Efficient Implementation of Strassen's Algorithm for Memory Allocation using AVX Intrinsic on Multi-core Architecture

Oo, Nwe Zin; Chaikan, Panyayot

doi:10.1109/itc-cscc.2019.8793377

Cited by 1 publication

(1 citation statement)

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“…In Strassen's algorithm, there are seven submatrices-calculations (M 1 to M 7) that are independent works that can be executed by parallel section constructs. The implementation in [31] only presented the performance of Strassen's algorithm in terms of GF LOP S and speed-up computation excluding the measurement of energy/power consumption. The most suitable recursive stop-point was proposed in [31], and its tiling pattern was different from our proposed 2D blocking method, as shown in Fig.…”

Section: Avx Vectorization and Openmpmentioning

confidence: 99%

Power Efficient Strassen’s Algorithm using AVX512 and OpenMP in a Multi-core Architecture

Oo¹,

Chaikan²

2023

ECTI-CIT Transactions

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This paper presents an effective implementation of Strassen's algorithm for matrix-matrix multiplication on shared memory multi-core architecture. The proposed algorithm aims to augment the computation speed in terms of GFLOPS performance on average 4.5 and 4.1 times faster than Eigen and OpenBLAS, respectively while reducing the power consumption to as low as possible. Our algorithm relies on using AVX512 intrinsics, loop unrolling factor, and OpenMP directives. A new 2D blocking data allocation pattern is proposed for Strassen's algorithm to provide optimized cache temporal and spatial locality. The proposed implementation reduced not only the amount of main memory but also the burden of unnecessary memory allocation/deallocation and data transferring for each level of recursion in Strassen's algorithm. Moreover, the proposed algorithm consumed, on average, 4.25 and 3.67 times lower energy than the multiplication functions of the Eigen and OpenBLAS libraries, respectively. To measure the computational performance with the awareness of power consumption, GFLOPS per Watt (GFPW) is calculated, which out- performed on average 3.78 and 3.47 times higher than those of Eigen and OpenBLAS libraries, respectively.

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Section: Avx Vectorization and Openmpmentioning

confidence: 99%