With the growing power of GPUs, how to utilize the high computing performance provided by
the GPU hardware becomes an urgent yet challenging problem, especially for applications …

GPUs are seeing increasingly widespread use for general purpose computation due to their
excellent performance for highly-parallel, throughput-oriented applications. For many …

The conventional OpenCL 1. x style CPU-GPU heterogeneous computing paradigm treats
the CPU and GPU processors as loosely connected separate entities. At best each executes …

Heterogeneous systems are ubiquitous in the field of High-Performance Computing (HPC).
Graphics processing units (GPUs) are widely used as accelerators for their enormous …

The disparity between the CPU and GPU domains has forced the programmers to adhere to
the traditional driver-based GPU programming approach. The negative implications of this …

GPUs are widely used as powerful accelerators for data-parallel applications such as
financial and scientific applications in industrial and scientific areas. Effective scheduling of …

The use of computational accelerators, specifically programmable GPUs, is becoming
popular in cloud computing environments. Cloud vendors currently provide GPUs as …

Commodity graphic processing units (GPUs) have rapidly evolved to become high
performance accelerators for data-parallel computing through a large array of processing …

Graphics processing units (GPUs) are attractive to the generalpurpose computing (GPGPU)
beyond the graphics purpose. Sharing GPUs among such GPGPU applications is a key …

The computational power and the physical memory size of a single GPU device are often
insufficient for large-scale problems. Using CUDA, the user must explicitly partition such …