high performance computing on graphics processing units: hgpu.org

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BIDMach: Large-scale Learning with Zero Memory Allocation

John Canny, Huasha Zhao

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 690

December 25, 2013 by hgpu

Warps and Atomics: Beyond Barrier Synchronization in the Verification of GPU Kernels

Ethel Bardsley, Alastair F. Donaldson

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Package, Python

December 20, 2013 by hgpu

The Comparisons of OpenCL and OpenMP Computing Paradigm

Slo-Li Chu, Chih-Chieh Hsiao

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Tags: ATI, ATI Radeon HD 5850, Benchmarking, Computer science, nVidia GeForce GTX 285, OpenCL, OpenMP, Performance

December 9, 2013 by hgpu

Multithreaded Transposition of Square Matrices with Common Code for Intel Xeon Processors and Intel Xeon Phi Coprocessors

Andrey Vladimirov

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Tags: Algorithms, Benchmarking, Computer science, Intel Phi, Intel Xeon Phi, Linear Algebra

December 3, 2013 by hgpu

SESH framework: A Space Exploration Framework for GPU Application and Hardware Codesign

Joo Hwan Lee, Jiayuan Meng, Hyesoon Kim

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GTX 580, Performance

December 3, 2013 by hgpu

GenBase: A Complex Analytics Genomics Benchmark

Rebecca Taft, Manasi Vartak, Nadathur Rajagopalan Satish, Narayanan Sundaram, Samuel Madden, Michael Stonebraker

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

November 30, 2013 by hgpu

Benchmarking Parallel Performance on Many-Core Processors

Bryant C. Lam, Ajay Barboza, Ravi Agrawal, Alan D. George, Herman Lam

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

November 29, 2013 by hgpu

Regression Modelling of Power Consumption for Heterogeneous Processors

Tahir Diop

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Tags: Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, OpenCL, Tesla M2090, Thesis

November 27, 2013 by hgpu

Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster

Mads R. B. Kristensen, Simon A. F. Lund, Troels Blum, Kenneth Skovhede, Brian Vinter

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Tags: ATI, ATI Radeon HD 7850, Benchmarking, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 680, OpenCL, Python

November 22, 2013 by hgpu

Performance and Power Comparisons Between Fermi and Cypress GPUs

Ying Zhang

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

November 18, 2013 by hgpu

An Empirical Study of Intel Xeon Phi

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

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

October 25, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 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

BIDMach: Large-scale Learning with Zero Memory Allocation

Warps and Atomics: Beyond Barrier Synchronization in the Verification of GPU Kernels

The Comparisons of OpenCL and OpenMP Computing Paradigm

Multithreaded Transposition of Square Matrices with Common Code for Intel Xeon Processors and Intel Xeon Phi Coprocessors

SESH framework: A Space Exploration Framework for GPU Application and Hardware Codesign

GenBase: A Complex Analytics Genomics Benchmark

Benchmarking Parallel Performance on Many-Core Processors

Regression Modelling of Power Consumption for Heterogeneous Processors

Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster

Performance and Power Comparisons Between Fermi and Cypress GPUs

An Empirical Study of Intel Xeon Phi

Understanding and Modeling the Synchronization Cost in the GPU Architecture

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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