high performance computing on graphics processing units: hgpu.org

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TEG: GPU Performance Estimation Using a Timing Model

Junjie Lai, Andre Seznec

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

November 23, 2011 by hgpu

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Chris Gregg, Michael Boyer, Kim Hazelwood, Kevin Skadron

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Tags: ATI, ATI Radeon HD 5870, Computer science, Heterogeneous systems, OpenCL, Performance, Task scheduling

November 22, 2011 by hgpu

Experiences with Achieving Portability across Heterogeneous Architectures

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, OpenMP, Performance

November 22, 2011 by hgpu

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Rajib Nath, Stanimire Tomov, Tingxing "Tim" Dong, Jack Dongarra

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Source codes

Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package, Performance, Tesla C2050

November 20, 2011 by hgpu

Exploiting concurrent kernel execution on graphic processing units

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla C2070

November 19, 2011 by hgpu

Towards Efficient GPU Sharing on Multicore Processors

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Programming techniques, Tesla C1060, Tesla C2070

November 19, 2011 by hgpu

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 560 Ti, Performance, Tesla C2070

November 19, 2011 by hgpu

Design and Implementation of a PTX Emulation Library

Albert Claret Exojo

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Tags: Computer science, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Performance, PTX, Tesla S870, Thesis

November 18, 2011 by hgpu

Programming Future Parallel Architectures with Haskell and Intel ArBB

Bo Joel Svensson, Ryan Newton

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance, Programming Languages

November 17, 2011 by hgpu

Characterization and Transformation of Unstructured Control Flow in GPU Applications

Haicheng Wu, Gregory Diamos, Si Li, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Performance, Programming Languages

November 17, 2011 by hgpu

Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs

Allen D. Malony, Scott Biersdorff, Sameer Shende, Heike Jagode, Stanimire Tomov, Guido Juckeland, Robert Dietrich, Duncan Poole, Christopher Lamb

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla C870

November 17, 2011 by hgpu

Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters

Long Wang, Weile Jia, Xuebin Chi, Yue Wu, Weiguo Gao, Lin-Wang Wang

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Tags: Algorithms, Computational chemistry, CUDA, GPU cluster, MPI, nVidia, Performance, Physics, Tesla C2050

November 14, 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

TEG: GPU Performance Estimation Using a Timing Model

Dynamic Heterogeneous Scheduling Decisions Using Historical Runtime Data

Experiences with Achieving Portability across Heterogeneous Architectures

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Exploiting concurrent kernel execution on graphic processing units

Towards Efficient GPU Sharing on Multicore Processors

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

Design and Implementation of a PTX Emulation Library

Programming Future Parallel Architectures with Haskell and Intel ArBB

Characterization and Transformation of Unstructured Control Flow in GPU Applications

Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs

Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters

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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