Uncut-GEMMs: Communication-Aware Matrix Multiplication on Multi-GPU Nodes

General Matrix Multiplication (GEMM) is one of the most common kernels in high-performance computing (HPC) and machine-learning (ML) applications, frequently dominating their execution time, rendering its performance vital. As multi-GPU nodes have become common in modern HPC systems, GEMM is usually offloaded on GPUs as its compute-intensive nature is a good match for their architecture. On the other hand, despite the GEMM kernel itself being usually compute-bound, execution on multi-GPU systems also requires fine-grained communication and task scheduling to achieve optimal performance. While numerous multi-GPU level-3 BLAS libraries have faced these issues in the past, they are bound by older design concepts that are not necessarily applicable to modern multi-GPU clusters, resulting in considerable deviation from peak performance. In this work, we thoroughly analyze the current challenges regarding data movement, caching, and overlap of multi-GPU GEMM, and the shortcomings of previous solutions, and provide a fresh approach to multi-GPU GEMM optimization. We devise a static scheduler for GEMM, enabling a variety of algorithmic, communication, and auto-tuning optimizations, and integrate those in an end-to-end open-source multi-GPU GEMM library. Our library is evaluated on a multi-GPU NVIDIA HGX system with 8 NVIDIA A100 GPUs, achieving on average a 1.37x and 1.29x performance improvement over the state-of-the-art multi-GPU GEMM libraries, for double and single precision, respectively.