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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. [citation needed]
Petascale computing refers to computing systems capable of performing at least 1 quadrillion (10^15) floating-point operations per second (FLOPS).These systems are often called petaflops systems and represent a significant leap from traditional supercomputers in terms of raw performance, enabling them to handle vast datasets and complex computations.
Floating point operations per second (FLOPS) are one measure of computer performance.FLOPS can be recorded in different measures of precision, however the standard measure (used by the TOP500 supercomputer list) uses 64 bit (double-precision floating-point format) operations per second using the High Performance LINPACK (HPLinpack) benchmark.
The performance measured by the LINPACK benchmark consists of the number of 64-bit floating-point operations, generally additions and multiplications, a computer can perform per second, also known as FLOPS. However, a computer's performance when running actual applications is likely to be far behind the maximal performance it achieves running ...
The fact that floating-point numbers cannot accurately represent all real numbers, and that floating-point operations cannot accurately represent true arithmetic operations, leads to many surprising situations. This is related to the finite precision with which computers generally represent numbers.
A zettascale computer system could generate more single floating point data in one second than was stored by any digital means on Earth in the first quarter of 2011. [ citation needed ] Beyond zettascale computing (>10 21 )
Determine how many 64 bit (or better) floating point operations every processor in the system can perform per clock cycle (best case). This is FPO(i). Determine the clock frequency of every processor. This is F(i). Choose the weighting factor for each processor: 0.9 for vector processors and 0.3 for non-vector processors. This is W(i).
In 1978, the program was updated to log running time of each of the tests, allowing MFLOPS (Millions of Floating Point Operations Per Second) to be included in reports, along with an estimation of Integer MIPS (Millions of Instructions Per Second). In 1987, MFLOPS calculations were included in the log for the three appropriate tests and MOPS ...