high performance computing on graphics processing units: hgpu.org

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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

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

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

Accelerating image recognition on mobile devices using GPGPU

Miguel Bordallo Lopez, Henri Nykanen, Jari Hannuksela, Olli Silven, Markku Vehvilainen

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Tags: Algorithms, Computer science, Image processing, Image recognition, OpenCL, OpenGL

September 25, 2011 by hgpu

GPGPU workload analysis and media performance studies

Ashwini Simha

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Tags: ATI, Benchmarking, Computer science, OpenCL, Performance, Thesis

September 25, 2011 by hgpu

Utilising OpenCL Framework for Ray-Tracing Acceleration

Pavel Hradsky

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Tags: 3D Graphics and Realism, Computer science, nVidia, nVidia GeForce GTS 250, OpenCL, Raytracing, Thesis

September 24, 2011 by hgpu

A portable implementation of the radix sort algorithm in OpenCL

Philippe Helluy

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Tags: Algorithms, ATI, ATI Radeon HD 5850, nVidia, nVidia GeForce GTX 320 M, nVidia GeForce GTX 470, OpenCL, Package, Particle-in-cell methods, Physics, Plasma physics, Sorting

September 24, 2011 by hgpu

Analyzing Use of OpenCL on the Cell Broadband Engine and a Proposal for OpenCL Extensions

Jens Breitbart, Claudia Fohry

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Tags: Cell processor, Computer science, Distributed computing, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Playstation

September 24, 2011 by hgpu

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

Deepthi Nandakumar

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Thesis

September 24, 2011 by hgpu

OpenCL: a viable solution for high-performance medical image reconstruction?

Christian Siegl, H. G. Hofmann, B. Keck, M. Prummer, J. Hornegger

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Tags: ATI, ATI Radeon HD 5870, Image processing, Image reconstruction, nVidia, OpenCL, Optimization, Tesla C1060

September 24, 2011 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

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

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

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

Accelerating image recognition on mobile devices using GPGPU

GPGPU workload analysis and media performance studies

Utilising OpenCL Framework for Ray-Tracing Acceleration

A portable implementation of the radix sort algorithm in OpenCL

Analyzing Use of OpenCL on the Cell Broadband Engine and a Proposal for OpenCL Extensions

Automatic Translation of CUDA to OpenCL and Comparison of Performance Optimizations on GPUs

OpenCL: a viable solution for high-performance medical image reconstruction?

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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