Original author(s) | Jack Dongarra, Jim Bunch, Cleve Moler, and Gilbert Stewart |
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Initial release | 1979 |
Website | www |
The LINPACK Benchmarks are a measure of a system's floating point computing power. Introduced by Jack Dongarra, they measure how fast a computer solves a dense n by n system of linear equations Ax = b, which is a common task in engineering.
The latest version of these benchmarks is used to build the TOP500 list, ranking the world's most powerful supercomputers.
The aim is to approximate how fast a computer will perform when solving real problems. It is a simplification, since no single computational task can reflect the overall performance of a computer system. Nevertheless, the LINPACK benchmark performance can provide a good correction over the peak performance provided by the manufacturer. The peak performance is the maximal theoretical performance a computer can achieve, calculated as the machine's frequency, in cycles per second, times the number of operations per cycle it can perform. The actual performance will always be lower than the peak performance. The performance of a computer is a complex issue that depends on many interconnected variables. 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 appropriate LINPACK benchmark.
The name of these benchmarks comes from the LINPACK package, a collection of algebra Fortran subroutines widely used in the 1980s, and initially tightly linked to the LINPACK benchmark. The LINPACK package has been since then replaced by other libraries.
The LINPACK benchmark report appeared first in 1979 as an appendix to the LINPACK user's manual.
LINPACK was designed to help users estimate the time required by their systems to solve a problem using the LINPACK package, by extrapolating the performance results obtained by 23 different computers solving a matrix problem of size 100.