1 research outputs found
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 sys-
tems suffer from poor performance portability, code tuned for one device must
be retuned to achieve high performance on another. Image processing is increas-
ing 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
per- formance 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 ex- pert programmer knowledge to determine which
optimizations to apply, making our tuning procedure highly robust. Furthermore,
we can generate high perform- ing implementations for different devices from a
single source code, thereby im- proving 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