An All-Software Thread-Level Data Dependence Speculation System for Multiprocessors

We present a software approach to design a thread-level data dependence speculation system targeting multiprocessors. Highly-tuned checking codes are associated with loads and stores whose addresses cannot be disambiguated by parallel compilers and that can potentially cause dependence violations at run-time. Besides resolving many name and true data dependencies through dynamic renaming and forwarding, respectively, our method supports parallel commit operations. Performance results collected on an architectural simulator and validated on a commercial multi-processor show that the overhead can be reduced to less than ten instructions per speculative memory operation. Moreover, we demonstrate that a ten-fold speedup is possible on some of the difficult-to-parallelize loops in the Perfect Club benchmark suite on a 16-way multiprocessor