Hardware Pipelining of Repetitive Patterns in Processor Instruction Traces

Abstract

Dynamic partitioning is a promising technique where computations are transparently moved from a Gene-
 ral Purpose Processor (GPP) to a coprocessor during application execution. To be effective, the mapping
 of computations to the coprocessor needs to consider aggressive optimizations. One of the mapping opti-
 mizations is loop pipelining, a technique extensively studied and known to allow substantial performance
 improvements. This paper describes a technique for pipelining Megablocks, a type of runtime loop deve-
 loped for dynamic partitioning. The technique transforms the body of Mega-blocks into an acyclic dataflow
 graph which can be fully pipe-lined and is based on the atomic execution of loop iterations. For a set of 9 ben-
 chmarks without memory operations, we generated pipelined hardware versions of the loops and esti-mate
 that the presented loop pipelining technique increases the average speedup of non-pipelined coprocessor
 accelerated designs from 1.6× to 2.2×. For a larger set of 61 benchmarks which include memory operations,
 we estimate through simulation a speedup increase from 2.5× to 5.6× with this technique.