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hgpu.org » Applications » Computer science

Real-Time Scheduling Using GPUs – Advanced and More Accurate Proof of Feasibility

Peter Fodrek, Ludovit Farkas, Michal Blaho, Martin Foltin, Juraj Hnat, Tomas Murgas

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Tags: Benchmarking, Computer science, OpenCL, Performance, Task scheduling

November 14, 2012 by hgpu

Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation

Haicheng Wu, Gregory Diamos, Srihari Cadambi, Sudhakar Yalamanchili

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Tags: Computer science, CUDA, nVidia, Optimization, Tesla C2070

November 14, 2012 by hgpu

A parallel method for tuning Fuzzy TSK Systems with CUDA

Bruno B. Ferreira, Adriano J. O. Cruz

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 550 Ti, nVidia GeForce GTX 590, Tesla C2070

November 11, 2012 by hgpu

Sigma*: Symbolic Learning of Input-Output Specifications

Matko Botincan, Domagoj Babic

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Programming Languages

November 10, 2012 by hgpu

Efficient Dynamic Derived Field Generation on Many-Core Architectures Using Python

Cyrus Harrison, Paul Navratil, Maysam Moussalem, Ming Jiang, Hank Childs

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Tags: Computer science, nVidia, OpenCL, Python, Tesla M2050, Visualization

November 10, 2012 by hgpu

Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Apan Qasem

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Tags: Compilers, Computer science, CUDA, Memory model, nVidia, nVidia GeForce 9800 GT, Tesla C2050

November 8, 2012 by hgpu

Flexible N-Way MIMO Detector on GPU

Michael Wu, Bei Yin, Joseph R. Cavallaro

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Tags: Computer science, CUDA, MIMO, nVidia, nVidia GeForce GTX 560 Ti

November 8, 2012 by hgpu

Reusable OpenCL FPGA Infrastructure

Stephen Alexander Chin

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Thesis

November 8, 2012 by hgpu

SPH Based Fluid Animation Using CUDA Enabled GPU

Uday A. Nuli, P. J. Kulkarni

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Fluid simulation, nVidia, nVidia GeForce GTX 280

November 8, 2012 by hgpu

Study of Convolution Algorithms using CPU and Graphics Hardware

Matz Johansson Bergstrom

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 570, Thesis

November 7, 2012 by hgpu

A GPU-Based Transient Stability Simulation Using Runge-Kutta Integration Algorithm

Zhijun Qin, Yunhe Hou

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Tags: Algorithms, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GTX 580, Sparse direct solvers

November 7, 2012 by hgpu

GPU Virtualization

Kristoffer Robin Stokke

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Source codes

Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia Quadro NVS 295, Package, Python, Thesis, Virtualization

November 7, 2012 by hgpu

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

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

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Real-Time Scheduling Using GPUs – Advanced and More Accurate Proof of Feasibility

Kernel Weaver: Automatically Fusing Database Primitives for Efficient GPU Computation

A parallel method for tuning Fuzzy TSK Systems with CUDA

Sigma*: Symbolic Learning of Input-Output Specifications

Efficient Dynamic Derived Field Generation on Many-Core Architectures Using Python

Architectural Considerations for Compiler-guided Unroll-and-Jam of CUDA Kernels

Flexible N-Way MIMO Detector on GPU

Reusable OpenCL FPGA Infrastructure

SPH Based Fluid Animation Using CUDA Enabled GPU

Study of Convolution Algorithms using CPU and Graphics Hardware

A GPU-Based Transient Stability Simulation Using Runge-Kutta Integration Algorithm

GPU Virtualization

Recent source codes

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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