high performance computing on graphics processing units: hgpu.org

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HAccRG: Hardware-Accelerated Data Race Detection in GPUs

Anup Holey, Vineeth Mekkat, Antonia Zhai

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia Quadro FX 5800, Performance

July 19, 2013 by hgpu

Benchmarking Intel Xeon Phi to Guide Kernel Design

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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Tags: Benchmarking, Computer science, Intel, Intel Phi, Performance

July 14, 2013 by hgpu

Identifying the Key Features of Intel Xeon Phi: A Comparative Approach

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: Computer science, Intel, Intel Phi, Performance

July 13, 2013 by hgpu

SIMD Divergence Optimization through Intra-Warp Compaction

Aniruddha S. Vaidya, Anahita Shayesteh, Dong Hyuk Woo, Roy Saharoy, Mani Azimi

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Tags: Computer science, Hardware Architecture, OpenCL, OpenGL, Performance

July 12, 2013 by hgpu

ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

Nouredine Melab, The van Luong, Karima Boufaras, El-Ghazali Talbi

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Source codes

Tags: Computer science, CUDA, Metaheuristics, nVidia, nVidia GeForce GTX 480, Package, Performance, Search

July 8, 2013 by hgpu

Optimize Overall System Performance Through Workload Sequencing for GPUs Data Offloading

Wei Liu, Jian-Jia Chen, Qingxu Deng, Tei-Wei Kuo, Xue Liu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 630 M, Performance

July 5, 2013 by hgpu

Preliminary Experiences with the Uintah Framework on Intel Xeon Phi and Stampede

Qingyu Meng, Alan Humphrey, John Schmidt, Martin Berzins

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Tags: Computer science, Heterogeneous systems, Intel, Intel Phi, Performance

July 2, 2013 by hgpu

GPU-CC: a Reconfigurable GPU Architecture with Communicating Cores

Gert-Jan van den Braak, Henk Corporaal

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 470, Performance

June 28, 2013 by hgpu

A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels

Paul Rosen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Wavelet

June 24, 2013 by hgpu

Provably Efficient GPU Algorithms

Nodari Sitchinava, Volker Weichert

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Performance, Sorting

June 24, 2013 by hgpu

Exploring GPGPUs Workload Characteristics and Power Consumption

Sohan Lal, Jan Lucas, Mauricio Alvarez-Mesa, Ahmed Elhossini, Ben Juurlink

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Tags: Computer science, CUDA, Energy-efficient computing, GPGPU-sim, nVidia, nVidia GeForce GTX 580, Performance

June 22, 2013 by hgpu

Auto-Tunning of Data Communication on Heterogeneous Systems

Marc Jorda, Ivan Tanasic, Javier Cabezas, Lluis Vilanova, Isaac Gelado, Nacho Navarro

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2050, Tesla C2070, Tesla M2090

June 17, 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

HAccRG: Hardware-Accelerated Data Race Detection in GPUs

Benchmarking Intel Xeon Phi to Guide Kernel Design

Identifying the Key Features of Intel Xeon Phi: A Comparative Approach

SIMD Divergence Optimization through Intra-Warp Compaction

ParadisEO-MO-GPU: a Framework for Parallel GPU-based Local Search Metaheuristics

Optimize Overall System Performance Through Workload Sequencing for GPUs Data Offloading

Preliminary Experiences with the Uintah Framework on Intel Xeon Phi and Stampede

GPU-CC: a Reconfigurable GPU Architecture with Communicating Cores

A Visual Approach to Investigating Shared and Global Memory Behavior of CUDA Kernels

Provably Efficient GPU Algorithms

Exploring GPGPUs Workload Characteristics and Power Consumption

Auto-Tunning of Data Communication on Heterogeneous Systems

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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