high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

OpenCL Accelerated Multi-GPU Cone-Beam Reconstruction

Marc Kaseberg, Steffen Melnik, Erwin Keeve

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Tags: Algorithms, ATI, ATI Radeon HD 5770, ATI Radeon HD 6970, Computed tomography, CT, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 680, nVidia Quadro 600, OpenCL, Tomography

December 17, 2013 by hgpu

Data Transfer Matters for GPU Computing

Yusuke Fujii, Takuya Azumi, Nobuhiko Nishio, Shinpei Kato, Masato Edahiro

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Package, Performance

December 13, 2013 by hgpu

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

Hector Ortega-Arranz, Yuri Torres, Diego R. Llanos, Arturo Gonzalez-Escribano

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, OpenMP

December 13, 2013 by hgpu

Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Abdullah Gharaibeh, Elizeu Santos-Neto, Lauro Beltrao Costa, Matei Ripeanu

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Tags: Algorithms, Computer science, CUDA, Graph theory, Heterogeneous systems, Hybrid computing, nVidia, Package

December 12, 2013 by hgpu

Runtime Support toward Transparent Memory Access in GPU-accelerated Heterogeneous Systems

Feng Ji

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Tags: Computer science, Hadoop, Heterogeneous systems, MapReduce, MPI, nVidia, nVidia GeForce GTX 280, Tesla M2070, Thesis

December 11, 2013 by hgpu

A Distributed Data Mining Framework Accelerated with Graphics Processing Units

Nam-Luc Tran, Quentin Dugauthier, Sabri Skhiri

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Tags: Clustering, Computer science, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, Tesla K20

December 6, 2013 by hgpu

GPU-based Multi-stream Analyzer on Application Layer for Service-oriented Router

Kazumasa Ikeuchi, Janaka Wijekoon, Shinichi Ishida, Hiroaki Nishi

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, Software router, String matching

December 4, 2013 by hgpu

Exploiting Heterogeneous Systems: Keccak on OpenCL

Allan Mariano de Souza, Fabio Dacencio Pereira, Edward David Moreno

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 295, OpenCL, Security

December 4, 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

Autotuning of Pattern Runtimes for Accelerated Parallel Systems

Enes Bajrovic, Siegfried Benkner, Jiri Dokulil, Martin Sandrieser

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Tesla C1060, Tesla C2050

November 27, 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

Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

Wasuwee Sodsong, Jingun Hong, Seongwook Chung, Shin-Dug Kim, Bernd Burgstaller

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Tags: Compression, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GT 430, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 680, OpenCL, Performance

November 23, 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

OpenCL Accelerated Multi-GPU Cone-Beam Reconstruction

Data Transfer Matters for GPU Computing

TuCCompi: A Multi-Layer Programing Model for Heterogeneous Systems with Auto-Tuning Capabilities

Efficient Large-Scale Graph Processing on Hybrid CPU and GPU Systems

Runtime Support toward Transparent Memory Access in GPU-accelerated Heterogeneous Systems

A Distributed Data Mining Framework Accelerated with Graphics Processing Units

GPU-based Multi-stream Analyzer on Application Layer for Service-oriented Router

Exploiting Heterogeneous Systems: Keccak on OpenCL

Benchmarking Parallel Performance on Many-Core Processors

Autotuning of Pattern Runtimes for Accelerated Parallel Systems

Regression Modelling of Power Consumption for Heterogeneous Processors

Dynamic Partitioning-based JPEG Decompression on Heterogeneous Multicore Architectures

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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