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A Massively Parallel Algorithm for Cell Classification Using CUDA

Samuel Schmidt

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Tags: Bioinformatics, Biology, CUDA, Machine learning, nVidia, nVidia GeForce GTX 770, RNA, Thesis

March 20, 2016 by hgpu

Analyzing and Improving the Performance of Spatial Database Processing

Bogdan Simion

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Thesis

March 20, 2016 by hgpu

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

Nicholas D. Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, Fahim Kawsar

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia Jetson TK1, SoC

March 15, 2016 by hgpu

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Li-Wen Chang, Hee-Seok Kim, Wen-mei Hwu

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, OpenCL, Performance, Tesla K20

March 15, 2016 by hgpu

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Wei Tan, Liangliang Cao, Liana Fong

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Factorization, nVidia, nVidia GeForce GTX Titan X, OpenMP, Tesla K80

March 15, 2016 by hgpu

Machine Learning at the Limit

John Canny, Huasha Zhao, Ye Chen, Bobby Jaros, Jiangchang Mao

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Source codes

Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX Titan X, Package

March 12, 2016 by hgpu

SGO: An ultrafast engine for atomic structure global optimization by differential evolution

Zhanghui Chen, Lin-Wang Wang

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Tags: Condensed matter, Differential evolution, Materials Science, nVidia, nVidia GeForce GTX 980

March 12, 2016 by hgpu

A portable platform for accelerated PIC codes and its application to GPUs using OpenACC

F. Hariri, T.M. Tran, A. Jocksch, E. Lanti, J. Progsch, P. Messmer, S. Brunner, G. Gheller, L. Villard

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Tags: Computational Physics, Heterogeneous systems, nVidia, OpenACC, Particle-in-cell methods, Physics, Plasma physics, Tesla K20

March 12, 2016 by hgpu

Clinically applicable Monte Carlo-based biological dose optimization for the treatment of head and neck cancers with spot-scanning proton therapy

H. Wan Chan Tseung, J. Ma, C. R. Kreofsky, D. Ma, C. Beltran

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Tags: Medical Physics, nVidia, nVidia GeForce GTX Titan X, Physics, Tesla K20

March 12, 2016 by hgpu

Automatic and Explicit Parallelization Approaches for Mathematical Simulation Models

Mahder Gebremedhin

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Tags: Compilers, Computer science, nVidia, OpenCL, Tesla M2050, Thesis

March 12, 2016 by hgpu

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

David S. Lawrie

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Tags: Algorithms, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 780 M, nVidia GeForce GTX 980

March 10, 2016 by hgpu

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar

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Tags: Algorithms, Compilers, Computer science, Heterogeneous systems, nVidia, OpenCL, Programming techniques, Tesla M2050

March 10, 2016 by hgpu

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

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

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high performance computing on graphics processing units: hgpu.org

A Massively Parallel Algorithm for Cell Classification Using CUDA

Analyzing and Improving the Performance of Spatial Database Processing

DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

DySel: Lightweight Dynamic Selection for Kernel-based Data-parallel Programming Model

Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs

Machine Learning at the Limit

SGO: An ultrafast engine for atomic structure global optimization by differential evolution

A portable platform for accelerated PIC codes and its application to GPUs using OpenACC

Clinically applicable Monte Carlo-based biological dose optimization for the treatment of head and neck cancers with spot-scanning proton therapy

Automatic and Explicit Parallelization Approaches for Mathematical Simulation Models

Accelerating Wright-Fisher Forward Simulations on the Graphics Processing Unit

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Recent source codes

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

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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