high performance computing on graphics processing units: hgpu.org

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RapidMind: Portability across Architectures and its Limitations

Iris Christadler, Volker Weinberg

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Tags: Cell processor, Computer science, CUDA, nVidia, Performance, Programming Languages, Tesla C1060, Tesla S1070

November 12, 2010 by hgpu

Exploring the Limits of GPUs With Parallel Graph Algorithms

Frank Dehne, Kumanan Yogaratnam

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 260, Performance

November 12, 2010 by hgpu

Magnetohydrodynamics on Heterogeneous architectures: a performance comparison

Bijia Pang, Ue-li Pen, Michael Perrone

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Tags: Astrophysics, ATI, ATI Radeon HD 5870, Cell processor, Computational Physics, CUDA, Electrodynamics, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 260, OpenCL, OpenMP, Performance

November 11, 2010 by hgpu

General purpose Molecular Dynamics Simulations on GPUs: Issues of Pair Forces and Scaling to large Clusters

Christian Trott, Lars Winterfeld

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Tags: Computational Physics, Condensed matter, CUDA, GPU cluster, Materials Science, Molecular dynamics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, Package, Performance, Physics, Tesla C1060, Tesla S1070

November 9, 2010 by hgpu

Fast Histograms using Adaptive CUDA Streams

Sisir Koppaka, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan

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Tags: Computer science, CUDA, nVidia, Performance

November 9, 2010 by hgpu

On Dynamic Load Balancing on Graphics Processors

Daniel Cederman, Philippas Tsigas

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 9600 GT, Optimization, Performance

November 5, 2010 by hgpu

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Allen D. Malony,Scott Biersdorff,Wyatt Spear,Shangkar Mayanglambam

Tags: Computer science, CUDA, nVidia, Performance

November 5, 2010 by hgpu

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

E. Z. Zhang, Y. Jiang, G. Ziyu, X. Shen

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, Programming Languages, Tesla C1060

November 5, 2010 by hgpu

The Scalable Heterogeneous Computing (SHOC) benchmark suite

Anthony Danalis,Gabriel Marin,Collin McCurdy,Jeremy S. Meredith,Philip C. Roth,Kyle Spafford,Vinod Tipparaju,Jeffrey S. Vetter

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Tags: ATI, ATI Radeon HD 5770, ATI Radeon HD 5870, ATI Stream, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTS, nVidia GeForce 8800 GTX, nVidia GeForce 9400 M, OpenCL, Package, Performance, Tesla C1060

November 5, 2010 by hgpu

A Performance Comparison of CUDA and OpenCL

Kamran Karimi, Neil G. Dickson, Firas Hamze

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Tags: Computational Physics, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, OpenCL, Package, Performance

November 5, 2010 by hgpu

Fast tridiagonal solvers on the GPU

Yao Zhang, Jonathan Cohen, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance

November 4, 2010 by hgpu

Performance Predictions for General-Purpose Computation on GPUs

Weiguo Liu, Wolfgang M. Wittig, Bertil Schmidt

Tags: Computer science, Performance

November 2, 2010 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

RapidMind: Portability across Architectures and its Limitations

Exploring the Limits of GPUs With Parallel Graph Algorithms

Magnetohydrodynamics on Heterogeneous architectures: a performance comparison

General purpose Molecular Dynamics Simulations on GPUs: Issues of Pair Forces and Scaling to large Clusters

Fast Histograms using Adaptive CUDA Streams

On Dynamic Load Balancing on Graphics Processors

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

The Scalable Heterogeneous Computing (SHOC) benchmark suite

A Performance Comparison of CUDA and OpenCL

Fast tridiagonal solvers on the GPU

Performance Predictions for General-Purpose Computation on GPUs

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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