Association for Computing Machinery
Recent advances in multi-core and many-core processors requires programmers to exploit an increasing amount of parallelism from their applications. Data parallel languages such as CUDA and OpenCL make it possible to take advantage of such processors, but still require a large amount of effort from programmers. A number of parallelizing source-to-source compilers have recently been developed to ease programming of multi-core and many-core processors. This paper presents and evaluates a number of such tools, focused in particular on C-to-CUDA transformations targeting GPUs.