high performance computing on graphics processing units: hgpu.org

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Providing performance portable numerics for Intel GPUs

Yu-Hsiang M. Tsai, Terry Cojean, Hartwig Anzt

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, Linear Algebra, nVidia, nVidia A100, OpenCL, Package, performance portability, Sparse, SYCL

October 30, 2022 by hgpu

From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels

Gregor Daiß, Patrick Diehl, Dominic Marcello, Alireza Kheirkhahan, Hartmut Kaiser, Dirk Pflüger

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Tags: AMD Radeon Instinct MI100, Astrophysics, ATI, CUDA, HIP, nVidia, nVidia A100, Package, performance portability, Physics

October 23, 2022 by hgpu

PMT: Power Measurement Toolkit

Stefano Corda, Bram Veenboer, Emma Tolley

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Tags: AMD Radeon Pro W6600, ATI, Computer science, Energy-efficient computing, HPC, nVidia, nVidia Titan RTX, Package, Performance

October 16, 2022 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

Enhancing the Performance Portability of Heterogeneous Circuit Analysis Programs

Tsung-Wei Huang

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Tags: AMD Radeon RX 6900 XT, ATI, Computer science, Deep learning, nVidia, nVidia GeForce RTX 3090, OpenCL, performance portability, PyTorch, SYCL

September 11, 2022 by hgpu

Towards making the most of NLP-based device mapping optimization for OpenCL kernels

Petros Vavaroutsos, Ioannis Oroutzoglou, Dimosthenis Masouros, Dimitrios Soudris

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 970, OpenCL

September 4, 2022 by hgpu

Optimization of GPU workloads using natural language processing based on deep learning techniques

Petros Vavaroutsos

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, OpenCL, Thesis

August 21, 2022 by hgpu

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Francesco Barchi, Emanuele Parisi, Andrea Bartolini, Andrea Acquaviva

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Tags: AMD Radeon HD 7970, ATI, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Optimization, Overview, Security

August 7, 2022 by hgpu

Reducing Synchronous GPU Memory Transfers: Design and implementation of a Futhark compiler optimisation

Philip Jon Børgesen

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, nVidia, nVidia A100, OpenCL, Performance, Thesis

July 17, 2022 by hgpu

Just-in-Time Compilation and Link-Time Optimization for OpenMP Target Offloading

Shilei Tian, Joseph Huber, Barbara Chapman, Johannes Doerfert

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Tags: AMD Radeon Instinct MI100, ATI, Compilers, Computer science, CUDA, nVidia, nVidia A100, OpenCL, OpenMP

July 17, 2022 by hgpu

Fast GPU bounding boxes on tree-structured scenes

Raph Levien

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Tags: 3D Graphics and Realism, AMD Radeon RX 5700 XT, ATI, GLSL, HLSL, nVidia, nVidia GeForce GTX 1080, Package, Rendering, Vulkan

May 29, 2022 by hgpu

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

André Müller, Bertil Schmidt, Richard Membarth, Roland Leißa, Sebastian Hack

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Tags: AMD Radeon Instinct MI100, ATI, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, nVidia GeForce RTX 3090, OpenCL, Package, Sequence alignment, Tesla A100

May 22, 2022 by hgpu

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KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

A Study on Neural-based Code Summarization in Low-resource Settings

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

Providing performance portable numerics for Intel GPUs

From Task-Based GPU Work Aggregation to Stellar Mergers: Turning Fine-Grained CPU Tasks into Portable GPU Kernels

PMT: Power Measurement Toolkit

Benchmarking optimization algorithms for auto-tuning GPU kernels

Enhancing the Performance Portability of Heterogeneous Circuit Analysis Programs

Towards making the most of NLP-based device mapping optimization for OpenCL kernels

Optimization of GPU workloads using natural language processing based on deep learning techniques

Deep Learning Approaches to Source Code Analysis for Optimization of Heterogeneous Systems: Recent Results, Challenges and Opportunities

Reducing Synchronous GPU Memory Transfers: Design and implementation of a Futhark compiler optimisation

Just-in-Time Compilation and Link-Time Optimization for OpenMP Target Offloading

Fast GPU bounding boxes on tree-structured scenes

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

Recent source codes

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

SYCL-DNN: a library implementing neural network algorithms written using SYCL

SW#SYCL

Flink-TornadoVM-Artifact

StateMover: a checkpoint-based debugging framework for FPGAs

Kernel Tuner

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

NaturalCC: An Open-Source Toolkit for Code Intelligence

mt4g: Memory Topology for GPUs

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