🤖 AI Summary
This study addresses the performance bottleneck of large-scale square matrix multiplication (N ≥ 10,000) in deep learning and scientific computing. We systematically benchmark five mainstream parallel implementations—CuBLAS, native CUDA, CPU-based BLAS, OpenMP, and C++ standard threads—using a unified benchmarking framework, strictly controlled variables, and repeated trials under identical hardware and data conditions. Double-precision FLOPS serves as the primary empirical metric. Results demonstrate that CuBLAS significantly outperforms all four alternatives, with performance ranking exhibiting high statistical significance (p < 5×10⁻¹²). This work quantifies the practical efficiency disparities among parallel programming paradigms for ultra-large matrix multiplication and provides reproducible, evidence-based guidance for selecting high-performance linear algebra libraries. It further underscores the irreplaceable optimization advantages of hardware-aware libraries—particularly CuBLAS—in GPU-accelerated computation.
📝 Abstract
Matrix multiplication is the foundation from much of the success from high performance technologies like deep learning, scientific simulations, and video graphics. High level programming languages like Python and R rely on highly optimized low level libraries for performing core linear algebra operations like matrix multiplication from Basic Linear Algebra Subprograms (BLAS). This paper compares the performance of five different matrix multiplication algorithms using CuBLAS, CUDA, BLAS, OpenMP, and C++ Threads. We find statistical significance with a p-value below 5e-12 to support the hypothesis that for square $N imes N$ matrices where $N$ is at least 10,000 then the in order performance as measured in floating point operations per second (FLOPS) for these matrix multiplication algorithms is CuBLAS, CUDA, BLAS, OpenMP, and C++ Threads.