High-Performance Matrix Multiplication: Hierarchical Data Structures, Optimized Kernel Routines, and Qualitative Performance Modeling
CommitteeLuke, Edward A.
Reese, Donna S.
The optimal implementation of matrix multiplication on modern computer architectures is of great importance for scientific and engineering applications. However, achieving the optimal performance for matrix multiplication has been continuously challenged both by the ever-widening performance gap between the processor and memory hierarchy and the introduction of new architectural features in modern architectures. The conventional way of dealing with these challenges benefits significantly from the blocking algorithm, which improves the data locality in the cache memory, and from the highly tuned inner kernel routines, which in turn exploit the architectural aspects on the specific processor to deliver near peak performance. A state-of-art improvement of the blocking algorithm is the self-tuning approach that utilizes "heroic" combinatorial optimization of parameters spaces. Other recent research approaches include the approach that explicitly blocks for the TLB (Translation Lookaside Buffer) and the hierarchical formulation that employs memory-friendly Morton Ordering (a space-filling curve methodology). This thesis compares and contrasts the TLB-blocking-based and Morton-Order-based methods for dense matrix multiplication, and offers a qualitative model to explain the performance behavior. Comparisons to the performance of self-tuning library and the "vendor" library are also offered for the Alpha architecture. The practical benchmark experiments demonstrate that neither conventional blocking-based implementations nor the self-tuning libraries are optimal to achieve consistent high performance in dense matrix multiplication of relatively large square matrix size. Instead, architectural constraints and issues evidently restrict the critical path and options available for optimal performance, so that the relatively simple strategy and framework presented in this study offers higher and flatter overall performance. Interestingly, maximal inner kernel efficiency is not a guarantee of global minimal multiplication time. Also, efficient and flat performance is possible at all problem sizes that fit in main memory, rather than "jagged" performance curves often observed in blocking and self-tuned blocking libraries.