SpiceC: scalable parallelism via implicit copying and explicit commit

In this paper we present an approach to parallel programming called SpiceC. SpiceC simplifies the task of parallel programming through a combination of an intuitive computation model and SpiceC directives. The SpiceC parallel computation model consists of multiple threads where every thread has a private space for data and all threads share data via a shared space. Each thread performs computations using its private space thus offering isolation which allows for speculative computations. SpiceC provides easy to use SpiceC compiler directives using which the programmers can express different forms of parallelism. It allows developers to express high level constraints on data transfers between spaces while the tedious task of generating the code for the data transfers is performed by the compiler. SpiceC also supports data transfers involving dynamic data structures without help from developers. SpiceC allows developers to create clusters of data to enable parallel data transfers. SpiceC programs are portable across modern chip multiprocessor based machines that may or may not support cache coherence. We have developed implementations of SpiceC for shared memory systems with and without cache coherence. We evaluate our implementation using seven benchmarks of which four are parallelized speculatively. Our compiler generated implementations achieve speedups ranging from 2x to 18x on a 24 core system.