high performance computing on graphics processing units: hgpu.org

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Improving GPU Performance Prediction with Data Transfer Modeling

Michael Boyer, Jiayuan Meng, Kalyan Kumaran

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Tags: Computer science, CUDA, nVidia, nVidia Quadro FX 5600, Performance

April 6, 2013 by hgpu

Optimising Purely Functional GPU Programs

Trevor L. McDonell, Manuel M. T. Chakravarty, Gabriele Keller, Ben Lippmeier

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Tags: Computer science, CUDA, Haskell, nVidia, Package, Performance, Tesla T10

April 4, 2013 by hgpu

Warp Size Impact in GPUs: Large or Small?

Ahmad Lashgar, Amirali Baniasadi, Ahmad Khonsari

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Tags: Computer science, GPGPU-sim, Memory, Performance

March 29, 2013 by hgpu

How to Benefit from AMD, Intel and Nvidia Accelerator Technologies in Scilab

Thibault Person, Francois Bodin

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Intel, Intel Phi, nVidia, OpenCL, Package, Performance, Portability, Tesla K20

March 24, 2013 by hgpu

Performance study of filtered back-projection algorithms implemented on GPUs

Bruno Faria

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Tags: Computed tomography, CT, CUDA, Image processing, Medicine, nVidia, nVidia GeForce GTX 580, Performance, Tomography

March 21, 2013 by hgpu

General Purpose Computing on Low-Power Embedded GPUs: Has It Come of Age?

Arian Maghazeh, Unmesh D. Bordoloi, Petru Eles, Zebo Peng

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Tags: ARM, Computer science, nVidia, OpenCL, Performance, Tesla M2050

March 20, 2013 by hgpu

Convergence and Scalarization for Data-Parallel Architectures

Yunsup Lee, Ronny Krashinsky, Vinod Grover, Stephen W. Keckler, Krste Asanovic

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, Data parallelism, nVidia, Performance

March 15, 2013 by hgpu

Input-Aware Auto-Tuning for Directive-based GPU Programming

Alberto Magni, Dominik Grewe, Nick Johnson

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Tags: Computer science, nVidia, OpenACC, Performance, Programming techniques, Tesla C2050

March 15, 2013 by hgpu

Just-in-time Acceleration of JavaScript

Uday Pitambare, Arun Chauhan, Saurabh Malviya

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Tags: Computer science, Java, nVidia, Performance, PTX, Tesla C1060

March 12, 2013 by hgpu

Performance Traps in OpenCL for CPUs

Jie Shen, Jianbin Fang, Henk Sips, Ana Lucia Varbanescu

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Tags: Computer science, Memory model, OpenCL, Performance

March 12, 2013 by hgpu

Morph Algorithms on GPUs

Rupesh Nasre, Martin Burtscher, Keshav Pingali

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

March 12, 2013 by hgpu

Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures

Jin Wangy, Norman Rubinz, Haicheng Wuy, Sudhakar Yalamanchiliy

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Tags: Algorithms, ATI, ATI Radeon HD 7660, ATI Radeon HD 7950, Computer science, Heterogeneous systems, OpenCL, Performance, Vehicular traffic

March 5, 2013 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

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high performance computing on graphics processing units: hgpu.org

Improving GPU Performance Prediction with Data Transfer Modeling

Optimising Purely Functional GPU Programs

Warp Size Impact in GPUs: Large or Small?

How to Benefit from AMD, Intel and Nvidia Accelerator Technologies in Scilab

Performance study of filtered back-projection algorithms implemented on GPUs

General Purpose Computing on Low-Power Embedded GPUs: Has It Come of Age?

Convergence and Scalarization for Data-Parallel Architectures

Input-Aware Auto-Tuning for Directive-based GPU Programming

Just-in-time Acceleration of JavaScript

Performance Traps in OpenCL for CPUs

Morph Algorithms on GPUs

Accelerating Simulation of Agent-Based Models on Heterogeneous Architectures

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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