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About SciMark 2.0

SciMark is a composite Java benchmark measuring the performance of numerical codes occurring in scientific and engineering applications. It consists of five computational kernels: FFT, Gauss-Seidel relaxation, Sparse matrix-multiply, Monte Carlo integration, and dense LU factorization.

These kernels are chosen to provide an indication of how well the underlying JVM/JITs perform on applications utilizing these types of algorithms. The problems sizes are purposely chosen to be small in order to isolate the effects of memory hierarchy and focus on internal JVM/JIT and CPU issues. A larger version of the benchmark (SciMark 2.0 LARGE) addresses performance of the memory subsystem with out-of-cache problem sizes.

Computational Kernels

SciMark executes the following kernels:

SciMark 2.0 benchmark developed by
Roldan Pozo and Bruce R Miller.

Commercial equipment and software is referred to on these pages in order to precisely document experimental measurements. Mention of these products does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the products so identified are necessarily the best available for that purpose.