high performance computing on graphics processing units: hgpu.org

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Soft Error Resilient QR Factorization for Hybrid System

Peng Du, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Fault tolerance, Hybrid computing, Linear Algebra, nVidia, Tesla C2050, Tesla M2070

November 20, 2011 by hgpu

Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

Kwang-Ting (Tim) Cheng, Yi-Chu Wang

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Tags: ARM, Computer science, Computer vision, FFT, Image recognition, nVidia, nVidia Tegra

November 20, 2011 by hgpu

Autotuning GEMMs for Fermi

Jakub Kurzak, Stanimire Tomov, Jack Dongarra

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Tags: Benchmarking, BLAS, Computer science, CUDA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Package, Tesla S2050

November 20, 2011 by hgpu

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Fengguang Song, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, BLAS, Computer science, CUDA, Factorization, Heterogeneous systems, nVidia, Tesla M2070

November 20, 2011 by hgpu

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Rajib Nath, Stanimire Tomov, Tingxing "Tim" Dong, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Package, Performance, Tesla C2050

November 20, 2011 by hgpu

Using the High Productivity Language Chapel to Target GPGPU Architectures

Albert Sidelnik, Maria J. Garzaran, David Padua, Bradford L. Chamberlain

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 280, Optimization

November 19, 2011 by hgpu

Anisotropic mesh coarsening and refinement on GPU architecture

Matthew Potter

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Tags: Algorithms, CUDA, Differential equations, Finite volume method, Fluid dynamics, nVidia, Partial differential equations, PDEs, Tesla M2050, Thesis

November 19, 2011 by hgpu

Exploiting concurrent kernel execution on graphic processing units

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla C2070

November 19, 2011 by hgpu

Towards Efficient GPU Sharing on Multicore Processors

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Programming techniques, Tesla C1060, Tesla C2070

November 19, 2011 by hgpu

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

Jonathan Passerat-Palmbach, Claude Mazel, David R.C. Hill

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Tags: Computer science, CUDA, nVidia, Pseudo-random number generators, Stochastic simulation, Tesla C1060

November 19, 2011 by hgpu

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Matthieu Ospici, Dimitri Komatitsch, Jean-Francois Mehaut, Thierry Deutsch

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Tags: Benchmarking, Computer science, CUDA, Hybrid computing, nVidia, Tesla T10

November 19, 2011 by hgpu

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 560 Ti, Performance, Tesla C2070

November 19, 2011 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Soft Error Resilient QR Factorization for Hybrid System

Using mobile GPU for general-purpose computing – a case study of face recognition on smartphones

Autotuning GEMMs for Fermi

Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures

Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs

Using the High Productivity Language Chapel to Target GPGPU Architectures

Anisotropic mesh coarsening and refinement on GPU architecture

Exploiting concurrent kernel execution on graphic processing units

Towards Efficient GPU Sharing on Multicore Processors

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

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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