Device specialization in heterogeneous multi-GPU environments

Accelerating high-order WENO schemes using two heterogeneous GPUs

A double-GPU code is developed to accelerate WENO schemes. The test problem is a compressible viscous flow. The convective terms are discretized using third- to ninth-order WENO schemes and the viscous terms are discretized by the standard fourth-order central scheme. The code written in CUDA programming language is developed by modifying a single-GPU code. The OpenMP library is used for parall...

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

During the last decade, parallel processor architectures have become a powerful tool to deal with massively-parallel problems that require High Performance Computing (HPC). The last trend of HPC is the use of heterogeneous environments, that combine different computational power units, such as CPU-cores and GPUs. Performance maximization of any GPU parallel implementation of an algorithm requir...

Heterogeneous GPU reallocation

Emerging cloud markets like spot markets and batch computing services scale up services at the granularity of whole VMs. In this paper, we observe that GPU workloads underutilize GPU device memory, leading us to explore the benefits of reallocating heterogeneous GPUs within existing VMs. We outline approaches for upgrading and downgrading GPUs for OpenCL GPGPU workloads, and show how to minimiz...

Runtime specialization for heterogeneous CPU-GPU platforms

Execution of Compound Multi-Kernel OpenCL Computations in Multi-CPU/Multi-GPU Environments

Current computational systems are heterogeneous by nature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming of these hybrid systems as a whole. The distinct nature of the architectural and execution models in place raises several challenges, as the best hardware con...