high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

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

Optimal Control of the Process Systems Using Graphic Processing Unit

Arash Sadrieh, Parisa A. Bahri

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance

October 31, 2011 by hgpu

SIMD Re-Convergence At Thread Frontiers

Gregory Diamos, Andrew Kerr, Haicheng Wu, Sudhakar Yalamanchili, Benjamin Ashbaugh, Subramaniam Maiyuran

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Tags: Computer science, CUDA, nVidia, Performance, Programming Languages, PTX

October 31, 2011 by hgpu

Exploring Many-Core Design Templates for FPGAs and ASICs

Ilia Lebedev, Christopher Fletcher, Shaoyi Cheng, James Martin, Austin Doupnik, Daniel Burke, Mingjie Lin, John Wawrzynek

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Tags: ASIC, Bayesian, Computer science, FPGA, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

October 30, 2011 by hgpu

Leveraging Binary Translation for Heterogeneous Profiling

Dan Upton, Kim Hazelwood

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, nVidia, OpenCL, Performance, Tesla C2050

October 30, 2011 by hgpu

Dynamic Scheduling of Parallel Code for Heterogeneous Systems

Jeff Brantley, Chris Gregg

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Tags: ATI, ATI Radeon HD 4350, Computer science, Heterogeneous systems, OpenCL, Performance

October 30, 2011 by hgpu

GPU based particle system

Martin Wexo Olsson

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Tags: ATI, ATI Radeon HD 4870 X2, ATI Radeon HD 5770, Game physics, OpenCL, Particle simulation, Performance, Physics

October 29, 2011 by hgpu

Parallel Random Numbers: As Easy as 1, 2, 3

John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw

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Source codes

Tags: AES, ATI, ATI Radeon HD 6970, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Performance, Pseudo-random number generators, Security

October 28, 2011 by hgpu

Techniques to maximize memory bandwidth on the Rigel compute accelerator

William J. Tuohy

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Tags: ATI, ATI Radeon HD 5870, Computer science, Memory, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Tesla T10, Thesis

October 27, 2011 by hgpu

A Memory Efficient and Fast Sparse Matrix Vector Product on a GPU

A. Dziekonski, A. Lamecki, M. Mrozowski

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Tags: Computer science, CUDA, Electrodynamics, FEM, Finite element method, nVidia, nVidia GeForce GTX 480, Performance, Sparse matrix

October 26, 2011 by hgpu

Design and implementation of a high-performance stream-based computing platform on multigenerational GPUs

Pablo Lamilla Alvarez

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Tags: Computer science, GPU cluster, nVidia, nVidia GeForce GT 220, OpenCL, Performance, Thesis

October 24, 2011 by hgpu

Power and Performance Studies of the Explicit Multi-Threading (XMT) Architecture

Fuat Keceli

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Tags: Algorithms, ASIC, Benchmarking, Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Package, Performance, Thesis

October 23, 2011 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

Performance Portability of a GPU Enabled Factorization with the DAGuE Framework

Optimal Control of the Process Systems Using Graphic Processing Unit

SIMD Re-Convergence At Thread Frontiers

Exploring Many-Core Design Templates for FPGAs and ASICs

Leveraging Binary Translation for Heterogeneous Profiling

Dynamic Scheduling of Parallel Code for Heterogeneous Systems

GPU based particle system

Parallel Random Numbers: As Easy as 1, 2, 3

Techniques to maximize memory bandwidth on the Rigel compute accelerator

A Memory Efficient and Fast Sparse Matrix Vector Product on a GPU

Design and implementation of a high-performance stream-based computing platform on multigenerational GPUs

Power and Performance Studies of the Explicit Multi-Threading (XMT) Architecture

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)