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Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Xavier Andrade, Alan Aspuru-Guzik

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, Condensed matter, Data parallelism, nVidia, OpenCL, Package, Physics, Tesla K20

June 14, 2013 by hgpu

Job Parallelism using Graphical Processing Unit individual Multi-Processors and Highly Localised Memory

D. P. Playne, K. A. Hawick

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 680, Tesla M2075

May 29, 2013 by hgpu

An implementation of level set based topology optimization using GPU

D. Herrero, J. Martinez, P. Marti

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Tags: Computational Complexity, CUDA, Data parallelism, FEM, Finite element method, Image processing, nVidia, Tesla C2070

May 19, 2013 by hgpu

Parallel Zonal Summations of Large-Scale Species Occurrence Data on Hybrid CPU-GPU Systems

Jianting Zhang, Simin You

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Tags: CUDA, Data parallelism, Earth and Space Sciences, Filtering, Geoscience, nVidia, nVidia Quadro FX 6000, Performance

May 19, 2013 by hgpu

GPU Acceleration of the Variational Monte Carlo Method for Many Body Physics

Rajagopalan Kaushik Ragavan

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Tags: CUDA, Data parallelism, Heterogeneous systems, Monte Carlo simulation, MPI, nVidia, Physics, Tesla M2070, Thesis

May 1, 2013 by hgpu

Zero-copy I/O processing for low-latency GPU computing

Shinpei Kato, Jason Aumiller, Scott Brandt

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Tags: Algorithms, Benchmarking, CUDA, Data parallelism, nVidia, Plasma physics

April 16, 2013 by hgpu

Convergence and Scalarization for Data-Parallel Architectures

Yunsup Lee, Ronny Krashinsky, Vinod Grover, Stephen W. Keckler, Krste Asanovic

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, Data parallelism, nVidia, Performance

March 15, 2013 by hgpu

Multi-GPU based on multicriteria optimization for motion estimation system

Carlos Garcia, Guillermo Botella, Fermin Ayuso, Manuel Prieto, Francisco Tirado

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Tags: Algorithms, Computer vision, CUDA, Data parallelism, nVidia, Optical flow, Signal processing, Tesla C1060, Tesla M2070

February 27, 2013 by hgpu

GPU-based Motion Planning under Uncertainties using POMDP

Taekhee Lee, Young J. Kim

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 680

February 22, 2013 by hgpu

Data parallel patterns on CPU/GPU mix

T. Serban, M. Danelutto, M. Coppola

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GT 540, nVidia GeForce GTX 285, Tesla C2050

January 22, 2013 by hgpu

HiDP: A Hierarchical Data Parallel Language

Yongpeng Zhang, Frank Mueller

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Tags: Algorithms, Computer science, CUDA, Data parallelism, High-level Languages, nVidia, nVidia GeForce GTX 480

January 13, 2013 by hgpu

Video coding on multicore graphics processors (GPUs)

Bruno Alexandre de Medeiros

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Tags: Compression, CUDA, Data parallelism, H.264/AVC, Image processing, nVidia, nVidia GeForce GTX 580, Thesis, Video encoding

January 11, 2013 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

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high performance computing on graphics processing units: hgpu.org

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Job Parallelism using Graphical Processing Unit individual Multi-Processors and Highly Localised Memory

An implementation of level set based topology optimization using GPU

Parallel Zonal Summations of Large-Scale Species Occurrence Data on Hybrid CPU-GPU Systems

GPU Acceleration of the Variational Monte Carlo Method for Many Body Physics

Zero-copy I/O processing for low-latency GPU computing

Convergence and Scalarization for Data-Parallel Architectures

Multi-GPU based on multicriteria optimization for motion estimation system

GPU-based Motion Planning under Uncertainties using POMDP

Data parallel patterns on CPU/GPU mix

HiDP: A Hierarchical Data Parallel Language

Video coding on multicore graphics processors (GPUs)

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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