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Flexible Hardware Mapping for Finite Element Simulations on Hybrid CPU / GPU Clusters

Aaron Becker, Isaac Dooley, Laxmikant V. Kale

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Tags: Computer science, CUDA, Finite element method, GPU cluster, nVidia, Tesla S1070

February 23, 2011 by hgpu

Accelerating the ANSYS Direct Sparse Solver with GPUs

Geraud P. Krawezik, Gene Poole

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Tags: Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, Sparse direct solvers, Tesla C1060

February 22, 2011 by hgpu

Production Floating Point Applications on FPGAs

Martin C. Herbordt, Bharat Sukhwani, Matt Chiu, Md. Ashfaq Khan

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Tags: Chemistry, Computer science, CUDA, FPGA, Molecular dynamics, Molecular modeling, nVidia, Tesla C1060

February 22, 2011 by hgpu

Real-Time Stereo on GPGPU using Progressive Multi-Resolution Adaptive Windows

Yong Zhao, Gabriel Taubin

Tags: Computer science, Computer vision

February 22, 2011 by hgpu

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms

Hartwig Anzt, Bjorn Rocker, Vincent Heuveline

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Tags: Computer science, Energy-efficient computing, Linear Algebra, Mixed precision, nVidia, Tesla C1060, Tesla S1070

February 22, 2011 by hgpu

Performance analysis and optimization of three-dimensional FDTD on GPU using roofline model

Ki-Hwan Kim, KyoungHo Kim, Q-Han Park

Tags: Computer science, CUDA, FDTD, Finite-difference time-domain, nVidia, nVidia GeForce GTX 480

February 22, 2011 by hgpu

Data Structures and Transformations for Physically Based Simulation on a GPU

Perhaad Mistry, Dana Schaa, Byunghyun Jang, David Kaeli, Albert Dvornik, Dwight Meglan

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 285, Physics-based simulation

February 22, 2011 by hgpu

Parallel power flow solutions using a biconjugate gradient algorithm and a Newton method: A GPU-based approach

Norberto Garcia

Tags: Computational Engineering, Computer science, Conjugate gradient solver

February 22, 2011 by hgpu

Software-Based ECC for GPUs

Naoya Maruyama, Akira Nukada, Satoshi Matsuoka

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Tags: Computer science, CUDA, Error recovery, FFT, N-body simulation, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce GTX 285, Tesla S1070

February 21, 2011 by hgpu

An Automated Approach for SIMD Kernel Generation for GPU based Software Acceleration

Kanupriya Gulati and Sunil P Khatri

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 280

February 21, 2011 by hgpu

A massively parallel framework using P systems and GPUs

Jose M. Cecilia, Gines D. Guerrero, Jose M. Garcia, Miguel A. Martinez-del-Amor, Ignacio Perez-Hurtado, Mario J. Perez-Jimenez

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Tags: Biology, Computer science, CUDA, nVidia, P systems, Tesla C1060

February 21, 2011 by hgpu

GPU Acceleration of Equations Assembly in Finite Elements Method – Preliminary Results

Jiri Filipovic, Igor Peterlik, Jan Fousek

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Tags: Computer science, CUDA, Finite element method, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs

February 21, 2011 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

Flexible Hardware Mapping for Finite Element Simulations on Hybrid CPU / GPU Clusters

Accelerating the ANSYS Direct Sparse Solver with GPUs

Production Floating Point Applications on FPGAs

Real-Time Stereo on GPGPU using Progressive Multi-Resolution Adaptive Windows

Energy efficiency of mixed precision iterative refinement methods using hybrid hardware platforms

Performance analysis and optimization of three-dimensional FDTD on GPU using roofline model

Data Structures and Transformations for Physically Based Simulation on a GPU

Parallel power flow solutions using a biconjugate gradient algorithm and a Newton method: A GPU-based approach

Software-Based ECC for GPUs

An Automated Approach for SIMD Kernel Generation for GPU based Software Acceleration

A massively parallel framework using P systems and GPUs

GPU Acceleration of Equations Assembly in Finite Elements Method – Preliminary Results

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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