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.

翻译：支持向量机算法在训练过程中需要高计算成本（包括内存和时间）来解决复杂的二次规划优化问题。因此，支持向量机对计算硬件性能提出了较高要求。受微型化工艺的物理限制，中央处理器时钟频率无法进一步提升。然而，多核CPU与高扩展性GPU中存在的并行多架构潜力，为提升算法性能提供了新的解决方案。这为降低支持向量机求解二次规划优化问题所需的高计算时间创造了机遇。本文开展了一项比较研究，在不同并行架构框架上实现了支持向量机算法。实验结果表明，支持向量机的MPI-CUDA实现在不同数据集上相比TensorFlow实现实现了加速比提升。此外，支持向量机的TensorFlow实现提供了可移植至替代硬件组件的跨平台解决方案，这将有效缩短开发时间。