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Autotuning CUDA: Applying NLP Techniques to LS-CAT

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Computer science, CUDA, Machine learning, NLP, nVidia

December 5, 2021 by hgpu

Bayesian Optimization for auto-tuning GPU kernels

Floris-Jan Willemsen, Rob van Nieuwpoort, Ben van Werkhoven

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Tags: Bayesian, Computer science, CUDA, Machine learning, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 2070, OpenCL, Package, Performance

December 5, 2021 by hgpu

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Yuxin Chen, Benjamin Brock, Serban Porumbescu, Aydın Buluç, Katherine Yelick, John D. Owens

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Tags: Computer science, CUDA, nVidia, Task scheduling, Tesla V100

December 5, 2021 by hgpu

Predictive Data Race Detection for GPUs

Sagnik Dey, Mayant Mukul, Parth Sharma, Swarnendu Biswas

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro RTX 5000, Performance

November 28, 2021 by hgpu

A Variant RSA Acceleration with Parallelization

Jun-jie Liu, Kang-Too Tsang, Yu-Hui Deng

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Tags: Computer science, CUDA, nVidia, RSA, Security, Tesla P100

November 28, 2021 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

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Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

Lukas Sommer

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2070, OpenCL, OpenMP, Package, Python, Thesis

November 21, 2021 by hgpu

Accelerating JPEG Decompression on GPUs

André Weißenberger, Bertil Schmidt

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Tags: Compression, CUDA, Image processing, nVidia, nVidia A100

November 21, 2021 by hgpu

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Yuke Wang, Boyuan Feng, Yufei Ding

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, TPU

November 21, 2021 by hgpu

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Zane Fink, Simeng Liu, Jaemin Choi, Matthias Diener, Laxmikant V. Kale

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Tags: Benchmarking, Computer science, CUDA, Distributed computing, HPC, Machine learning, MPI, nVidia, Package, Performance, Python, Tesla V100

November 14, 2021 by hgpu

Collage: Automated Integration of Deep Learning Backends

Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2070, Tesla V100

November 7, 2021 by hgpu

iGUARD: In-GPU Advanced Race Detection

Aditya K Kamath, Arkaprava Basu

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Tags: Computer science, CUDA, nVidia, nVidia Titan RTX, Package, Performance

November 7, 2021 by hgpu

Kernel Tuner

Bayesian Optimization for auto-tuning GPU kernels

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice

HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

BAT: A standardized benchmark suite for auto-tuners

BAT: A Benchmark suite for AutoTuners

The Darmstadt Automotive Parallel HeterogeNEous (DAPHNE) Benchmark-Suite

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

TaPaSCo: The Task Parallel System Composer

Optimizing a Hardware Network Stack to Realize an In-Network ML Inference Application

Tapenade Automatic Differentiation Engine

Source-to-Source Automatic Differentiation of OpenMP Parallel Loops

See all packages

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high performance computing on graphics processing units: hgpu.org

Autotuning CUDA: Applying NLP Techniques to LS-CAT

Bayesian Optimization for auto-tuning GPU kernels

Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations

Predictive Data Race Detection for GPUs

A Variant RSA Acceleration with Parallelization

BAT: A Benchmark suite for AutoTuners

Programming Heterogeneous Systems with General and Domain-Specific Frameworks

Accelerating JPEG Decompression on GPUs

QGTC: Accelerating Quantized GNN via GPU Tensor Core

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

Collage: Automated Integration of Deep Learning Backends

iGUARD: In-GPU Advanced Race Detection

Recent source codes

Kernel Tuner

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice

BAT: A standardized benchmark suite for auto-tuners

The Darmstadt Automotive Parallel HeterogeNEous (DAPHNE) Benchmark-Suite

General purpose lattice QCD code set Bridge++ 2.0 for high performance computing

MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Performance Evaluation of Python ParallelProgramming Models: Charm4Py and mpi4py

iGUARD: In-GPU Advanced Race Detection

TaPaSCo: The Task Parallel System Composer

Tapenade Automatic Differentiation Engine

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