A Unified Approach to Speculative Parallelization of Loops in DSM Multiprocessors

Speculative parallel execution of statically non-analyzable codes on Distributed Shared-Memory (DSM) multiprocessors is challenging because of the long latency and memory distribution present. However, such an approach may well be the best way of speeding up codes whose dependences can not be compiler analyzed. In this paper, we have extended past work by proposing a hardware scheme for the speculative parallel execution of loops that have a modest number of cross-iteration dependences. In this case, when a dependence violation is detected, we locally repair the state. Then, depending on the situation, we either re-execute one out-of-order iteration or, restart parallel execution from that point on. The general algorithm, called the Unified Privatization and Reduction algorithm (UPAR), privatizes, on demand, at cache-line level, executes reductions in parallel, merges the last values and partial results of reductions on-the-fly with minimum residual work at loop end. UPAR allows for completely dynamic scheduling and does not get slowed down if the working set of an iteration is larger than the cache size. Simulations indicate good speedups relative to sequential execution. The hardware support for reduction optimizations brings, on average, 50 % performance improvement and can be used both in speculative and normal execution