Predicated switching - optimizing speculation on EPIC machines

Explicitly parallel instruction computing (EPIC) processors are a very attractive platform for many of today\´s multimedia and communications applications. In particular, clustered EPIC machines can take aggressive advantage of the available instruction-level parallelism, while maintaining high energy-delay efficiency. However, multicluster machines are more challenging to compile to than centralized machines. In this paper, we propose a novel compiler-directed speculation technique called predicated switching (PS) that can be applied to both centralized and multicluster EPIC machines. The two novel contributions in PS are: 1) a compiler transformation, denoted static single assignment-predicated switching, that leverages required data transfers between clusters for performance gains and 2) a static speculation algorithm to decide which specific kernel operations should actually be speculated in the final code, so as to maximize execution performance on the target processor. Experimental results performed on a representative set of time critical kernels compiled for a number of target machines show that, when compared to "resource-unaware" speculation techniques, PS improves performance with respect to at least one of the baselines in 80% of the cases by up to 38%. Moreover, we show that code size and register pressure are not adversely affected by our technique.