high performance computing on graphics processing units: hgpu.org

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Data-Driven Dynamic Autotuning: Optimizing Autotuning Overhead with Prior Tuning Data

Jaroslav Oľha

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Tags: AMD Radeon RX Vega 56, ATI, Auto-Tuning, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Performance, Tesla K20, Thesis

November 3, 2024 by hgpu

Dynamic autotuning of SpMV kernel in CUSP library

Miroslav Demek

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Tags: Auto-Tuning, Computer science, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, Performance, Sparse matrix, Thesis

October 29, 2023 by hgpu

Kernel Tuning Toolkit

Filip Petrovič, Jiří Filipovič

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, Python, Vulkan

April 16, 2023 by hgpu

A Ray Tracing Implementation Performance Comparison between the CPU and the GPU

Robin Nordmark, Tim Olsén

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Tags: 3D Graphics and Realism, CUDA, nVidia, nVidia GeForce GTX 1070, Package, Raytracing, Rendering

October 23, 2022 by hgpu

Using hardware performance counters to speed up autotuning convergence on GPUs

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, HPC, Machine learning, nVidia, nVidia GeForce GTX 1070, nVidia GeForce RTX 2080, OpenCL, Package, Performance, Vulkan

February 23, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

Fast CUDA-Aware MPI Datatypes without Platform Support

Carl Pearson, Kun Wu, I-Hsin Chung, Jinjun Xiong, Wen-Mei Hwu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, Tesla V100

January 3, 2021 by hgpu

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

Jianqi Lai, Hang Yu, Zhengyu Tian, Hua Li

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Tags: cfd, CUDA, Fluid dynamics, GPU cluster, HPC, MPI, nVidia, nVidia GeForce GTX 1070, OpenMP, Tesla V100

September 27, 2020 by hgpu

PERCH 2.0: Fast and Accurate GPU-based Perception via Search for Object Pose Estimation

Aditya Agarwal, Yupeng Han, Maxim Likhachev

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Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 1070, Package, Robotics, Tesla P100, Tesla V100

August 9, 2020 by hgpu

Studies on CUDA Offloading for Real-Time Simulation and Visualization

Edgar Josafat Martinez-Noriega

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Tags: Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 970 M, nVidia GeForce RTX 2080, nVidia Jetson K1, Rendering, Thesis, Virtualization, Visualization

July 5, 2020 by hgpu

Framework for Parallel Kernels Auto-tuning

Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenCL, Package, Performance, Thesis

November 10, 2019 by hgpu

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič

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Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20

October 27, 2019 by hgpu

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

No More Shading Languages: Compiling C++ to Vulkan Shaders

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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high performance computing on graphics processing units: hgpu.org

Data-Driven Dynamic Autotuning: Optimizing Autotuning Overhead with Prior Tuning Data

Dynamic autotuning of SpMV kernel in CUSP library

Kernel Tuning Toolkit

A Ray Tracing Implementation Performance Comparison between the CPU and the GPU

Using hardware performance counters to speed up autotuning convergence on GPUs

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Fast CUDA-Aware MPI Datatypes without Platform Support

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

PERCH 2.0: Fast and Accurate GPU-based Perception via Search for Object Pose Estimation

Studies on CUDA Offloading for Real-Time Simulation and Visualization

Framework for Parallel Kernels Auto-tuning

A Benchmark Set of Highly-efficient CUDA and OpenCL Kernels and its Dynamic Autotuning with Kernel Tuning Toolkit

Recent source codes

WiLLM: An Open Wireless LLM Communication System

Vcc: the Vulkan Clang Compiler

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

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