X Skylake X Cascade Lake Intel Xeon Phi Knights Landing Knights Mill Intel Ice Lake Tiger Lake and Rocket Lake AVX 512 amp FMA 512 bit 32 64 0 AMD CPU AMD Bobcat AMD64 64 bit 2 4 0 AMD Jaguar AMD Puma 4 8 0 AMD K10 SSE4 4a 128 bit 4 8 0 AMD Bulldozer 13 Piledriver Steamroller Excavator AVX 128 bit Bulldozer Steamroller AVX2 128 bit Excavator FMA3 Bulldozer 14 FMA3 4 Piledriver Excavator 4 8 0 AMD Zen Ryzen 1000 series Threadripper 1000 series Epyc Naples AMD Zen 13 15 16 17 Ryzen 2000 series Threadripper 2000 series AVX2 amp FMA 128 bit 256 bit decoding 18 8 16 0 AMD Zen 2 19 Ryzen 3000 series Threadripper 3000 series Epyc Rome AMD Zen 3 Ryzen 5000 series Epyc Milan AVX2 amp FMA 256 bit 16 32 0 ARM CPU ARM Cortex A7 A9 A15 ARMv7 1 8 0 ARM Cortex A32 A35 ARMv8 2 8 0 ARM Cortex A53 A55 A57 13 A72 A73 A75 ARMv8 4 8 0 ARM Cortex A76 A77 A78 ARMv8 8 16 0 ARM Cortex X1 ARMv8 16 32 Qualcomm Krait ARMv8 1 8 0 Qualcomm Kryo 1xx 3xx ARMv8 2 8 0 Qualcomm Kryo 4xx 5xx ARMv8 8 16 0 Samsung Exynos M1 and M2 ARMv8 2 8 0 Samsung Exynos M3 and M4 ARMv8 3 12 0 IBM PowerPC A2 Blue Gene Q 8 8 as FP64 0 Hitachi SH 4 20 21 SH 4 1 7 0 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 2 0 Nvidia Fermi only Quadro 600 2000 PTX 1 8 2 0 Nvidia Fermi only Quadro 4000 7000 Tesla PTX 1 2 0 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 2 0 Nvidia Kepler GeForce GTX Titan and Titan Black Quadro K6000 Tesla except K10 PTX 2 3 2 0 Nvidia Maxwell Nvidia Pascal all except Quadro GP100 and Tesla P100 PTX 1 16 2 1 32 Nvidia Pascal only Quadro GP100 and Tesla P100 PTX 1 2 4 Nvidia Volta 22 PTX 1 2 FP32 2 INT32 16 Nvidia Turing only GeForce 16XX PTX 1 16 2 FP32 2 INT32 4 Nvidia Turing all except GeForce 16XX PTX 1 16 2 FP32 2 INT32 16 Nvidia Ampere 23 24 only Tesla A100 A30 PTX 2 2 FP32 2 INT32 32 Nvidia Ampere all GeForce and Quadro Tesla A40 A10 PTX 1 32 2 FP32 0 INT32 or 1 FP32 1 INT32 8 AMD GPU AMD TeraScale 1 Radeon HD 4000 series TeraScale 1 0 4 2 AMD TeraScale 2 Radeon HD 5000 series TeraScale 2 1 2 AMD TeraScale 3 Radeon HD 6000 series TeraScale 3 1 4 AMD GCN only Radeon Pro W 8100 9100 GCN 1 2 AMD GCN all except Radeon Pro W 8100 9100 Vega 10 20 GCN 1 8 2 4 AMD GCN Vega 10 GCN 1 8 2 4 AMD GCN Vega 20 only Radeon VII GCN 1 2 locked by driver 1 in hardware 2 4 AMD GCN Vega 20 only Radeon Instinct MI50 MI60 and Radeon Pro VII GCN 1 2 4 AMD RDNA 25 26 AMD RDNA 2 RDNA 1 8 2 4 AMD RDNA3 RDNA 1 8 4 8 AMD CDNA CDNA 1 4 Tensor 27 16 AMD CDNA 2 CDNA 2 4 Tensor 4 Tensor 16 Intel GPU Intel Xe LP Iris Xe MAX 28 Xe 1 2 2 4 Intel Xe HPG Arc Alchemist 28 Xe 0 2 16 Intel Xe HPC Ponte Vecchio 29 Xe 2 2 32 Qualcomm GPU Qualcomm Adreno 5x0 Adreno 5xx 1 2 4 Qualcomm Adreno 6x0 Adreno 6xx 1 2 4 Graphcore Graphcore Colossus GC2 30 31 0 16 64 Graphcore Colossus GC200 Mk2 32 Graphcore Bow 2000 33 0 32 128 Supercomputer ENIAC 100 kHz in 1945 0 004 34 0 00000003 FLOPS W 48 bit processor 208 kHz in CDC 1604 in 1960 60 bit processor 10 MHz in CDC 6600 in 1964 0 3 FP60 60 bit processor 10 MHz in CDC 7600 in 1967 1 0 FP60 Cray 1 80 MHz in 1976 2 700 FLOPS W CDC Cyber 205 50 MHz in 1981 FORTRAN compiler ANSI 77 with vector extensions 8 16 Transputer IMS T800 20 20 MHz in 1987 0 08 35 Parallella E16 1000 MHz in 2012 2 36 5 0 GFLOPS W 37 Parallella E64 800 MHz in 2012 2 38 50 0 GFLOPS W 37 Microarchitecture Instruction set architecture FP64 FP32 FP16Performance records editSingle computer records edit 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 update 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 was installed at the National Supercomputing Center in Wuxi and represented more performance than the next five most powerful systems on the TOP500 list did at the time 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 In June 2022 the United States Frontier is the most powerful supercomputer on TOP500 reaching 1102 petaFlops 1 102 exaFlops on the LINPACK benchmarks 66 Distributed computing records edit Distributed computing uses the Internet to link personal computers to achieve more FLOPS As of April 2020 update the Folding home network has over 2 3 exaFLOPS of total computing power 67 68 69 70 It is the most powerful distributed computer network being the first ever to break 1 exaFLOPS of total computing power This level of performance is primarily enabled by the cumulative effort of a vast array of powerful GPU and CPU units 71 As of December 2020 update the entire BOINC network averages about 31 petaFLOPS 72 As of June 2018 update SETI home employing the BOINC software platform averages 896 teraFLOPS 73 As of June 2018 update Einstein Home a project using the BOINC network is crunching at 3 petaFLOPS 74 As of June 2018 update MilkyWay home using the BOINC infrastructure computes at 847 teraFLOPS 75 As of June 2020 update GIMPS searching for Mersenne primes is sustaining 1 354 teraFLOPS 76 Cost of computing editHardware costs edit Date Approximate USD per GFLOPS Platform providing the lowest cost per GFLOPS Comments Unadjusted 2023 77 1945 130 trillion 2 quadrillion ENIAC 487 000 in 1945 and 7 916 000 in 2022 487 000 0 000000 0385 GFLOPS First generation vacuum tube based electronic digital computer 1961 20 billion 204 billion A 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 78 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 57 000 Two 16 processor Beowulf clusters with Pentium Pro microprocessors 79 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 80 August 2003 90 100 KASY0 KASY0 was the first sub US 100 GFLOPS computing technology 81 August 2007 50 70 Microwulf As of August 2007 this 26 GFLOPS personal Beowulf cluster can be built for 1256 82 March 2011 1 80 2 HPU4Science This 30 000 cluster was built using only commercially available gamer grade hardware 83 August 2012 0 75 1 Quad AMD Radeon 7970 System A 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 84 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 85 November 2013 0 16 0 21 AMD Sempron 145 amp GeForce GTX 760 system Built 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 86 December 2013 0 12 0 16 Pentium G550 amp Radeon R9 290 system Built using commercially available parts Intel Pentium G550 and AMD Radeon R9 290 tops out at 4 848 TFLOPS grand total of US 681 84 87 January 2015 0 08 0 1 Celeron G1830 amp Radeon R9 295X2 system Built 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 88 89 June 2017 0 06 0 07 AMD Ryzen 7 1700 amp AMD Radeon Vega Frontier Edition system Built 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 90 October 2017 0 03 0 04 Intel Celeron G3930 amp AMD RX Vega 64 system Built 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 91 November 2020 0 03 0 03 AMD Ryzen 3600 amp 3 NVIDIA RTX 3080 system AMD Ryzen 3600 484 GFLOPS amp 199 99 3 NVIDIA RTX 3080 29 770 GFLOPS each amp 699 99Total system GFLOPS 89 794 TFLOPS 89 2794Total system cost incl realistic but low cost parts matched with other example 2839 92 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 93 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 94 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 95 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 96 See also editComputer performance by orders of magnitude Exascale computing Gordon Bell Prize LINPACK benchmarks Moore s law Multiply accumulate operation Performance per watt FLOPS per watt SPECfp SPECint SUPS TOP500References edit Understand measures of supercomputer performance and storage system capacity kb iu edu Retrieved March 23 2024 Floating Point Retrieved on December 25 2009 Summary Fixed point integer vs floating point Archived December 31 2009 at the Wayback Machine Retrieved on December 25 2009 NASA Technical Note National Aeronautics and Space Administration 1970 Fixed versus floating point Retrieved on December 25 2009 Data manipulation and math calculation Retrieved on December 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