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Distributed OpenCL Distributing OpenCL Platform on Network Scale

Baris Eskikaya, D Turgay Altilar

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Tags: ATI, ATI Radeon HD 6370 M, Computer science, Distributed computing, GPU cluster, nVidia, nVidia GeForce GT 240 M, nVidia GeForce GT 520 M, nVidia GeForce GT 525 M, nVidia GeForce GTX 540 M, nVidia GeForce GTX 555 M, OpenCL

July 15, 2012 by hgpu

Optimizing All-to-All and Allgather Communications on GPGPU Clusters

Ashish Kumar Singh

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Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, Tesla C2050, Thesis

July 14, 2012 by hgpu

Runtime Systems and Scheduling Support for High-End CPU-GPU Architectures

Vignesh Trichy Ravi

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

July 10, 2012 by hgpu

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

V.N. Pistulkar, C.A. Uttarwar

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Tags: Computer science, CUDA, GPU cluster, nVidia, PTX, Tesla C1060

July 5, 2012 by hgpu

The Fat-Link Computation On Large GPU Clusters for Lattice QCD

Guochun Shi, Ronald Babich, Michael A. Clark, Balint Joo, Steven Gottlieb, Volodymyr Kindratenko

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Tags: Algorithms, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 480, nVidia Quadro FX 5800, Physics, QCD, Tesla M2070

July 1, 2012 by hgpu

Multi-GPU Island-Based Genetic Algorithm for Solving the Knapsack Problem

Jiri Jaros

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Source codes

Tags: Algorithms, Computer science, CUDA, Genetic programming, GPU cluster, MPI, nVidia, nVidia GeForce GTX 580, Package

June 26, 2012 by hgpu

An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem

Imen Chakroun, Nouredine Melab

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Tags: Computer science, CUDA, GPU cluster, nVidia, Optimization, Tesla S1070

June 25, 2012 by hgpu

Parallel Rendering on Hybrid Multi-GPU Clusters

S. Eilemann, A. Bilgili, M. Abdellah, J. Hernando, M. Makhinya, R. Pajarola, F. Schurmann

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Tags: 3D Graphics and Realism, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 580, OpenGL, Optimization, Rendering, Visualization

June 19, 2012 by hgpu

Fluid Dynamics Simulations on Multi-GPU Systems

Eugenio Rustico

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Tags: CUDA, Fluid dynamics, GPU cluster, nVidia, nVidia GeForce GTX 480, Thesis

June 13, 2012 by hgpu

Range query processing in a multi-GPU environment

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

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Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Databases, GPU cluster, nVidia, OpenMP, Tesla C1060

June 11, 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

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

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

Distributed OpenCL Distributing OpenCL Platform on Network Scale

Optimizing All-to-All and Allgather Communications on GPGPU Clusters

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

The Fat-Link Computation On Large GPU Clusters for Lattice QCD

Multi-GPU Island-Based Genetic Algorithm for Solving the Knapsack Problem

An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem

Parallel Rendering on Hybrid Multi-GPU Clusters

Fluid Dynamics Simulations on Multi-GPU Systems

Range query processing in a multi-GPU environment

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

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

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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