high performance computing on graphics processing units: hgpu.org

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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

SqueezCL: Squeezing OpenCL Kernels for Approximate Computing on Contemporary GPUs

Atieh Lotf, Abbas Rahimi, Hadi Esmaeilzadeh, Rajesh K. Gupta

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, OpenCL

December 23, 2015 by hgpu

Challenges Adapting CUDA PIC Codes to multiple GPUs

Olav Emil Eiksund

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Tags: Benchmarking, CUDA, Electrostatics, FFT, nVidia, nVidia GeForce GTX 980, OpenMP, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Tesla K20, Thesis

December 19, 2015 by hgpu

GRATER: An Approximation Workflow for Exploiting Data-Level Parallelism in FPGA Acceleration

Atieh Lotfi, Abbas Rahimi, Amir Yazdanbakhsh, Hadi Esmaeilzadeh, Rajesh K. Gupta

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Code generation, Computer science, FPGA, OpenCL

December 12, 2015 by hgpu

GEMMbench: a framework for reproducible and collaborative benchmarking of matrix multiplication

Anton Lokhmotov

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Tags: ARM, Benchmarking, Computer science, Linear Algebra, Mathematical Software, Matrix multiplication, OpenCL, Package, Performance

November 13, 2015 by hgpu

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

Hector Dearman

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Tags: Benchmarking, Computer science, CUDA, Differential equations, FEM, Finite element method, nVidia, nVidia GRID K520, OpenCL, Partial differential equations, PDEs, Thesis

November 3, 2015 by hgpu

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K40

October 22, 2015 by hgpu

Comparison of Thread Execution Methods for GPU-oriented OpenCL Programs on Multicore Processors

Naohisa Hojo, Ittetsu Taniguchi, Hiroyuki Tomiyama

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Tags: Benchmarking, Computer science, OpenCL, Performance

October 16, 2015 by hgpu

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

Felix Weninger, Johannes Bergmann, Bjorn Schuller

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Tags: Benchmarking, Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 560, Package, Speech recognition

October 8, 2015 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

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

SqueezCL: Squeezing OpenCL Kernels for Approximate Computing on Contemporary GPUs

Challenges Adapting CUDA PIC Codes to multiple GPUs

GRATER: An Approximation Workflow for Exploiting Data-Level Parallelism in FPGA Acceleration

GEMMbench: a framework for reproducible and collaborative benchmarking of matrix multiplication

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Comparison of Thread Execution Methods for GPU-oriented OpenCL Programs on Multicore Processors

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

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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