high performance computing on graphics processing units: hgpu.org

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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

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Code generation, Computer science, nVidia, OpenCL, Optimization, Statistics, Tesla C1060

September 25, 2011 by hgpu

Exploiting Heterogeneous Computing Platforms By Cataloging Best Solutions For Resource Intensive Seismic Applications

Thomas Grosser, Alexandros Gremm, Sebastian Veith, Gerald Heim, Wolfgang Rosenstiel, Victor Medeiros, Manoel Eusebio de Lima

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Tags: Earth and Space Sciences, FPGA, Geoscience, Heterogeneous systems, nVidia, OpenCL, Optimization, Seismic modeling, Seismology, Tesla T10P

September 25, 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

The Test and Evaluation Uses of Heterogeneous Computing: GPGPUs and Other Approaches

Dan M. Davis, Gene Wagenbreth, Robert F. Lucas, Paul C. Gregory

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Tags: Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, Overview, Tesla C1060

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

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

A Performance Portable Matrix Free Dense MTTKRP in GenTen

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

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high performance computing on graphics processing units: hgpu.org

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

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Exploiting Heterogeneous Computing Platforms By Cataloging Best Solutions For Resource Intensive Seismic Applications

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

Numerical Accuracy Differences in CPU and GPGPU Codes

The Test and Evaluation Uses of Heterogeneous Computing: GPGPUs and Other Approaches

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

AutoDock-GPU: AutoDock for GPUs and other accelerators

NCCLX: collective communication framework

Tutoring LLM into a Better CUDA Optimizer

Kernel Library for LLM Serving

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Genten: Software for Generalized Tensor Decompositions by Sandia National Laboratories

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

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