Typically, the execution time of a kernel on a GPU is a difficult to predict measure as it
depends on a wide range of factors. Performance can be limited by either memory transfer …

Contemporary graphics processing units (GPUs) support dynamic voltage and frequency
scaling to balance computational performance and energy consumption. However, accurate …

Energy conservation of large data centers for high performance computing workloads, such
as deep learning with Big Data, is of critical significance, where cutting down a few percent …

Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU
cores are typically required to execute applications concurrently. However, as will be shown …

Energy conservation of large data centers for high-performance computing workloads, such
as deep learning with big data, is of critical significance, where cutting down a few percent of …

Heterogeneous Mobile System-on-Chips (SoCs) containing CPU and GPU cores are
becoming prevalent in embedded computing, and they need to execute applications …

As datasets grow, high performance computing has gradually become an important tool for
artificial intelligence, particularly due to the powerful and efficient parallel computing …

Graphics Processing Units (GPUs) are vastly used for running massively parallel programs.
GPU kernels exhibit different behavior at runtime and can usually be classified in a simple …

The corresponding concepts and calculation methods are very in line with the requirements
of information platform resource scheduling. Based on this, this paper discusses the related …

Graphics processing units (GPUs) are powerful in performing data-parallel applications.
Such applications most often rely on the GPU's memory hierarchy to deliver high …