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

Yang He

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Tags: Computer science, CUDA, Deep learning, nVidia, PyTorch, Software Engineering, Tesla V100, Thesis

November 13, 2022 by hgpu

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

Fei Xu, Jianian Xu, Jiabin Chen, Li Chen, Ruitao Shang, Zhi Zhou, Fangming Liu

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Tags: Cloud, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 13, 2022 by hgpu

pyGSL: A Graph Structure Learning Toolkit

Max Wasserman, Gonzalo Mateos

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Tags: Computer science, Machine learning, Package, PyTorch, Signal processing

November 13, 2022 by hgpu

Multi-GPU thermal lattice Boltzmann simulations using OpenACC and MPI

Ao Xu, Bo-Tao Li

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Tags: cfd, Fluid dynamics, Lattice Boltzmann model, MPI, OpenACC

November 13, 2022 by hgpu

An Open-source FPGA Library for Data Sorting

Ryohei Kobayashi, Kento Miura, Norihisa Fujita, Taisuke Boku, Toshiyuki Amagasa

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Tags: Algorithms, Computer science, FPGA, OpenCL, Package, Sorting

November 6, 2022 by hgpu

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Guyue Huang, Yang Bai, Liu Liu, Yuke Wang, Bei Yu, Yufei Ding, Yuan Xie

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia A100

November 6, 2022 by hgpu

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

David Defour

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Tags: Computer science, CUDA, Floating point error, nVidia, nVidia GeForce RTX 2060, OpenMP

November 6, 2022 by hgpu

Apple Silicon Performance in Scientific Computing

Connor Kenyon, Collin Capano

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Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia A100, nVidia V100, OpenCL, Performance

November 6, 2022 by hgpu

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings

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Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, Materials Science, Neural networks, Package, Python

November 6, 2022 by hgpu

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Erik Tedhamre

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, OpenCL, OpenMP, Package, Performance

October 30, 2022 by hgpu

Benchmarking GPU and TPU Performance with Graph Neural Networks

Xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma, Steven Farrell, Paolo Calafiura

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia A100, Pattern recognition, Tesla V100, TPU

October 30, 2022 by hgpu

gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs

Taha Tekdoğan, Serkan Göktaş, Ayse Yilmazer-Metin

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Tags: Benchmarking, Computer science, CUDA, Machine learning, Neural networks, nVidia, Performance, Tesla V100

October 30, 2022 by hgpu

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

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high performance computing on graphics processing units: hgpu.org

Applications

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

pyGSL: A Graph Structure Learning Toolkit

Multi-GPU thermal lattice Boltzmann simulations using OpenACC and MPI

An Open-source FPGA Library for Data Sorting

Enabling Data Movement and Computation Pipelining in Deep Learning Compiler

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues

Apple Silicon Performance in Scientific Computing

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

A systematic performance study of the parallel programming framework SkePU 3 using HPC-benchmarks

Benchmarking GPU and TPU Performance with Graph Neural Networks

gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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