In computing, FLOPS is a measure of computer performance, useful in fields of scientific
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