calculations that make heavy use of floating-point calculations. For such cases it is a more

accurate measure than the generic instructions per second.

Since the final S stands for "second", conservative speakers consider "FLOPS" as both the singular

and plural of the term, although the singular "FLOP" is frequently encountered. Alternatively,

the singular FLOP is used as an abbreviation for "FLoating-point OPeration", and a flop

count is a count of these operations. In this context, "flops" is simply the plural rather

than a rate, which would then be "flop/s". The expression 1 flops is actually interpreted

as .

Computing One can calculate FLOPS using this equation:

Most microprocessors today can do 4 FLOPs per clock cycle. Therefore, a single-core

2.5 GHz processor has a theoretical performance of 10 billion FLOPS = 10 GFLOPS.

Note: In this context, sockets is referring to chip sockets on a motherboard, in other

words, how many computer chips are in use, with each chip having one or more cores on

it. This equation only applies to one very specific hardware architecture and it ignores

limits imposed by memory bandwidth and other constraints. In general, GigaFLOPS are not

determined by theoretical calculations such as this one; instead, they are measured by

benchmarks of actual performance/throughput. Because this equation ignores all sources

of overhead, in the real world, one will never get actual performance that is anywhere near

to what this equation predicts. Records

Single computer records In late 1996, Intel's ASCI Red was the world's

first computer to achieve one TFLOPS 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.” NEC's SX-9 supercomputer was the world's first

vector processor to exceed 100 gigaFLOPS per single core.

For comparison, a handheld calculator performs relatively few FLOPS. A computer response

time below 0.1 second in a calculation context is usually perceived as instantaneous by a

human operator, so a simple calculator needs only about 10 FLOPS to be considered functional.

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 TFLOPS at 3.13 GHz. The 80-core chip can raise this

result to 2 TFLOPS at 6.26 GHz, although the thermal dissipation at this frequency

exceeds 190 watts. 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. When configured to do so, it can reach speeds in excess of

three petaFLOPS. 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.

The Cray XT4 hit second place with 101.7 teraFLOPS. On October 25, 2007, NEC Corporation of Japan

issued a press release announcing its SX series model SX-9, 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,

the most powerful supercomputing system in the world for open science research, which

operates at sustained speed of .5 petaFLOPS. On May 25, 2008, an American supercomputer

built by IBM, named 'Roadrunner', reached the computing milestone of one petaflops by

processing more than 1.026 quadrillion calculations per second. It headed the June 2008 and November

2008 TOP500 list of the most powerful supercomputers. The computer is located at Los Alamos National

Laboratory in New Mexico, and the computer's name refers to the New Mexico state bird,

the Greater Roadrunner. In June 2008, AMD released ATI Radeon HD4800

series, which are reported to be the first GPUs to achieve one teraFLOPS scale. 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 XT

Jaguar supercomputer at the Department of Energy’s Oak Ridge National Laboratory raised

the system's computing power to a peak 1.64 “petaflops,” or a quadrillion mathematical

calculations per second, 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, which is the global standard for ranking supercomputers. 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. In October 2010, China unveiled the Tianhe-I,

a supercomputer that operates at a peak computing rate of 2.5 petaflops.

As of 2010, the fastest six-core PC processor reaches 109 gigaFLOPS in double precision

calculations. GPUs are considerably more powerful. For example, Nvidia Tesla C2050 GPU computing

processors perform around 515 gigaFLOPS in double precision calculations, and the AMD

FireStream 9270 peaks at 240 gigaFLOPS. In single precision performance, Nvidia Tesla

C2050 computing processors perform around 1.03 teraFLOPS and the AMD FireStream 9270

cards peak at 1.2 teraFLOPS. Both Nvidia and AMD's consumer gaming GPUs may reach higher

FLOPS. For example, AMD’s HemlockXT 5970 reaches 928 gigaFLOPS in double precision

calculations with two GPUs on board and the Nvidia GTX 480 reaches 672 gigaFLOPS with

one GPU on board. On December 2, 2010, the US Air Force unveiled

a defense supercomputer made up of 1,760 PlayStation 3 consoles that can run 500 trillion floating-point

operations per second. In November 2011, it was announced that Japan

had achieved 10.51 petaflops with its K computer. It is still under development and software

performance tuning is currently underway. 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, 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 TeraFlop on a wide range of DGEMM operations. Intel emphasized

during the demonstration that this was a sustained TeraFlop, and that it was the first general

purpose processor to ever cross a TeraFlop. On June 18, 2012, IBM's Sequoia supercomputer

system, based at the U.S. Lawrence Livermore National Laboratory, reached 16 petaFLOPS,

setting the world record and claiming first place in the latest TOP500 list.

On November 12, 2012, the TOP500 list certified Titan as the world's fastest supercomputer

per the LINPACK benchmark, at 17.59 petaFLOPS. It was developed by Cray Inc. at the Oak Ridge

National Laboratory and combines AMD Opteron processors with “Kepler” NVIDIA Tesla

graphic processing unit technologies. On June 10, 2013, China's Tianhe-2 was ranked

the world's fastest with a record of 33.86 petaflops.

On April 8, 2014, AMD launched R9 295X2, a dual R9 290X in a single PCB, with 11.6 TFlops.

Distributed computing records Distributed computing uses the Internet to

link personal computers to achieve more FLOPS: Folding@home is sustaining over 20.7 native

petaFLOPS as of June 2014 or 43.1 x86 petaFLOPS. It is the first computing project of any kind

to cross the 1, 2, 3, 4, and 5 native petaFLOPS milestone. This level of performance is primarily

enabled by the cumulative effort of a vast array of powerful GPU and CPU units.

As of July 2014, The entire BOINC network averages about 5.6 petaFLOPS.

As of July 2014, SETI@Home, employing the BOINC software platform, averages 681 teraFLOPS.

As of July 2014, Einstein@Home, a project using the BOINC network , is crunching at

492 teraFLOPS. As of July 2014, MilkyWay@Home, using the

BOINC infrastructure, computes at 471 teraFLOPS. As of July 2014, GIMPS, is searching for Mersenne

primes and sustaining 173 teraFLOPS. Future developments

In 2008, James Bamford's book The Shadow Factory reported that NSA told the Pentagon it would

need an exaflop computer by 2018. Given the current speed of progress, supercomputers

are projected to reach 1 exaFLOPS in 2019. Cray, Inc. announced in December 2009 a plan

to build a 1 EFLOPS supercomputer before 2020. Erik P. DeBenedictis of Sandia National Laboratories

theorizes that a zettaFLOPS computer is required to accomplish full weather modeling of two

week time span. Such systems might be built around 2030.

In India, ISRO and Indian Institute of Science have stated that they have planned to make

a 132.8 EFLOPS supercomputer by 2017, 100 times faster than any supercomputer ever planned.

They have estimated that the project would cost US $2 billion, which the state has budgeted.

Cost of computing Hardware costs

The following is a list of examples of computers that demonstrates how drastically performance

has increased and price has decreased. The "cost per GFLOPS" is the cost for a set of

hardware that would theoretically operate at one billion floating-point operations per

second. During the era when no single computing platform was able to achieve one GFLOPS, this

table lists the total cost for multiple instances of a fast computing platform which speed sums

to one GFLOPS. Otherwise, the least expensive computing platform able to achieve one GFLOPS

is listed.

The trend toward placing ever more transistors inexpensively on an integrated circuit follows

Moore's law. This trend explains the rising speed and falling cost of computer processing.

Operation costs In energy cost, according to the Green500

list, as of June 2011 the most efficient TOP500 supercomputer runs at 2097.19 MFLOPS per watt.

This translates to an energy requirement of 0.477 watts per GFLOPS, however this energy

requirement will be much greater for less efficient supercomputers.

Hardware costs for low cost supercomputers may be less significant than energy costs

when running continuously for several years. Floating-point operation and integer operation

FLOPS measures the computing ability of a computer. An example of a floating-point operation

is the calculation of mathematical equations; as such, FLOPS is a useful measure of supercomputer

performance. MIPS is used to measure the integer performance of a computer. Examples of integer

operation include data movement or value testing. MIPS as a performance benchmark is adequate

for the computer when it is used in database query, word processing, spreadsheets, or to

run multiple virtual operating systems. Frank H. McMahon, of the Lawrence Livermore National

Laboratory, invented the terms FLOPS and MFLOPS so that he could compare the so-called supercomputers

of the day by the number of floating-point calculations they performed per second. 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.

Fixed-point These designations refer to the format used

to store and manipulate numeric representations of data without using a decimal point. Fixed-point

are designed to represent and manipulate integers – positive and negative whole numbers; for

example, 16 bits, yielding up to 65,536 possible bit patterns that typically represent the

whole numbers from −32768 to +32767. Floating-point

This is needed for very large or very small real numbers, or numbers requiring the use

of a decimal point. The encoding scheme used by the processor for floating-point numbers

is more complicated than for fixed-point. 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 and the mantissa. 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. 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.

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 which

are extremely large or where the range may be unpredictable. As such, floating-point

processors are ideally suited for computationally intensive applications.

See also

Gordon Bell Prize Orders of magnitude

References

External links Current Einstein@Home benchmark

BOINC projects global benchmark Current GIMPS throughput

Top500.org LinuxHPC.org Linux High Performance Computing

and Clustering Portal WinHPC.org Windows High Performance Computing

and Clustering Portal Oscar Linux-cluster ranking list by CPUs/types

and respective FLOPS Information on how to calculate "Composite

Theoretical Performance" Information on the Oak Ridge National Laboratory

Cray XT system. Infiscale Cluster Portal – Free GPL HPC

Source code, pre-compiled versions and results for PCs – Linpack, Livermore Loops, Whetstone

MFLOPS PC CPU Performance Comparisons %MFLOPS/MHz

– CPU, Caches and RAM Xeon export compliance metrics, including

GFLOPS IBM Brings NVIDIA Tesla GPUs Onboard