Programming accelerators such as GPUs with
low-level APIs and languages such as OpenCL and CUDA
is difficult, error-prone, and not performance-portable. Automatic
parallelization and domain specific languages (DSLs)
have been proposed to hide complexity and regain performance
portability. We present PENCIL, a rigorously-defined subset of
GNU C99—enriched with additional language constructs—that
enables compilers to exploit parallelism and produce highly
optimized code when targeting accelerators. PENCIL aims to
serve both as a portable implementation language for libraries,
and as a target language for DSL compilers.
We implemented a PENCIL-to-OpenCL backend using a
state-of-the-art polyhedral compiler. The polyhedral compiler,
extended to handle data-dependent control flow and non-affine
array accesses, generates optimized OpenCL code. To demonstrate
the potential and performance portability of PENCIL
and the PENCIL-to-OpenCL compiler, we consider a number
of image processing kernels, a set of benchmarks from the
Rodinia and SHOC suites, and DSL embedding scenarios for
linear algebra (BLAS) and signal processing radar applications
(SpearDE), and present experimental results for four GPU
platforms: AMD Radeon HD 5670 and R9 285, NVIDIA
GTX 470, and ARM Mali-T604.