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Studying OpenMP thread mapping for parallel linear algebra kernels on multicore system

Bylina B., Bylina J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2018

Vol. 66, nr 6

981--990

Thread mapping is one of the techniques which allow for efficient exploiting of the potential of modern multicore architectures. The aim of this paper is to study the impact of thread mapping on the computing performance, the scalability, and the energy consumption for parallel dense linear algebra kernels on hierarchical shared memory multicore systems. We consider the basic application, namely a matrix-matrix product (GEMM), and two parallel matrix decompositions (LU and WZ). Both factorizations exploit parallel BLAS (basic linear algebra subprograms) operations, among others GEMM. We compare differences between various thread mapping strategies for these applications. Our results show that the choice of thread mapping has the measurable impact on the performance, the scalability, and energy consumption of the GEMM and two matrix factorizations.

Parallelization of symmetric block ciphers

Bielecki W., Burak D.

Computing, Multimedia and Intelligent Techniques

2005

Vol. 1 nr 1

133-152

In this paper, we present the parallelization process of well-known symmetric block ciphers, such as DBS, Triple DES, LOKI91, IDEA and GOST along with a detailed description of their parallelization methods. Algorithms of parallel symmetric block ciphers are attached. The data dependences analysis of loops was applied in order to parallelize sequential algorithms. The OpenMP standard was chosen for representing parallelism of symmetric block ciphers. The efficiency measurements for parallel programs are presented.