Automatic Parallelization in a Binary Rewriter

Today, nearly all general-purpose computers are parallel, but nearly all software running on them is serial. However bridging this disconnect by manually rewriting source code in parallel is prohibitively expensive. Automatic parallelization technology is therefore an attractive alternative. We present a method to perform automatic parallelization in a binary rewriter. The input to the binary rewriter is the serial binary executable program and the output is a parallel binary executable. The advantages of parallelization in a binary rewriter versus a compiler include (i) compatibility with all compilers and languages, (ii) high economic feasibility from avoiding repeated compiler implementation, (iii) applicability to legacy binaries, and (iv) applicability to assembly-language programs. Adapting existing parallelizing compiler methods that work on source code to work on binary programs instead is a significant challenge. This is primarily because symbolic and array index information used in existing compiler parallelizers is not available in a binary. We show how to adapt existing parallelization methods to achieve equivalent parallelization from a binary without such information. Preliminary results using our x86 binary rewriter called Second Write on a suite of dense-matrix regular programs including the externally developed Polybench suite of benchmarks shows an average speedup of 5.1 from binary and 5.7 from source with 8 threads compared to the input serial binary on an x86 Xeon E5530 machine, and 14.7 from binary and 15.4 from source with 32 threads compared to the input serial binary on a SPARC T2. Such regular loops are an important component of scientific and multi-media workloads, and are even present to a limited extent in otherwise non-regular programs.