high performance computing on graphics processing units: hgpu.org

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Solving Incompressible Two-Phase Flows on Massively Parallel Multi-GPU Clusters

P. Zaspel, M. Griebel

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Tags: Compression, CUDA, Finite difference, Fluid dynamics, GPU cluster, Navier-Stokes equations, NSEs, nVidia, Tesla M1060, Tesla S1070

November 14, 2011 by hgpu

A multi-GPU acceleration for 3D imaging of the prostate

E. A. Attardo, A. Borsic, R. J. Halter

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Tags: CUDA, Medicine, nVidia, Tesla S1070, Tomography, Ultrasound

November 12, 2011 by hgpu

Sustainable GPU Computing at Scale

Justin Y. Shi, Moussa Taifi, Abdallah Khreishah, Jie Wu

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Tags: Computer science, CUBLAS, CUDA, Energy-efficient computing, Fault tolerance, nVidia, OpenMPI, Tesla S1070

November 12, 2011 by hgpu

Performance Portability of a GPU Enabled Factorization with the DAGuE Framework

George Bosilca, Aurelien Bouteiller, Thomas Herault, Pierre Lemarinier, Narapat Ohm Saengpatsa, Stanimire Tomov, Jack J. Dongarra

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Tags: Computer science, Factorization, GPU cluster, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla C1060, Tesla C2050, Tesla S1070

November 3, 2011 by hgpu

Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs

Vincent Heuveline, Dimitar Lukarski, Nico Trost, Jan-Philipp Weiss

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Tags: Algorithms, Computer science, CUDA, Differential equations, Linear Algebra, nVidia, Package, Partial differential equations, PDEs, Tesla S1070

October 31, 2011 by hgpu

Dense Dynamic Programming on Multi GPU

Vincent Boyer, Didier El Baz, Moussa Elkihel

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Tags: Computer science, CUDA, GPU cluster, Hybrid computing, nVidia, Tesla S1070

October 26, 2011 by hgpu

A case study on porting scientific applications to GPU/CUDA

Javier Delgado, Joao Gazolla, Esteban Clua, S. Masoud Sadjadi

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Tags: Computer science, CUDA, Earth and Space Sciences, Fortran, nVidia, nVidia GeForce 9300 M, nVidia GeForce 9400 M, Tesla S1070, Weather prediction

October 26, 2011 by hgpu

Scalability of Incompressible Flow Computations on Multi-GPU Clusters Using Dual-Level and Tri-Level Parallelism

Dana A. Jacobsen, Inanc Senocak

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Tags: CUDA, Fluid dynamics, GPU cluster, MPI, nVidia, OpenMP, Tesla C1060, Tesla S1070

October 25, 2011 by hgpu

Enabling Traceability in an MDE Approach to Improve Performance of GPU Applications

Antonio Wendellde O. Rodrigues, Vincent Aranega, Anne Etien, Frederic Guyomarc'h, Jean-Luc Dekeyser

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Tags: Algorithms, Computer science, nVidia, OpenCL, Performance, Programming techniques, Tesla S1070

October 18, 2011 by hgpu

Benchmarking Across Platforms: European Option Pricing

Igor N. Kozin

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Tags: AMD FireStream 9270, Benchmarking, Cell processor, Computer science, CUDA, Finance, nVidia, OpenCL, Tesla M2050, Tesla S1070

October 13, 2011 by hgpu

PlinkGPU: A Framework for GPU Acceleration of Whole Genome Data Analysis

Jeffrey Poznanovic

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, OpenCL, Tesla S1070

October 13, 2011 by hgpu

A Massive Data Parallel Computational Framework on Petascale/Exascale Hybrid Computer Systems

Marek Blazewicz, Steven R. Brandt, Peter Diener, David M. Koppelman, Krzysztof Kurowski, Frank Loffler, Erik Schnetter, Jian Tao

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, nVidia, OpenCL, Optimization, Package, Programming techniques, Tesla S1070

October 4, 2011 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Solving Incompressible Two-Phase Flows on Massively Parallel Multi-GPU Clusters

A multi-GPU acceleration for 3D imaging of the prostate

Sustainable GPU Computing at Scale

Performance Portability of a GPU Enabled Factorization with the DAGuE Framework

Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs

Dense Dynamic Programming on Multi GPU

A case study on porting scientific applications to GPU/CUDA

Scalability of Incompressible Flow Computations on Multi-GPU Clusters Using Dual-Level and Tri-Level Parallelism

Enabling Traceability in an MDE Approach to Improve Performance of GPU Applications

Benchmarking Across Platforms: European Option Pricing

PlinkGPU: A Framework for GPU Acceleration of Whole Genome Data Analysis

A Massive Data Parallel Computational Framework on Petascale/Exascale Hybrid Computer Systems

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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