high performance computing on graphics processing units: hgpu.org

hgpu.org » Python

Caffe: Convolutional Architecture for Fast Feature Embedding

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell

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Tags: Cloud, Computer science, CUDA, Neural networks, nVidia, Package, Pattern recognition, Python

August 23, 2014 by hgpu

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

Gordon Inggs, David Thomas, Wayne Luk

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Tags: Computational finance, Finance, FPGA, Heterogeneous systems, OpenCL, Option pricing, Package, Python

August 23, 2014 by hgpu

High Level High Performance Computing for Multitask Learning of Time-varying Models

Marco Signoretto, Emanuele Frandi, Zahra Karevan, Johan A. K. Suykens

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Tags: Algorithms, Computer science, CUDA, nVidia, Python

August 18, 2014 by hgpu

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

Hyungsuk Choi, Woohyuk Choi, Tran Minh Quan, David G. C. Hildebrand, Hanspeter Pfister, Senior Member, Won-Ki Jeong

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Tags: CUDA, GPU cluster, Heterogeneous systems, Image processing, nVidia, Python, Rendering, Tesla M2090, Visualization

August 9, 2014 by hgpu

Dark Sky Simulations: Early Data Release

Samuel W. Skillman, Michael S. Warren, Matthew J. Turk, Risa H. Wechsler, Daniel E. Holz, P. M. Sutter

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Package, Python, Tesla K20

July 22, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

SimCommSys: taking the errors out of error-correcting code simulations

Johann A. Briffa, Stephan Wesemeyer

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Tags: Computer science, CUDA, Error recovery, nVidia, Package, Python

July 6, 2014 by hgpu

Loo.py: transformation-based code generation for GPUs and CPUs

Andreas Klockner

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Tags: ATI, ATI Radeon HD 7990, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, Programming Languages, PyOpenCL, Python

June 2, 2014 by hgpu

Engineering a static verification tool for GPU kernels

Ethel Bardsley, Adam Betts, Nathan Chong, Peter Collingbourne, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema, Daniel Liew, Shaz Qadee

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Tags: Computer science, CUDA, nVidia, OpenCL, Package, Python

May 25, 2014 by hgpu

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Andrea Manconi, Alessandro Orro, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 480, Package, Python, Sequence alignment, Tesla K10, Tesla K20

May 25, 2014 by hgpu

Efficient Energyminimization in Finite-Difference Micromagnetics: Speeding up Hysteresis Computations

Claas Abert, Gregor Wautischer, Florian Bruckner, Armin Satz, Dieter Suess

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Tags: Computational Physics, CUDA, Electrodynamics, Finite difference, nVidia, Package, Physics, Python

May 14, 2014 by hgpu

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

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Tags: Bayesian, Biology, CUDA, Machine learning, Neural networks, nVidia, Package, Python

April 17, 2014 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

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high performance computing on graphics processing units: hgpu.org

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

High Level High Performance Computing for Multitask Learning of Time-varying Models

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

Dark Sky Simulations: Early Data Release

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

SimCommSys: taking the errors out of error-correcting code simulations

Loo.py: transformation-based code generation for GPUs and CPUs

Engineering a static verification tool for GPU kernels

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

Efficient Energyminimization in Finite-Difference Micromagnetics: Speeding up Hysteresis Computations

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

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

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