high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

CudaRF: A CUDA-based Implementation of Random Forests

Hakan Grahn, Niklas Lavesson, Mikael Hellborg Lapajne, Daniel Slat

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Machine learning, nVidia, nVidia GeForce GT 220

February 9, 2012 by hgpu

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Jun Igarashi, Osamu Shouno, Tomoki Fukai, Hiroshi Tsujino

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Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 280, Visualization

February 9, 2012 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 8, 2012 by hgpu

The PEPPHER Approach to Programmability and Performance Portability for Heterogeneous many-core Architectures

Siegfried Benkner, Sabri Pllana, Jesper Larsson Traff, Philippas Tsigas, Andrew Richards, Raymond Namyst, Beverly Bachmayer, Christoph Kessler, David Moloney, Peter Sanders

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Optimization, Performance, Tesla C1060

February 8, 2012 by hgpu

Acceleration of a Locally Tuned Sine Non Linear Video Enhancement Algorithm on GPGPU

Julian Daniel John

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Tags: Algorithms, Computer science, Computer vision, CUDA, nVidia, Tesla C2070, Thesis

February 8, 2012 by hgpu

Symbolic Testing of OpenCL Code

Peter Collingbourne, Cristian Cadar, Paul H. J. Kelly

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Tags: Benchmarking, Code generation, Computer science, nVidia, OpenCL, Package, PTX, Tesla C1060

February 8, 2012 by hgpu

Verifiable Computation with Massively Parallel Interactive Proofs

Justin Thaler, Mike Roberts, Michael Mitzenmacher, Hanspeter Pfister

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Tags: Benchmarking, Cloud, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 480, Package, Security

February 8, 2012 by hgpu

GMP implementation on CUDA – A Backward Compatible Design With Performance Tuning

Hao Jun Liu, Chu Tong

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Tags: Algorithms, Computer science, CUDA, Elementary functions, nVidia, nVidia GeForce GTX 280, Performance, Security

February 7, 2012 by hgpu

Enabling Traceability in MDE to Improve Performance of GPU Applications

Antonio Wendell De Oliveira Rodrigues, Vincent Aranega, Anne Etien, Frederic Guyomarc'H, Jean-Luc Dekeyser

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Tags: Algorithms, Computer science, nVidia, OpenCL, Performance, Programming techniques, Tesla S1070

February 7, 2012 by hgpu

Seismic imaging based on spectral differentiation matrix and GPU implementation

Jing-Bo Chen, Guo-Feng Liu, Hong Liu

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Tags: Algorithms, CUDA, Earth and Space Sciences, nVidia, Seismology, Tesla C1060, Wave equation

February 7, 2012 by hgpu

Stochastic Differential Equations simulation using GPU

Narayana Darapaneni, Prakalp Somawanshi, Meghana Joshi

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Tags: Algorithms, Computer science, CUDA, Differential equations, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 275, Pseudo-random number generators, Stochastic simulation

February 7, 2012 by hgpu

Aggregate Gaze Visualization with Real-time Heatmaps

Andrew T. Duchowski, Margaux M. Price, Miriah Meyer, Pilar Orero

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Tags: Algorithms, Computer science, GLSL, Image generation, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia GeForce 9400 M, OpenGL, Rendering, Visualization

February 7, 2012 by hgpu

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

EnergyUCB-Bandit

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

CudaRF: A CUDA-based Implementation of Random Forests

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

The PEPPHER Approach to Programmability and Performance Portability for Heterogeneous many-core Architectures

Acceleration of a Locally Tuned Sine Non Linear Video Enhancement Algorithm on GPGPU

Symbolic Testing of OpenCL Code

GMP implementation on CUDA – A Backward Compatible Design With Performance Tuning

Enabling Traceability in MDE to Improve Performance of GPU Applications

Seismic imaging based on spectral differentiation matrix and GPU implementation

Stochastic Differential Equations simulation using GPU

Aggregate Gaze Visualization with Real-time Heatmaps

Recent source codes

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

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

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

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

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

EnergyUCB-Bandit

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