high performance computing on graphics processing units: hgpu.org

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LLVM to PTX Backend

Marco Keller

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, PTX

September 26, 2011 by hgpu

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

Pablo Lamilla Alvarez, Shinichi Yamagiwa, Masahiro Arai, Koichi Wada

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GT 220, nVidia GeForce GTX 280, OpenCL, OpenGL, Performance

September 26, 2011 by hgpu

High-level GPU computing with jacket for MATLAB and C/C++

Gallagher Pryor, Brett Lucey, Sandeep Maddipatla, Chris McClanahan, John Melonakos, Vishwanath Venugopalakrishnan, Krunal Patel, Pavan Yalamanchili, James Malcolm

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Source codes

Tags: Computer science, CUDA, FFT, Image processing, Linear Algebra, nVidia, nVidia GeForce 9600, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenGL, Package, PTX, Tesla C1060, Tesla C2070

September 26, 2011 by hgpu

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Peng Du, Rick Weber, Piotr Luszczek, Stanimire Tomov, Gregory Peterson, Jack Dongarra

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Performance, Tesla C2050

September 26, 2011 by hgpu

Identifying scalar behavior in CUDA kernels

Sylvain Collange

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, PTX

September 26, 2011 by hgpu

Putting Automatic Polyhedral Compilation for GPGPU to Work

Soufiane Baghdadi, Armin Grosslinger, Albert Cohen

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, Package

September 26, 2011 by hgpu

Running unstructured grid-based CFD solvers on modern graphics hardware

Andrew Corrigan, Fernando F. Camelli, Rainald Lohner, John Wallin

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, Tesla

September 26, 2011 by hgpu

Harnessing the Power of GPUs without Losing Abstractions in SaC and ArrayOL: A Comparative Study

Jing Guo, Wendell Rodrigues, Jeyarajan Thiyagalingamz, Frederic Guyomarch, Pierre Boulet, Sven-Bodo Scholz

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Video encoding

September 25, 2011 by hgpu

Numerical Accuracy Differences in CPU and GPGPU Codes

Devon Yablonski

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Tags: Computer science, CUDA, Floating point error, nVidia, Tesla C1060, Thesis

September 25, 2011 by hgpu

GPU-Based Acceleration of the MLEM Algorithm for SPECT Parallel Imaging with Attenuation Correction and Compensation for Detector Response

Akos Szlavecz, Gabor Hesz, Tamas Bukki, Bela Kari, Balazs Benyo

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Tags: Algorithms, Computed tomography, CT, CUDA, Medicine, nVidia, nVidia GeForce GTX 480, Tomography, Visualization

September 25, 2011 by hgpu

Algorithm Acceleration from GPGPUs for the ATLAS Upgrade

P.J. Clark, C. Jones, D. Emeilyanov, M. Rovatsou, A. Washbrook

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Tags: Algorithms, CUDA, High Energy Physics - Experiment, nVidia, Physics, Tesla C1060, Tesla C2050

September 25, 2011 by hgpu

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

Keun Soo Yim, Cuong Pham, Mushfiq Saleheen, Zbigniew Kalbarczyk, Ravishankar Iyer

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Tags: Computer science, CUDA, Error recovery, Fault tolerance, nVidia, Tesla S1070

September 25, 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

LLVM to PTX Backend

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

High-level GPU computing with jacket for MATLAB and C/C++

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Identifying scalar behavior in CUDA kernels

Putting Automatic Polyhedral Compilation for GPGPU to Work

Running unstructured grid-based CFD solvers on modern graphics hardware

Harnessing the Power of GPUs without Losing Abstractions in SaC and ArrayOL: A Comparative Study

Numerical Accuracy Differences in CPU and GPGPU Codes

GPU-Based Acceleration of the MLEM Algorithm for SPECT Parallel Imaging with Attenuation Correction and Compensation for Detector Response

Algorithm Acceleration from GPGPUs for the ATLAS Upgrade

Hauberk: Lightweight Silent Data Corruption Error Detector for GPGPU

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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