OpenCL: A pArALLeL prOgrAmming StAndArd

T he strong need for increased computational performance in science and engineering has led to the use of heterogeneous computing, with GPUs and other accelerators acting as coprocessors for arithmetic intensive data-parallel workloads.1–4 OpenCL is a new industry standard for task-parallel and data-parallel heterogeneous computing on a variety of modern CPUs, GPUs, DSPs, and other microprocessor designs.5 This trend toward heterogeneous computing and highly parallel architectures has created a strong need for software development infrastructure in the form of parallel programming languages and subroutine libraries that can support heterogeneous computing on multiple vendors’ hardware platforms. To address this, developers adapted many existing science and engineering applications to take advantage of multicore CPUs and massively parallel GPUs using toolkits such as Threading Building Blocks (TBB), OpenMP, Compute Unified Device Architecture (CUDA),6 and others.7,8 Existing programming toolkits, however, were either limited to a single microprocessor family or didn’t support heterogeneous computing. OpenCL provides easy-to-use abstractions and a broad set of programming APIs based on past successes with CUDA and other programming toolkits. OpenCL defines core functionality that all devices support, as well as optional functionality for high-function devices; it also includes an extension mechanism that lets vendors expose unique hardware features and experimental programming interfaces for application developers’ benefit. Although OpenCL can’t mask significant differences in hardware architecture, it does guarantee portability and correctness. This makes it much easier for developers to start with a correctly functioning OpenCL program tuned for one architecture and produce a correctly functioning program optimized for another architecture.