high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications

Automatic Kernel Generation for Volta Tensor Cores

Somashekaracharya G. Bhaskaracharya, Julien Demouth, Vinod Grover

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Tags: Compilers, Computer science, CUBLAS, CUDA, Deep learning, Matrix multiplication, nVidia, nVidia Quadro GV100, Performance, Programming Languages, PTX

June 28, 2020 by hgpu

Autotuning for Automatic Parallelization on Heterogeneous Systems

Philip Pfaffe

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Tags: Auto-Tuning, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenMP, Performance, Tesla P100, Thesis

June 21, 2020 by hgpu

FPGA Based Satisfiability Checking

Rishi Bharadwaj Subramanian

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Tags: Computer science, Electronic design automation, FPGA, Heterogeneous systems, OpenCL, Thesis

June 21, 2020 by hgpu

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Szilárd Páll, Artem Zhmurov, Paul Bauer, Mark Abraham, Magnus Lundborg, Alan Gray, Berk Hess, Erik Lindahl

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Tags: Algorithms, AMD Radeon Instinct Mi50, AMD Vega FE, ATI, Chemistry, CUDA, Heterogeneous systems, Molecular dynamics, MPI, nVidia, nVidia GeForce RTX 2080, nVidia Quadro P 6000, OpenCL, Package, Tesla V100

June 21, 2020 by hgpu

Ansor: Generating High-Performance Tensor Programs for Deep Learning

Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica

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Tags: ARM, Compilers, Computer science, CUDA, Deep learning, Neural networks, nVidia, Optimization, Search strategies, Tesla V100

June 21, 2020 by hgpu

Unsupervised Deep Learning of Incompressible Fluid Dynamics

Nils Wandel, Michael Weinmann, Reinhard Klein

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Tags: Deep learning, Differential equations, Fluid dynamics, Fluid simulation, Neural networks, nVidia, nVidia GeForce GTX 2080 Ti, Partial differential equations, PDEs, PyTorch

June 21, 2020 by hgpu

The Rodinia Benchmark Suite in SYCL

Zheming Jin

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Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, Package, SYCL

June 14, 2020 by hgpu

A Compiler Infrastructure for Embedded Multicore SoCs

Weihua Sheng

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Tags: Benchmarking, Compilers, Computer science, GLSL, OpenGL, SoC, Software Engineering, Thesis

June 14, 2020 by hgpu

Software Testing – Test Suite Compilation and Execution Optimizations

Panagiotis Stratis

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Tags: ATI, ATI Radeon HD 5450, Benchmarking, Computer science, Heterogeneous systems, Optimization, SYCL, Thesis

June 14, 2020 by hgpu

AutoMat – Automatic Differentiation for Generalized Standard Materials on GPUs

Johannes Blühdorn, Nicolas R. Gauger, Matthias Kabel

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Tags: Computer science, CUDA, FFT, Mathematical Software, nVidia, nVidia Quadro GV100

June 14, 2020 by hgpu

Neural Architecture Search without Training

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

June 14, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

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Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 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

Automatic Kernel Generation for Volta Tensor Cores

Autotuning for Automatic Parallelization on Heterogeneous Systems

FPGA Based Satisfiability Checking

Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS

Ansor: Generating High-Performance Tensor Programs for Deep Learning

Unsupervised Deep Learning of Incompressible Fluid Dynamics

The Rodinia Benchmark Suite in SYCL

A Compiler Infrastructure for Embedded Multicore SoCs

Software Testing – Test Suite Compilation and Execution Optimizations

AutoMat – Automatic Differentiation for Generalized Standard Materials on GPUs

Neural Architecture Search without Training

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

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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