Abstract
Numerical linear algebra is one of the most important forms of scientific computation. The basic computations in numerical linear algebra are matrix computations and linear systems solution. These computations are used as kernels in many computational problems. This study demonstrates the parallelisation of these scientific computations using multi-core programming frameworks. Specifically, the frameworks examined here are Pthreads, OpenMP, Intel Cilk Plus, Intel TBB, SWARM, and FastFlow. A unified and exploratory performance evaluation and a qualitative study of these frameworks are also presented for parallel scientific computations with several parameters. The OpenMP and SWARM models produce good results running in parallel with compiler optimisation when implementing matrix operations at large and medium scales, whereas the remaining models do not perform as well for some matrix operations. The qualitative results show that the OpenMP, Cilk Plus, TBB, and SWARM frameworks require minimal programming effort, whereas the other models require advanced programming skills and experience. Finally, based on an extended study, general conclusions regarding the programming models and matrix operations for some parameters were obtained.
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