high performance computing on graphics processing units: hgpu.org

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Algorithm level power efficiency optimization for CPU-GPU processing element in data intensive SIMD/SPMD computing

Da Q. Ren

Tags: Computer science, CUDA, Energy-efficient computing, nVidia

November 22, 2010 by hgpu

Efficient simulation of agent-based models on multi-GPU and multi-core clusters

Brandon G. Aaby, Kalyan S. Perumalla, Sudip K. Seal

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, OpenMPI, Performance

November 22, 2010 by hgpu

GPU accelerated tensor contractions in the plaquette renormalization scheme

J. F. Yu, H. C. Hsiao, Ying-Jer Kao

Tags: CUDA, Fluid dynamics, Heisenberg model, nVidia

November 22, 2010 by hgpu

GPU-accelerated phase-field simulation of dendritic solidification in a binary alloy

Akinori Yamanaka, Takayuki Aoki, Satoi Ogawa, Tomohiro Takaki

Tags: Alloys, CUDA, nVidia, Physics, Solidification, Tesla C1060

November 22, 2010 by hgpu

An Efficient Implementation of GPU Virtualization in High Performance Clusters

Jose Duato, Francisco Igual, Rafael Mayo, Antonio Pena, Enrique Quintana-Orti, Federico Silla

Tags: Computer science, CUDA, Grid, nVidia, Virtualization

November 22, 2010 by hgpu

Breaking ECC2K-130

Daniel V. Bailey, Lejla Batina, Daniel J. Bernstein, Peter Birkner, Joppe W. Bos, Hsieh-Chung Chen, Chen-Mou Cheng, Gauthier van Damme, Giacomo de Meulenaer, Luis Julian Dominguez Perez, Junfeng Fan, Tim Guneysu, Frank Gurkaynak, Thorsten Kleinjung, Tanja Lange, Nele Mentens, Ruben Niederhagen, Christof Paar, Francesco Regazzoni, Peter Schwabe, Leif Uhsadel, Anthony Van Herrewege, Bo-Yin Yang

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Security

November 21, 2010 by hgpu

A high performance agent based modelling framework on graphics card hardware with CUDA

Paul Richmond, Simon Coakley, Daniela M. Romano

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2

November 21, 2010 by hgpu

Large-scale deep unsupervised learning using graphics processors

Rajat Raina, Anand Madhavan, Andrew Y. Ng

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GTX 280

November 21, 2010 by hgpu

Quantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation

Ivan S. Ufimtsev, Todd J. Martinez

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Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280

November 21, 2010 by hgpu

Density-based clustering using graphics processors

Christian Bohm, Robert Noll, Claudia Plant, Bianca Wackersreuther

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Tags: Computer science, CUDA, Data mining, Databases, nVidia, nVidia GeForce GTX 280

November 21, 2010 by hgpu

Parallel latent semantic analysis using a graphics processing unit

Joseph M. Cavanagh, Thomas E. Potok, Xiaohui Cui

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Text mining

November 21, 2010 by hgpu

Using Graphics Processors for High-Performance Computation and Visualization of Plasma Turbulence

George Stantchev, Derek Juba, William Dorland, Amitabh Varshney

Tags: CUDA, nVidia, Physics, Plasma physics

November 21, 2010 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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