high performance computing on graphics processing units: hgpu.org

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Monitoring Collective Communication Among GPUs

Muhammet Abdullah Soyturk, Palwisha Akhtar, Erhan Tezcan, Didem Unat

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Tags: Computer science, CUDA, MPI, nVidia, Package, Performance, Tesla V100

October 24, 2021 by hgpu

Principal Kernel Analysis: A Tractable Methodology to Simulate Scaled GPU Workloads

Cesar A. Baddouh, Mahmoud Khairy, Roland Green, Mathias Payer, Timothy G. Rogers

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Tags: Computer science, CUDA, nVidia, Performance, Tesla V100

October 24, 2021 by hgpu

OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems

Nawras Alnaasan, Arpan Jain, Aamir Shafi, Hari Subramoni, Dhabaleswar K Panda

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Tags: Benchmarking, Computer science, CUDA, Deep learning, HPC, Machine learning, MPI, nVidia, Python, Tesla V100

October 24, 2021 by hgpu

Least Squares on GPUs in Multiple Double Precision

Jan Verschelde

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Tags: Code generation, Computer science, CUDA, Mathematical Software, nVidia, Package, Tesla P100, Tesla V100

October 24, 2021 by hgpu

Accelerating AutoDock VINA with GPUs

Shidi Tang, Ruiqi Chen, Mengru Lin, Qingde Lin, Yanxiang Zhu, Jiansheng Wu, Haifeng Hu, Ming Ling

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Tags: Chemistry, Databases, Heterogeneous systems, molecular docking, nVidia, nVidia GeForce GTX 1060, nVidia GeForce RTX 2080 Ti, nVidia GeForce RTX 3090, OpenCL, Package

October 17, 2021 by hgpu

Homomorphic-Encrypted Volume Rendering

Sebastian Mazza

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Tags: Algorithms, AMD Radeon RX 580, ATI, Computer science, GLSL, nVidia, nVidia GeForce RTX 3090, Package, Rendering, Security, Thesis, Visualization, Vulkan

October 17, 2021 by hgpu

Accelerating LBM on a Tightly-Coupled Field Programmable Gate Array

Mateo Vázquez Maceiras

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Tags: cfd, Fluid dynamics, FPGA, HLS, Lattice Boltzmann model, OpenCL, Thesis

October 17, 2021 by hgpu

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Shen Zhang

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, FPGA, nVidia, nVidia Tegra K1, Review

October 17, 2021 by hgpu

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

Martin Uray, Eduard Hirsch, Gerold Katzinger, Michael Gadermayr

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Tags: Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce RTX 2080 Ti, nVidia RTX A6000

October 17, 2021 by hgpu

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Yue Niu, Ramy E. Ali, Salman Avestimehr

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Tags: Computer science, Neural networks, nVidia, nVidia Quadro RTX 5000, Security

October 10, 2021 by hgpu

Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations

Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna

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Tags: Computer science, Deep learning, nVidia, nVidia GeForce GTX 1650

October 10, 2021 by hgpu

GCN Inference Acceleration using High-Level Synthesis

Yi Chien Lin, Bingyi Zhang, Viktor Prasanna

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Tags: Computer science, Data parallelism, Design space exploration, FPGA, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package

October 10, 2021 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

Monitoring Collective Communication Among GPUs

Principal Kernel Analysis: A Tractable Methodology to Simulate Scaled GPU Workloads

OMB-Py: Python Micro-Benchmarks for Evaluating Performance of MPI Libraries on HPC Systems

Least Squares on GPUs in Multiple Double Precision

Accelerating AutoDock VINA with GPUs

Homomorphic-Encrypted Volume Rendering

Accelerating LBM on a Tightly-Coupled Field Programmable Gate Array

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Beyond Desktop Computation: Challenges in Scaling a GPU Infrastructure

AsymML: An Asymmetric Decomposition Framework for Privacy-Preserving DNN Training and Inference

Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations

GCN Inference Acceleration using High-Level Synthesis

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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