high performance computing on graphics processing units: hgpu.org

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Designing Fast Architecture Sensitive Tree Search on Modern Multi-Core/Many-Core Processors

Changkyu Kim, Jatin Chhugani, Nadathur Satish, Eric Sedlar, Anthony D. Nguyen, Tim Kaldewey, Victor W. Lee, Scott A. Brandt, Pradeep Dubey

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Tags: Compression, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 280, Search, Tesla C2050

December 25, 2011 by hgpu

Accelerating linear system solutions using randomization techniques

Marc Baboulin, Jack Dongarra, Julien Herrmann, Stanimire Tomov

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050

December 25, 2011 by hgpu

A block-asynchronous relaxation method for graphics processing units

Hartwig Anzt, Stanimire Tomov, Jack Dongarra, Vincent Heuveline

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Tesla C2050

December 22, 2011 by hgpu

Generative programming methods for parallel partial differential field equation solvers

Daniel Peter Playne

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Tags: Code generation, Computer science, CUDA, Field equations, MPI, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Tesla C2050, Thesis

December 22, 2011 by hgpu

Extending adaptive sparse grids for stochastic collocation to hybrid parallel architectures

Richard K. Archibald, Ralf Deiterding, John Jakeman

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Tags: Computer science, CUDA, Hybrid computing, nVidia, Stochastic simulation, Tesla C2050

December 22, 2011 by hgpu

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Jonathan Passerat-Palmbach, Jonathan Caux, Pridi Siregar, Claude Mazel, D.R.C.Hill

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Tags: Benchmarking, Computer science, CUDA, nVidia, Stochastic simulation, Tesla C2050, Warp level parallelism

December 21, 2011 by hgpu

Parallel Inference on Structured Data with CRFs on GPUs

Nico Piatkowski, Katharina Morik

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Tags: Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, Package, Tesla C2050

December 21, 2011 by hgpu

Automatic code generation and tuning for stencil kernels on modern shared memory architectures

Matthias Christen, Olaf Schenk, Helmar Burkhart

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Tesla C2050

December 18, 2011 by hgpu

A Parallel GPU Version of the Traveling Salesman Problem

Molly A. O'Neil, Dan Tamir, Martin Burtscher

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Package, Tesla C2050

December 16, 2011 by hgpu

Reducing Thread Divergence in GPU-based B and B Applied to the Flow-shop problem

Imen Chakroun, Ahcene Bendjoudi, Nouredine Melab

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050

December 16, 2011 by hgpu

Reducing Thread Divergence in GPU-based B&B Applied to the Flow-shop problem

Imen Chakroun, Ahcene Bendjoudi, Nouredine Melab

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050

December 16, 2011 by hgpu

Automatic library generation for BLAS3 on GPUs

Huimin Cui, Lei Wang, Jingling Xue, Yang Yang, Xiaobing Feng

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Tags: Algorithms, Computer science, CUBLAS, Linear Algebra, nVidia, nVidia GeForce 9800 GTX, nVidia GeForce GTX 285, Optimization, Tesla C2050

December 13, 2011 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

Designing Fast Architecture Sensitive Tree Search on Modern Multi-Core/Many-Core Processors

Accelerating linear system solutions using randomization techniques

A block-asynchronous relaxation method for graphics processing units

Generative programming methods for parallel partial differential field equation solvers

Extending adaptive sparse grids for stochastic collocation to hybrid parallel architectures

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Parallel Inference on Structured Data with CRFs on GPUs

Automatic code generation and tuning for stencil kernels on modern shared memory architectures

A Parallel GPU Version of the Traveling Salesman Problem

Reducing Thread Divergence in GPU-based B and B Applied to the Flow-shop problem

Reducing Thread Divergence in GPU-based B&B Applied to the Flow-shop problem

Automatic library generation for BLAS3 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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