Graphics processing units (GPUs) are widely adopted as compute accelerators in cloud
computing environments and supercomputers. Sharing GPU resources in such …

Heterogeneous multi-and many-core systems are increasingly prevalent in the desktop and
mobile domains. On these systems it is common for programs to compete with co-running …

Integrating GPU with CPU on the same chip is increasingly common in current processor
architectures for high performance. CPU and GPU share on-chip network, last level cache …

To handle the increasing data scale in broad fields, GPU (Graphic Processing Unit) is
integrated with more and more cores to provide powerful computing capability. To obtain …

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 …

Applications implemented for GPU are important in various fields. GPU has many parallel
computing cores and high arithmetic throughput, enabling GPU applications to work …

General purpose GPU (GPGPU) programming frameworks such as OpenCL and CUDA
allow running individual computation kernels sequentially on a device. However, in some …

Executing multiple OpenCL kernels on the same GPU concurrently is a promising method
for improving hardware utilisation and system performance. Schemes of scheduling impact …

As a critical computing resource in multiuser systems such as supercomputers, data centers,
and cloud services, a GPU contains multiple compute units (CUs). GPU Multitasking is an …

Heterogeneous computing machines consisting of a CPU and one or more GPUs are
increasingly being used today because of their higher performance-cost ratio and lower …