high performance computing on graphics processing units: hgpu.org

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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

Auto-Tuning Dedispersion for Many-Core Accelerators

Alessio Sclocco, Henri E. Bal, Jason Hessels, Joeri van Leeuwen, Rob V. van Nieuwpoort

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Tags: Astrophysics, ATI, ATI Radeon HD 7970, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan, OpenCL, Tesla K20

February 25, 2014 by hgpu

Fast Hamiltonian Monte Carlo Using GPU Computing

Andrew L. Beam, Sujit K. Ghosh, Jon Doyle

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Tags: Algorithms, Bayesian, CUDA, CULA, Monte Carlo simulation, nVidia, Package, Python, Statistics, Tesla K20

February 19, 2014 by hgpu

Towards a Performance-Portable FFT Library for Heterogeneous Computing

Carlo del Mundo, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 6970, ATI Radeon HD 7970, FFT, nVidia, OpenCL, Signal processing, Spectral methods, Tesla C2075, Tesla K20

February 17, 2014 by hgpu

Petascale elliptic solvers for anisotropic PDEs on GPU clusters

Eike Hermann Muller, Robert Scheichl, Benson Muite, Eero Vainikko

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Tags: Algorithms, Computer science, CUDA, GPU cluster, Numerical Analysis, nVidia, Partial differential equations, PDEs, Tesla K20, Tesla M2090

February 17, 2014 by hgpu

Optimizing Performance of Stencil Code with SPL Conqueror

Alexander Grebhahn, Norbert Siegmund, Sven Apel, Sebastian Kuckuk, Christian Schmitt, Harald Kostler

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Tags: Computer science, CUDA, Differential equations, Finite difference, Image processing, nVidia, OpenCL, Package, Partial differential equations, PDEs, Tesla K20

February 17, 2014 by hgpu

Direct Numerical Simulation and Large Eddy Simulation on a Turbulent Wall-Bounded Flow Using Lattice Boltzmann Method and Multiple GPUs

Xian Wang, Yanqin Shangguan, Hiromichi KOBAYASHI, Takayuki AOKI, Naoyuki Onodera

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, Numerical simulation, nVidia, Tesla K20

February 16, 2014 by hgpu

LDetector: A Low Overhead Race Detector For GPU Programs

Pengcheng Li, Chen Ding, Xiaoyu Hu, Tolga Soyata

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20

February 16, 2014 by hgpu

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Sadaf Alam, Ugo Varetto

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, MPI, nVidia, OpenACC, OpenCL, Performance, Tesla K20, Tesla M2090

February 15, 2014 by hgpu

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

M. Bernaschi, M. Bisson, F. Salvadore

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Tags: Benchmarking, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, Physics, Spin Systems, Tesla K20

February 14, 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

GROMACS on Hybrid CPU-GPU and CPU-MIC Clusters: Preliminary Porting Experiences, Results and Next Steps

Sadaf Alam, Ugo Varetto

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, Intel Xeon Phi, MPI, nVidia, OpenCL, Tesla K20

February 12, 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

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

Auto-Tuning Dedispersion for Many-Core Accelerators

Fast Hamiltonian Monte Carlo Using GPU Computing

Towards a Performance-Portable FFT Library for Heterogeneous Computing

Petascale elliptic solvers for anisotropic PDEs on GPU clusters

Optimizing Performance of Stencil Code with SPL Conqueror

Direct Numerical Simulation and Large Eddy Simulation on a Turbulent Wall-Bounded Flow Using Lattice Boltzmann Method and Multiple GPUs

LDetector: A Low Overhead Race Detector For GPU Programs

Accelerator Aware MPI Micro-benchmarking using CUDA, OpenACC and OpenCL

Multi-Kepler GPU vs. Multi-Intel MIC for spin systems simulations

Designing Bit-Reproducible Portable High-Performance Applications

GROMACS on Hybrid CPU-GPU and CPU-MIC Clusters: Preliminary Porting Experiences, Results and Next Steps

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

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