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Massive Parallelism with GPUs for Centrality Ranking in Complex Networks

Frederico L. Cabral, Carla Osthoff, Rafael Nardes, Daniel Ramos

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Tags: Computer science, CUDA, GPU cluster, Graph theory, MPI, nVidia, Tesla K20

July 6, 2014 by hgpu

The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

Pisit Makpaisit, Putchong Uthayopas

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Tags: Compilers, Computer science, CUDA, GPU cluster, nVidia, OpenACC, Tesla M2050

July 3, 2014 by hgpu

GPU accelerated Hybrid Tree Algorithm for Collision-less N-body Simulations

Tsuyoshi Watanabe, Naohito Nakasato

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Tags: Algorithms, Astrophysics, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, OpenCL, Tesla M2090

June 25, 2014 by hgpu

Synergia CUDA: GPU-accelerated accelerator modeling package

Q. Lu, J. Amundson

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Tags: CUDA, FFT, GPU cluster, MPI, nVidia, Package, Particle-in-cell methods, Physics, Tesla C1070, Tesla K20

June 17, 2014 by hgpu

Scalable Lattice Boltzmann Solvers for CUDA GPU Clusters

Christian Obrecht, Frederic Kuznik, Bernard Tourancheau, Jean-Jacques Roux

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Tags: CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, MPI, nVidia, OpenMPI, Tesla M2070

June 15, 2014 by hgpu

GPPE: a GPU-based Parallel Processing Environment for Large Scale Concurrent Data Streams

Jianwei Cao, Qingkui Chen, Songlin Zhuang

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Tags: Cloud, Clustering, Computer science, CUDA, GPU cluster, H.264/AVC, MPI, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 280, nVidia GeForce GTX 295, nVidia GeForce GTX 480

June 12, 2014 by hgpu

Accelerating the scoring module of mass spectrometry-based peptide identification using GPUs

You Li, Hao Chi, Leihao Xia, Xiaowen Chu

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Tags: Bioinformatics, Biology, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 580, Spectrometry

May 12, 2014 by hgpu

Managing the Topology of Heterogeneous Cluster Nodes with Hardware Locality (hwloc)

Brice Goglin

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, OpenMPI, Package, Xeon Phi

May 7, 2014 by hgpu

Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing

Alexander Agathos, Jun Li, Dana Petcu, Antonio Plaza

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Tags: Algorithms, Computer science, CUDA, GPU cluster, nVidia, Sensing, Tesla M2070

April 29, 2014 by hgpu

Toward optimised skeletons for heterogeneous parallel architecture with performance cost model

Khari A. Armih

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MapReduce, Matrix multiplication, nVidia, nVidia GeForce GT 520, Thesis

April 21, 2014 by hgpu

Multi-GPU cluster wave propagation and OpenGL visualization

Jacob Lundgren, Nils Olofsson

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Tags: Computer science, CUDA, Finite difference, GPU cluster, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 670, OpenGL, Visualization

March 15, 2014 by hgpu

Two-fluid compressible simulations on GPU cluster

Philippe Helluy, Jonathan Jung

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Tags: ATI, ATI Radeon HD 5850, ATI Radeon HD 7970, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 320 M, OpenCL

March 15, 2014 by hgpu

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

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

BoltzGen:Toward Universal Binder Design

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

See all packages

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high performance computing on graphics processing units: hgpu.org

Massive Parallelism with GPUs for Centrality Ranking in Complex Networks

The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

GPU accelerated Hybrid Tree Algorithm for Collision-less N-body Simulations

Synergia CUDA: GPU-accelerated accelerator modeling package

Scalable Lattice Boltzmann Solvers for CUDA GPU Clusters

Accelerating the scoring module of mass spectrometry-based peptide identification using GPUs

Managing the Topology of Heterogeneous Cluster Nodes with Hardware Locality (hwloc)

Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing

Toward optimised skeletons for heterogeneous parallel architecture with performance cost model

Multi-GPU cluster wave propagation and OpenGL visualization

Two-fluid compressible simulations on GPU cluster

Recent source codes

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

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

BoltzGen:Toward Universal Binder Design

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

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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