OpenCL for programming shared memory multicore CPUs

Shared memory multicore processor technology is pervasive in mainstream computing. This new architecture challenges programmers to write code that scales over these many cores to exploit the full computational power of these machines. OpenMP and Intel Threading Building Blocks (TBB) are two of the popular frameworks used to program these architectures. Recently, OpenCL has been defined as a standard by Khronos group which focuses on programming a possibly heterogeneous set of processors with many cores such as CPU cores, GPUs, DSP processors. In this work, we evaluate the effectiveness of OpenCL for programming multicore CPUs in a comparative case study with OpenMP and Intel TBB for five benchmark applications: matrix multiply, LU decomposition, 2D image convolution, Pi value approximation and image histogram generation. The evaluation includes the effect of compiler optimizations for different frameworks, OpenCL performance on different vendors’ platforms and the performance gap between CPU-specific and GPU-specific OpenCL algorithms for execution on a modern GPU. Furthermore, a brief usability evaluation of the three frameworks is also presented.