Analyzing an In-line Compression on the Matrix Matrix Multiplication Kernel

High-performance computing (HPC) has enabled advancements in computation speed and resource cost by utilizing all available server resources and using parallelization for speedup. This computation scheme encourages simulation model development, massive data collection, and AI computation models, all which store and compute on massive amounts of data. Data compression has enhanced the performance of storing and transferring this HPC application data to enable acceleration, but the benefits of data compression can also be transferred to the active allocated memory used by the application. In-line compression is a compression method that keeps the application memory compressed in allocated memory, decompressing memory as its data is needed by the algorithm. The actively decompressed data size in allocated memory can be limited by grouping and compressing the data into blocks informed by the application's computational kernel's data access patterns and a selected compressor. This research explores factors such as block size and compressor choice on the runtime and memory usage of the Matrix Multiplication kernel. Matrix multiplication (MM) is a fundamental HPC algorithm that exemplifies the memory access patterns and use cases present in other HPC kernels. MM kernels provide a baseline for evaluating in-line compression due to MM's row-based data access patterns and the usage of several matrices to compute the resulting matrix. Trade-offs between memory size and the compression runtime necessitate tuned parameters for each in-line compression-enabled kernel. The results of this research demonstrate essential parameter trends, trade-offs, and the importance of locality in the kernel's data access patterns.