Support Vector Machine (SVM) algorithm requires a high computational cost
(both in memory and time) to solve a complex quadratic programming (QP)
optimization problem during the training process. Consequently, SVM
necessitates high computing hardware capabilities. The central processing unit
(CPU) clock frequency cannot be increased due to physical limitations in the
miniaturization process. However, the potential of parallel multi-architecture,
available in both multi-core CPUs and highly scalable GPUs, emerges as a
promising solution to enhance algorithm performance. Therefore, there is an
opportunity to reduce the high computational time required by SVM for solving
the QP optimization problem. This paper presents a comparative study that
implements the SVM algorithm on different parallel architecture frameworks. The
experimental results show that SVM MPI-CUDA implementation achieves a speedup
over SVM TensorFlow implementation on different datasets. Moreover, SVM
TensorFlow implementation provides a cross-platform solution that can be
migrated to alternative hardware components, which will reduces the development
time.

支持向量机算法在训练过程中需要解决复杂的二次规划优化问题，这导致计算成本高。本研究通过在不同的并行架构框架上实现支持向量机算法的比较研究，发现 SVM MPI-CUDA 实现在不同数据集上能够优于 SVM TensorFlow 实现，而且 SVM TensorFlow 实现提供了跨平台解决方案，能够迁移到替代硬件组件，从而减少开发时间。