ImageCL: An image processing language for performance portability on heterogeneous systems
Modern computer systems typically conbine multicore CPUs with accelerators like GPUs for inproved performance and energy efficiency. However, these systems suffer from poor performance portability code tuned for one device must be retuned to achieve high performance on another. Image processing is increasing in importance, with applications ranging from seismology and medicine to Photoshop. Based on our experience with medical image processing, we propose ImageCL, a high level domain specific language and source to source compiler, targeting heterogeneous hardware. ImageCL resembles OpenCL, but abstracts away performance optimization details, allowing the programmer to focus on algorithm development, rather than performance tuning. The latter is left to our source to source compiler and auto tuner. From high level ImageCL kernels, our source to source compiler can generate multiple OpenCL implementations with different optimizations applied. We rely on auto tuning rather than machine models or expert programmer knowledge to determine which optimizations to apply, making our tuning procedure highly robust. Furthermore, we can generate high performing implementations for different devices from a single source code, thereby improving performance portability. We evaluate our approach on three image processing benchmarks, on different GPU and CPU devices, and are able to outperform other state of the art solutions in several cases, achieving speedups of up to 4.57x.
2016 International Conference on High Performance Computing Simulation (HPCS)