Efficient and Portable Winograd Convolutions for Multi-core Processors

[EN] We take a step forward towards developing high-performance codes for the convolution operator, based on the Winograd algorithm, that are easy to customise for general-purpose processor architectures. In our approach, augmenting the portability of the solution is achieved via the introduction of vector instructions from Intel SSE/AVX2/AVX512 and ARM NEON/SVE to exploit the single-instruction multiple-data capabilities of current processors as well as OpenMP pragmas to exploit multi-threaded parallelism. While this comes at the cost of sacrificing a fraction of the computational performance, our experimental results on three distinct processors, with Intel Xeon Skylake, ARM Cortex A57 and Fujitsu A64FX processors, show that the impact is affordable and still renders a Winograd-based solution that is competitive when compared with the lowering gemm-based convolution. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was funded by Project PID2020-113656RB-C21/C22 supported by MCIN/AEI/10.13039/501100011033. Manuel F. Dolz was also supported by the Plan Gen T grant CDEIGENT/2018/014 of the Generalitat Valenciana. Héctor Martínez is a POSTDOC_21_00025 fellow supported by Junta de Andalucía. Adrián Castelló is a FJC2019-039222-I fellow supported by MCIN/AEI/10.13039/501100011033. Dolz Zaragozá, MF.; Martínez, H.; Castelló, A.; Alonso-Jordá, P.; Quintana-Ortí, ES. (2023). Efficient and Portable Winograd Convolutions for Multi-core Processors. The Journal of Supercomputing. 79. https://doi.org/10.1007/s11227-023-05088-4