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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]
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 ...
The final result comes from dividing the number of instructions by the number of CPU clock cycles. The number of instructions per second and floating point operations per second for a processor can be derived by multiplying the number of instructions per cycle with the clock rate (cycles per second given in Hertz) of the processor in question ...
In computer architecture, cycles per instruction (aka clock cycles per instruction, clocks per instruction, or CPI) is one aspect of a processor's performance: the average number of clock cycles per instruction for a program or program fragment. [1] It is the multiplicative inverse of instructions per cycle.
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).
Based on a 25 MHz clock, with all 256-PEs running on a single program, the machine was designed to deliver 1 billion floating point operations per second, or in today's terminology, 1 GFLOPS. [20] This made it much faster than any machine in the world; the contemporary CDC 7600 had a clock cycle of 27.5 nanoseconds, or 36 MIPS, [ 21 ] although ...
The Dhrystone benchmark contains no floating point operations, thus the name is a pun on the then-popular Whetstone benchmark for floating point operations. The output from the benchmark is the number of Dhrystones per second (the number of iterations of the main code loop per second).
1.88×10 18: U.S. Summit achieves a peak throughput of this many operations per second, whilst analysing genomic data using a mixture of numerical precisions. [ 16 ] 2.43×10 18 : Folding@home distributed computing system during COVID-19 pandemic response [ 17 ]