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Performance Evaluation of Parallel Count Sort using GPU Computing with CUDA

Neetu Faujdar, SatyaPrakash Ghrera

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Sorting

May 21, 2016 by hgpu

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Oren Segal, Nasibeh Nasiri, Martin Margala

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Tags: Benchmarking, Computer science, FPGA, Heterogeneous systems, nVidia, OpenCL, Tesla K40

May 17, 2016 by hgpu

Improving GPU Performance: Reducing Memory Conflicts and Latency

Gert-Jan van den Braak

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, Matrix multiplication, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 470, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Thesis

May 11, 2016 by hgpu

Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model

Jan Lemeire, Jan G. Cornelis, Laurent Segers

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Tags: Benchmarking, Computer science, Java, nVidia, nVidia GeForce GT 640, nVidia Quadro K620, OpenCL, Package

May 9, 2016 by hgpu

To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures

Feng Zhang, Jidong Zhai, Wenguang Chen, Bingsheng He, Shuhao Zhang

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Tags: Benchmarking, Computer science, OpenCL, Package

April 26, 2016 by hgpu

OpenCL-Based Mobile GPGPU Benchmarking: Methods and Challenges

Rotem Aviv, Guohui Wang

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Tags: Benchmarking, Mobile GPGPU, OpenCL

April 22, 2016 by lanshan317

Dynamic Sparse-Matrix Allocation on GPUs

James King, Thomas Gilray, Robert M. Kirby, Matthew Might

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla K20

April 3, 2016 by hgpu

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

Zhaokui Li, Jianbin Fang, Tao Tang, Xuhao Chen, Cheng Chen, Canqun Yang

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

April 3, 2016 by hgpu

Comparison of Technologies for General-Purpose Computing on Graphics Processing Units

Torbjorn Sorman

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Tags: AMD Radeon R7 260X, ATI, Benchmarking, Computer science, DirectCompute, DirectX, nVidia, nVidia GeForce GTX 670, OpenCL, OpenGL, Performance, Thesis

March 22, 2016 by hgpu

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

Weifeng Liu

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Tags: Algorithms, AMD Radeon R9 290X, ATI, Benchmarking, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan Black, OpenCL, Sparse matrix, Thesis

February 16, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

A Workload Balanced MapReduce Framework on GPU Platforms

Yue Zhang

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Tags: Benchmarking, Computer science, CUDA, MapReduce, nVidia, Performance, Tesla K40, Thesis

January 12, 2016 by hgpu

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

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Performance Evaluation of Parallel Count Sort using GPU Computing with CUDA

A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

Improving GPU Performance: Reducing Memory Conflicts and Latency

Microbenchmarks for GPU characteristics: the occupancy roofline and the pipeline model

To Co-Run, or Not To Co-Run: A Performance Study on Integrated Architectures

OpenCL-Based Mobile GPGPU Benchmarking: Methods and Challenges

Dynamic Sparse-Matrix Allocation on GPUs

Evaluating the Performance Impact of Multiple Streams on the MIC-based Heterogeneous Platform

Comparison of Technologies for General-Purpose Computing on Graphics Processing Units

Parallel and Scalable Sparse Basic Linear Algebra Subprograms

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

A Workload Balanced MapReduce Framework on GPU Platforms

Recent source codes

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

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

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