Modern computer systems are becoming distributed and heterogeneous by comprising multi-core CPUs, GPUs, and other accelerators. However, to program such systems, the user currently has to use a combination of several programming models (e.g., MPI with OpenCL or CUDA), which is difficult and error-prone. We present dOpenCL (distributed OpenCL) – a uniform approach to programming distributed syst...

The importance of heterogeneous multicore programming is increasing, and Open Computing Language (OpenCL) is an open industrial standard for parallel programming that provides a uniform programming model for programmers to write efficient, portable code for heterogeneous computing devices. However, OpenCL is not supported in the system virtualization environments that are often used to improve ...

With the broad deployment of multicore processors, there are increasing demands to port OpenCL programs written for GPUs onto the multicore processors. However, OpenCL programs written for GPUs cannot run efficiently on multicore processors since GPU-oriented OpenCL programs generally consist of a huge number of threads. This paper presents experimental comparisons of three thread execution met...

With heterogeneous computing becoming mainstream, researchers and software vendors have been trying to exploit the best of the underlying architectures like GPUs or CPUs to enhance performance. Parallel programming models play a crucial role in achieving this enhancement. One such model is OpenCL, a parallel computing API for cross platform computations targeting heterogeneous architectures. Ho...

Recently, OpenCL, a new open programming standard for GPGPU programming, has become available in addition to CUDA. OpenCL can support various compute devices due to its higher abstraction programming framework. Since there is a semantic gap between OpenCL and compute devices, the OpenCL C compiler plays important roles to exploit the potential of compute devices and therefore its capability sho...

CUDA and OpenCL offer two different interfaces for programming GPUs. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. Although OpenCL promises a portable language for GPU programming, its generality may entail a performance penalty. In this paper, we compare the performance of CUDA and OpenCL usin...

This paper describes Bacon, a data-parallel programming system targeting OpenCL-compatible graphics processors. This system is built upon the existing OpenCL standard in order to make it easier for programmers to write high performance kernels for GPU accelerated applications. The OpenCL C syntax is extended into a new language, Bacon C, intended to make development significantly more convenien...

Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget computing units in devices, careful implementation algorithms critical. In this paper, we analyze performance bottleneck two well-known for object tracking: detection optical flow Open-source Computer Vision library (OpenCV). Based o...

The increase in the data acquisition and processing needs of High Energy Physics experiments has made it more essential to use FPGAs to meet those needs. However harnessing the capabilities of the FPGAs has been hard for anyone but expert FPGA developers. The arrival of OpenCL with the two major FPGA vendors supporting it, offers an easy software-based approach to taking advantage of FPGAs in a...

This paper describes Bacon, a data-parallel programming system targeting OpenCL-compatible graphics processors. This system is built upon the existing OpenCL standard in order to make it easier for programmers to write high performance kernels for GPU accelerated applications. The OpenCL C syntax is extended into a new language, Bacon C, intended to make development significantly more convenien...