Graphics processing units (GPUs) have become a very powerful platform embracing a concept of heterogeneous many-core computing. However, application domains of GPUs are currently limited to specific systems, largely due to a lack of “first-class” GPU resource management for general-purpose multi-tasking systems. We present Gdev, a new ecosystem of GPU resource management in the operating system...

GPU-based computing systems have become a widely accepted solution for the high-performance-computing (HPC) domain. GPUs shown highly competitive performance-per-watt ratios and can exploit an astonishing level of parallelism. However, exploiting peak performance such devices is challenge, mainly due to combination two essential aspects multi-GPU execution. On one hand, workload should be distr...

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...

This paper discusses an OpenCL version of a volumetric JPEG 2000 codec that runs on GPUs, multi-core processors or a combination of both. Since the performance critical part consists of a fine-grained (discrete wavelet transform) and coarse-grained algorithm (Tier-1), the best performance is obtained with a hybrid execution in which the discrete wavelet transform is executed on a GPU and Tier-1...

Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and clusterbased parallelism demand for flexible and scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualization, scalable rendering systems are essential to cope with the rapid growth of data sets. In this poster we pre...

We present a GPU implementation of LAMMPS, a widely-used parallel molecular dynamics (MD) software package, and show 5x to 13x single node speedups versus the CPU-only version of LAMMPS. This new CUDA package for LAMMPS also enables multi-GPU simulation on hybrid heterogeneous clusters, using MPI for inter-node communication, CUDA kernels on the GPU for all methods working with particle data, a...

The Graphics Processing Unit (GPU) is now commonly used for graphics and data-parallel computing. As more and more applications tend to accelerate on the GPU in multi-tasking environments where multiple tasks access the GPU concurrently, operating systems must provide prioritization and isolation capabilities in GPU resource management, particularly in real-time setups. We present TimeGraph, a ...

Sustaining a large fraction of single GPU performance in parallel computations is considered to be the major problem of GPU-based clusters. In this article, this topic is addressed in the context of a lattice Boltzmann flow solver that is integrated in the WaLBerla software framework. We propose a multi-GPU implementation using a block-structured MPI parallelization, suitable for load balancing...

In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) particle physics processes. order to automate MC generic number processes, design program which provides user possibility simulate custom processes through MadGraph5_aMC@NLO framework. The pipeline includes first stage where analytic expressions matrix ele...

A typical consumer desktop computer has a multi-core CPU with at least two and up to eight processing elements over two processors, and a multi-core GPU with up to 512 processing elements. Both the CPU and the GPU are capable of running parallel code, yet it is not obvious when to utilize one processor or the other because of workload considerations and, as importantly, contention on each devic...