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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

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Yun Fei, Yuhan Huang, Ming Gao

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Tags: Computer science, CUDA, Graphics, nVidia, nVidia DGX-1, Physics-based simulation, Tesla V100

November 7, 2021 by hgpu

Improving Performance and Energy Efficiency of GPUs through Locality Analysis

Devashree Tripathy

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Performance, PTX, Thesis

October 31, 2021 by hgpu

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Zhengda Bian, Hongxin Liu, Boxiang Wang, Haichen Huang, Yongbin Li, Chuanrui Wang, Fan Cui, Yang You

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, Package, Tesla P100

October 31, 2021 by hgpu

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu

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Tags: Auto-Tuning, Computer science, CUDA, Deep learning, Neural networks, nVidia, Performance, Tesla T4

October 31, 2021 by hgpu

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

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Tags: Compilers, Computer science, CUDA, Deep learning, nVidia, Tesla V100

October 24, 2021 by hgpu

HPCToolkit

Refining HPCToolkit for application performance analysis at exascale

Open Catalog of Best Practices for Modernization and Optimization

Optimizing the Weather Research and Forecasting Model with OpenMP Offload and Codee

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

Exploring Scalability in C++ Parallel STL Implementations

GPU-Smash: Compression abstraction library

A Parallel Compression Pipeline for Improving GPU Virtualization Data Transfers

Noarr Structures

Abstractions for C++ code optimizations in parallel high-performance applications

$3D-fractal-generators$

HiCCL: A hierarchical collective communications library with portable optimizations

HiCCL: A Hierarchical Collective Communication Library

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Principles towards Real-Time Simulation of Material Point Method on Modern GPUs

Improving Performance and Energy Efficiency of GPUs through Locality Analysis

Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training

Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance

The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding

Recent source codes

HPCToolkit

Open Catalog of Best Practices for Modernization and Optimization

Owl: Differential-based Side-Channel Leakage Detection for CUDA Applications

pSTL-Bench: Micro-benchmark suite designed to evaluate the scalability and efficiency of parallel C++ STL implementations

GPU-Smash: Compression abstraction library

Noarr Structures

3D-fractal-generators

SyncPerformance

VerCors: a verification toolset for verifying parallel and concurrent software

HiCCL: A hierarchical collective communications library with portable optimizations

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