Heterogeneous systems | Page 58

hgpu.org » Heterogeneous systems

Designing Efficient MPI and UPC Runtime for Multicore Clusters with InfiniBand, Accelerators and Co-Processors

Miao Luo

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Tesla C2050

March 9, 2014 by hgpu

Dynamic Workload Division in GPU-CPU Heterogeneous Systems

Wei Chen

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C2070, Thesis

March 7, 2014 by hgpu

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

K.A. Hawick, D.P.Playne

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Software Engineering

March 4, 2014 by hgpu

Multi-scale problems, high performance computing and hybrid numerical methods

Guillaume Balarac, Georges-Henri Cottet, Jean-Mathieu Etancelin, Jean-Baptiste Lagaert, Franck Perignon, Christophe Picard

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Tags: Heterogeneous systems, Mathematics, nVidia, OpenCL, Tesla K20

March 3, 2014 by hgpu

Performance Assessment of A Multi-block Incompressible Navier-Stokes Solver using Directive-based GPU Programming in a Cluster Environment

Lixiang Luo, Jack R. Edwards, Hong Luo, Frank Mueller

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, nVidia, OpenACC, Tesla K20

February 28, 2014 by hgpu

Heterogenous Acceleration for Linear Algebra in Multi-Coprocessor Environments

Azzam Haidar, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Computer science, Factorization, Heterogeneous systems, Intel Xeon Phi, Linear Algebra

February 28, 2014 by hgpu

An Evaluation of the GAMA/StarPU Frameworks for Heterogeneous Platforms: the Progressive Photon Mapping Algorithm

Miguel Branco Palhas

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Heterogeneous systems, Memory model, nVidia, Rendering, Tesla K20, Tesla M2090, Thesis

February 25, 2014 by hgpu

Towards Porting a Real-World Seismological Application to the Intel MIC Architecture

V. Weinberg, M. Allalen, G. Brietzke

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Tags: Earth and Space Sciences, Fortran, Geoscience, Heterogeneous systems, Intel Xeon Phi, MPI, Seismology

February 16, 2014 by hgpu

Designing Bit-Reproducible Portable High-Performance Applications

Andrea Arteaga, Oliver Fuhrer, Torsten Hoefler

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Portability, Tesla K20

February 12, 2014 by hgpu

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Tarun Beri, Sorav Bansal, Subodh Kumar

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Tags: Computer science, CUDA, FFT, GPU cluster, Heterogeneous systems, Matrix multiplication, Memory model, nVidia, Prefetch, Task scheduling, Tesla M2070

February 11, 2014 by hgpu

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Vivek K. Pallipuram

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Performance, Tesla K20, Tesla M2075, Thesis

February 11, 2014 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 5, 2014 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Designing Efficient MPI and UPC Runtime for Multicore Clusters with InfiniBand, Accelerators and Co-Processors

Dynamic Workload Division in GPU-CPU Heterogeneous Systems

On-Demand Source Code Generation & Scheduling Optimised Parallel Applications on Heterogeneous Platforms

Multi-scale problems, high performance computing and hybrid numerical methods

Performance Assessment of A Multi-block Incompressible Navier-Stokes Solver using Directive-based GPU Programming in a Cluster Environment

Heterogenous Acceleration for Linear Algebra in Multi-Coprocessor Environments

An Evaluation of the GAMA/StarPU Frameworks for Heterogeneous Platforms: the Progressive Photon Mapping Algorithm

Towards Porting a Real-World Seismological Application to the Intel MIC Architecture

Designing Bit-Reproducible Portable High-Performance Applications

A scheduling and runtime framework for a cluster of heterogeneous machines with multiple accelerators

Exploring Multiple Levels of Performance Modeling for Heterogeneous Systems

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)