high performance computing on graphics processing units: hgpu.org

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DMA-Assisted, Intranode Communication in GPU Accelerated Systems

Feng Ji, Ashwin M. Aji, James Dinan, Darius Buntinas, Pavan Balaji, Rajeev Thakur, Wu-Chun Feng, Xiaosong Ma

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, Tesla M2070

June 6, 2012 by hgpu

Classical Mechanical Hard-Core Particles Simulated in a Rigid Enclosure using Multi-GPU Systems

D.P.Playne, K.A. Hawick

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Tags: Computer science, CUDA, GPU cluster, N-body simulation, nVidia, Tesla C2070

June 6, 2012 by hgpu

The Tradeoffs of Fused Memory Hierarchies in Heterogeneous Computing Architectures

Kyle Spafford, Jeremy S. Meredith, Seyong Lee, Dong Li, Philip C. Roth, Jeffrey S. Vetter

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Tags: APU, ATI, ATI FirePro V8800, ATI Radeon HD 5670, Computer science, Heterogeneous systems, Memory, OpenCL, Performance

June 6, 2012 by hgpu

A new parallelisation technique for heterogeneous CPUs

Youssef Omran Gdura

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Tags: Benchmarking, Code generation, Computer science, Heterogeneous systems, Programming techniques, Thesis

June 5, 2012 by hgpu

Using visualization to reveal weak cryptosystems

Eirik Eggesbo Ottesen

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Tags: Computer science, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 560 Ti, nVidia GeForce GTX 570, nVidia GeForce GTX 680, Security, Thesis, Visualization

June 5, 2012 by hgpu

Hierarchical Partitioning Algorithm for Scientific Computing on Highly Heterogeneous CPU + GPU Clusters

David Clarke, Aleksandar Ilic, Alexey Lastovetsky, Leonel Sousa

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Tags: Computer science, CPU cluster, GPU cluster, Heterogeneous systems, Matrix multiplication, nVidia, Tesla C2050, Tesla T10

June 5, 2012 by hgpu

Shortening design time through multiplatform simulations with a portable OpenCL golden-model: the LDPC decoder case

G. Falcao, M. Owaida, D. Novo, M. Purnaprajna, N. Bellas, C.D. Antonopoulos, G. Karakonstantis, A. Burg, P. Ienne

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Tags: ATI, ATI Radeon HD 5870, Computer science, Design space exploration, FPGA, OpenCL, Parity-check codes

June 5, 2012 by hgpu

Platform 2012, a Many-Core Computing Accelerator for Embedded SoCs: Performance Evaluation of Visual Analytics Applications

Diego Melpignano, Luca Benini, Eric Flamand, Bruno Jego, Thierry Lepley, Germain Haugou, Fabien Clermidy, Denis Dutoit

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Tags: ARM, Computer science, Computer vision, nVidia, nVidia GeForce GTX 280, OpenCL, Performance

June 4, 2012 by hgpu

A Compiler and Runtime for Heterogeneous Computing

Joshua Auerbach, David F. Bacon, Ioana Burcea, Perry Cheng, Stephen J. Fink, Rodric Rabbah, Sunil Shukla

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Tags: Computer science, FPGA, Heterogeneous systems, Java, nVidia, nVidia GeForce GTX 580, OpenCL, Programming Languages

June 4, 2012 by hgpu

Pipelining the Fast Multipole Method over a Runtime System

Emmanuel Agullo, Beranger Bramas, Olivier Coulaud, Eric Darve, Matthias Messner, Takahashi Toru

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Tags: Algorithms, Computer science, Fast multipole method, Heterogeneous systems, nVidia, Physics, Tesla M2070, Tesla M2090

June 4, 2012 by hgpu

GPU Join Processing Revisited

Tim Kaldewey, Guy Lohman, Rene Mueller, Peter Volk

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 580

June 3, 2012 by hgpu

Parallel Triangular Solvers on GPU

Hui Liu, Song Yu, Zhangxin Chen

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Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050, Tesla C2070

June 3, 2012 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

DMA-Assisted, Intranode Communication in GPU Accelerated Systems

Classical Mechanical Hard-Core Particles Simulated in a Rigid Enclosure using Multi-GPU Systems

The Tradeoffs of Fused Memory Hierarchies in Heterogeneous Computing Architectures

A new parallelisation technique for heterogeneous CPUs

Using visualization to reveal weak cryptosystems

Hierarchical Partitioning Algorithm for Scientific Computing on Highly Heterogeneous CPU + GPU Clusters

Shortening design time through multiplatform simulations with a portable OpenCL golden-model: the LDPC decoder case

Platform 2012, a Many-Core Computing Accelerator for Embedded SoCs: Performance Evaluation of Visual Analytics Applications

A Compiler and Runtime for Heterogeneous Computing

Pipelining the Fast Multipole Method over a Runtime System

GPU Join Processing Revisited

Parallel Triangular Solvers on GPU

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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