high performance computing on graphics processing units: hgpu.org

hgpu.org » Python

Particle Simulation on a GPU with PyCUDA

Min Ragan-Kelley

View

Tags: CUDA, nVidia, nVidia GeForce GTX 260, Particle simulation, Particle-in-cell methods, Physics, Plasma physics, Python, Tesla C1060

December 10, 2011 by hgpu

Playdoh: A lightweight Python library for distributed computing and optimisation

Cyrille Rossant, Bertrand Fontaine, Dan F.M. Goodman

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Differential equations, Distributed computing, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Package, Partial differential equations, PDEs, Python

December 10, 2011 by hgpu

Scalable Query Evaluation in Relational Databases

Rasmus Resen Amossen

View

Download (PDF)

Tags: Algorithms, Computer science, Databases, Matrix multiplication, nVidia, nVidia GeForce GTX 285, OpenCL, Optimization, Python, Thesis

December 5, 2011 by hgpu

A New Compilation Path: From Python/NumPy to OpenCL

Xunhao Li, Rahul Garg, Jose Nelson Amaral

View

Download (PDF)

Tags: ATI, ATI Radeon HD 5850, Benchmarking, Code generation, Compilers, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, Python

November 26, 2011 by hgpu

Computer Simulation of Dark Matter Effects on Galaxy Rotation

Cole Kendrick

View

Download (PDF)

Tags: Astrophysics, ATI, ATI Radeon HD 4870, Gravitation, N-body simulation, Nearest neighbour, nVidia, nVidia GeForce GT 210 M, OpenCL, Python

November 7, 2011 by hgpu

Fast computation of scattering maps of nanostructures using graphical processing units

Vincent Favre-Nicolin, Johann Coraux, Marie-Ingrid Richard, Hubert Renevier

View

Download (PDF)

Source codes

Tags: Condensed matter, CUDA, Materials Science, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce GTX 295, Package, Python

November 1, 2011 by hgpu

Fast Speaker Diarization Using a High-Level Scripting Language

Ekaterina Gonina, Gerald Friedland, Henry Cook, Kurt Keutzer

View

Download (PDF)

Tags: Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Python, Speech recognition

October 31, 2011 by hgpu

Jit4OpenCL: a compiler from Python to OpenCL

Xunhao Li

View

Download (PDF)

Tags: ATI, ATI Radeon HD 5850, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, OpenCL, Python, Thesis

October 29, 2011 by hgpu

gpustats: GPU Library for Statistical Computing in Python

Andrew Cron, Wes McKinney

View

Download (PDF)

Source codes

Tags: Algorithms, Bayesian, Code generation, CUDA, Mathematics, nVidia, nVidia GeForce GTS 250, OpenCL, Package, Probability, Python, Statistics

October 13, 2011 by hgpu

Python for Development of OpenMP and CUDA Kernels for Multidimensional Data

Zane W. Bell, Greg G. Davidson, Ed D'Azevedo, Thomas M. Evans, Wayne Joubert, John K. Munro, Jr., Dilip R. Patlolla, Bogdan Vacaliuc

View

Download (PDF)

Source codes

Tags: Boltzmann equation, CUDA, Heterogeneous systems, nVidia, Package, Physics, Presentation, Python, Tesla M2070

October 6, 2011 by hgpu

Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

Paul Richmond, Lars Buesing, Michele Giugliano, Eleni Vasilaki

View

Download (PDF)

Source codes

Tags: Biology, CUDA, Neuroscience, nVidia, nVidia GeForce GTX 480, Package, Python

October 5, 2011 by hgpu

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming

Andreas Klockner, Timothy Warburton, Jan S. Hesthaven

View

Download (PDF)

Tags: CUDA, Differential equations, Mathematics, nVidia, nVidia GeForce GTX 280, Python, Tesla T10

October 5, 2011 by hgpu

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

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

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

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Particle Simulation on a GPU with PyCUDA

Playdoh: A lightweight Python library for distributed computing and optimisation

Scalable Query Evaluation in Relational Databases

A New Compilation Path: From Python/NumPy to OpenCL

Computer Simulation of Dark Matter Effects on Galaxy Rotation

Fast computation of scattering maps of nanostructures using graphical processing units

Fast Speaker Diarization Using a High-Level Scripting Language

Jit4OpenCL: a compiler from Python to OpenCL

gpustats: GPU Library for Statistical Computing in Python

Python for Development of OpenMP and CUDA Kernels for Multidimensional Data

Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming

Recent source codes

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

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

hls4ml: Machine learning on FPGAs using HLS

Most viewed papers (last 30 days)