Deep Jam: Conversion of Coarse-Grain Parallelism to Fine-Grain and Vector Parallelism

A number of computational applications lack instruction-level parallelism. This loss is particularly acute on sequences of dependent instructions on wide-issue or deeply pipelined architectures. We consider four real applications from computational biology, cryptanalysis, and data compression. These applications are characterized by long sequences of dependent instructions, irregular control-flow and intricate scalar and memory dependence patterns. While these benchmarks exhibit good memory locality and branch-predictability, state-of-the-art compiler optimizations fail to exploit much instruction-level parallelism. This paper shows that major performance gains are possible on such applications, through a loop transformation called deep jam. This transformation reshapes the control-flow of a program to facilitate the extraction of independent computations through classical back-end techniques. Deep jam combines accurate dependence analysis and control speculation, with a generalized form of recursive, multi-variant unroll-and-jam; it brings together independent instructions across irregular control structures, removing memory-based dependences through scalar and array renaming. This optimization contributes to the extraction of fine-grain parallelism in irregular applications. We propose a feedback-directed deep jam algorithm, selecting a jamming strategy, function of the architecture and application characteristics.