A compiler-automated array compression scheme for optimizing memory intensive programs

This paper proposes a compiler-automated array compression scheme to reduce the memory bandwidth consumption of programs and thereby to improve their execution speed. Three encoding methods are developed for such compression. Formulas are derived to analyze the cost and benefit of such methods. To ease the programmer's effort for writing and maintaining complex source code that utilizes compression, we implement our technique in a compiler which automatically transforms the program into different versions corresponding to different encoding methods. The compiler also inserts operations to adaptively invoke the preferred version at run time, including the original version which performs no compression. Results show that our compiler-automated adaptive scheme improves the execution speed over the original version by an average of 9% for a set of benchmark programs which perform memory-intensive sparse matrix-vector multiplications (SpMV). These results take into account of overhead to make the adaptive decision. When tested separately, the individual encoding methods speed up program execution by as high as 41%, which compares favorably against previous compression methods manually applied to SpMV.