Information about Flops

In computing, FLOPS (or flops or flop/s) is an acronym meaning FLoating point Operations Per Second. The FLOPS is a measure of a computer's performance, especially in fields of scientific calculations that make heavy use of floating point calculations; similar to 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 (or flop) is used as an abbreviation for "FLoating-point OPeration", and a flop count is a count of these operations (e.g., required by a given algorithm or computer program). In this context, "flops" is simply the plural rather than a rate.

Computing devices exhibit an enormous range of performance levels in floating-point applications, so it makes sense to introduce larger units than FLOPS.
The standard SI prefixes can be used for this purpose, resulting in such units as teraFLOPS (1×10¹² FLOPS)

According to Top500.org, the fastest computer in the world as of June 2007 was the IBM Blue Gene/L supercomputer, measuring a peak of 280.6 TFLOPS. But Cray Inc. recently released their Cray XT4 which can peak at 318 TFLOPS.

A basic calculator performs relatively few FLOPS. Each calculation request to a typical calculator requires only a single operation, so there is rarely any need for its response time to exceed that needed by the operator. Any response time below 0.1 second is perceived as instantaneous by a human operator, so a simple calculator could be said to operate at about 10 FLOPS.

FLOPS as a measure of performance

In order for FLOPS to be useful as a measure of floating-point performance, a standard benchmark must be available on all computers of interest. One example is the LINPACK benchmark.

FLOPS in isolation are arguably not very useful as a benchmark for modern computers. There are many factors in computer performance other than raw floating-point computation speed, such as I/O performance, interprocessor communication, cache coherence, and the memory hierarchy. This means that supercomputers are in general only capable of a small fraction of their "theoretical peak" FLOPS throughput (obtained by adding together the theoretical peak FLOPS performance of every element of the system). Even when operating on large highly parallel problems, their performance will be bursty, mostly due to the residual effects of Amdahl's law. Real benchmarks therefore measure both peak actual FLOPS performance as well as sustained FLOPS performance.

For ordinary (non-scientific) applications, integer operations (measured in MIPS) are far more common. Measuring floating point operation speed, therefore, does not predict accurately how the processor will perform on just any problem. However, for many scientific jobs such as analysis of data, a FLOPS rating is effective.

Historically, the earliest reliably documented serious use of the Floating Point Operation as metric appears to be AEC justification to Congress for purchasing a Control Data CDC 6600 in the mid-1960s.

The terminology is currently so confusing that until April 24, 2006 U.S. export control was based upon measurement of "Composite Theoretical Performance" (CTP) in millions of "Theoretical Operations Per Second" or MTOPS. On that date, however, the U.S. Department of Commerce's Bureau of Industry and Security amended the Export Administration Regulations to base controls on Adjusted Peak Performance (APP) in Weighted TeraFLOPS (WT).

Records

Today Blue Gene is the world's fastest computer, at 360 TFLOPS. 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 petaflop. When configured to do so, it can reach speeds in excess of three petaflops. In June 2006, a new computer was announced by Japanese research institute RIKEN, the MDGRAPE-3. The computer's performance tops out at one petaflop, over three times faster than the Blue Gene/L. MDGRAPE-3 is not a general purpose computer, which is why it does not appear in the TOP500 list. It has special-purpose pipelines for simulating molecular dynamics. MDGRAPE-3 houses 4,808 custom processors, 64 servers each with 256 dual-core processors, and 37 servers each containing 74 processors, for a total of 40,314 processor cores, compared to the 131,072 needed for the Blue Gene/L. MDGRAPE-3 is able to do many more computations with few chips because of its specialized architecture. The computer is a joint project between Riken, Hitachi, Intel, and NEC subsidiary SGI Japan.

Distributed computing uses the Internet to link personal computers to achieve a similar effect:

• The entire BOINC averages 663 TFLOPS as of September 8, 2007.^[1]
• SETI@home computes data averages more than 265 TFLOPS.^[2]
• Folding@home has reached over 1 PFLOPS.^[3] Note, as of March 22nd, 2007, PlayStation 3 owners may now participate in the FAH project. Because of this, FAH is now sustaining considerably higher than 210 TFLOPS (1267 as of 9/23/07). See current stats^[4] for details.
• Einstein@home is crunching more than 70 TFLOPS.^[5]
• As of June 2007, GIMPS is sustaining 23 TFLOPS.^[6]
• Intel Corporation has recently unveiled the experimental multi-core POLARIS chip, which achieves 1 TFLOPS at 3.2GHz. The 80-core chip can increase this to 1.8 TFLOPS at 5.6GHz, although the thermal dissipation at this frequency exceeds 260 watts.

As of 2007, the fastest PC processors perform over 30 GFLOPS.^[7] GPUs in PCs are considerably more powerfull in terms of pure FLOPS. For example, in the GeForce 8 Series the nVidia 8800 Ultra performs around 576 GFLOPS on 128 Processing elements. This equates to around 4.5 GFLOPS per element, compared with 2.75 per core for the Blue Gene/L. It should be noted that the 8800 series performs only single precision calculations, and that while GPUs are highly efficient at calculations they are not as flexible as a general purpose CPU. Current top end ATI GPU cards do perform double precision operations.

Cost of computing

• 1997: about US$30,000 per GFLOPS; with two 16-Pentium-Pro–processor Beowulf cluster computers, ^[8]
• 2000, April: $1,000 per GFLOPS, Bunyip, Australian National University. First sub-US$1/MFlop. Gordon Bell Prize 2000.
• 2000, May: $640 per GFLOPS, KLAT2, University of Kentucky
• 2003, August: $82 per GFLOPS, KASY0, University of Kentucky
• 2006, February: about $1 per GFLOPS in ATI PC add-in graphics card (X1900 architecture) - these figures are disputed as they refer to highly parallelized GPU power''
• 2007, March: about $0.42 per GFLOPS in Ambric AM2045 ^[9]''

This trend toward low cost follows Moore's law.

Pop culture references

• In the Star Trek fictional universe, circa 2364, the android Data was constructed with an initial linear computational speed rated at 60 trillion operations per second, or 60 TOPS (and thereby, potentially 'dating' the series in which he appears).
• In the series, the main computer reported it processed 575 Trillion operations per nanosecond. This would be 575 ZettaOPS.
• In the movie Terminator III, Skynet is said to be operating at 60 teraFLOPS.
• In the mass multiplayer online game EVE-Online, the computer systems on starships is rated in teraFLOPS, ranging from 100 teraFLOPS on the smallest sized vessels, to 1.25 petaFLOPS on the largest vessel.

See also

• Gordon Bell Prize

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
• Petaflop developments and information
• Information on how to calculate “Composite Theoretical Performance" (CTP)
• Information on the Oak Ridge National Laboratory Cray XT system.
• Infiscale Cluster Portal - Free GPL HPC