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Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, Boguslaw Obara, Andrew Stephen McGough

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Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Titan RTX, PyTorch, Tesla P100, Tesla V100

November 1, 2020 by hgpu

Mixed-Precision Embedding Using a Cache

Jie (Amy)Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang, Andrew Tulloch

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Tags: Artificial intelligence, Computer science, CUDA, Machine learning, Mixed precision, nVidia, Tesla V100

October 25, 2020 by hgpu

A Tensor Compiler for Unified Machine Learning Prediction Serving

Supun Nakandala Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

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Tags: Computer science, CUDA, Machine learning, nVidia, Package, Tesla K80, Tesla P100

October 18, 2020 by hgpu

Mastering Atari with Discrete World Models

Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba

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Tags: Artificial intelligence, Computer science, Deep learning, Machine learning, nVidia, Package, Python, TensorFlow, Tesla V100

October 11, 2020 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

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Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Anirban Ghose, Siddharth Singh, Vivek Kulaharia, Lokesh Dokara, Srijeeta Maity, Soumyajit Dey

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 970, OpenCL, Package

September 20, 2020 by hgpu

Performance portability through machine learning guided kernel selection in SYCL libraries

John Lawson

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Tags: AMD R9 Nano, ATI, Auto-Tuning, Benchmarking, Clustering, Computer science, Machine learning, Package, Performance, performance portability, SYCL

September 6, 2020 by hgpu

A Distributed Architecture for Smart Recycling Using Machine Learning

Dimitris Ziouzios, Dimitris Tsiktsiris, Nikolaos Baras, Minas Dasygenis

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Tags: Algorithms, Cloud, Computer science, Computer vision, CUDA, Machine learning, nVidia, Security

September 6, 2020 by hgpu

Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA

Zhe Lin, Sharad Sinha, Wei Zhang

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Tags: Computer science, FPGA, Machine learning

September 6, 2020 by hgpu

Machine Learning in Compilers: Past, Present and Future

Hugh Leather, Chris Cummins

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Tags: Compilers, Computer science, Deep learning, Heterogeneous systems, Machine learning

August 30, 2020 by hgpu

AIPerf: Automated machine learning as an AI-HPC benchmark

Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen

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Tags: Algorithms, Artificial intelligence, Benchmarking, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla V100

August 23, 2020 by hgpu

Deep Learning Application in Plant Stress Imaging: A Review

Zongmei Gao, Zhongwei Luo, Wen Zhang, Zhenzhen Lv, Yanlei Xu

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Tags: Deep learning, Earth and Space Sciences, Machine learning, OpenCL, Review, Sensing

August 2, 2020 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

Mixed-Precision Embedding Using a Cache

A Tensor Compiler for Unified Machine Learning Prediction Serving

Mastering Atari with Discrete World Models

Flexible Performant GEMM Kernels on GPUs

PySchedCL: Leveraging Concurrency in Heterogeneous Data-Parallel Systems

Performance portability through machine learning guided kernel selection in SYCL libraries

Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA

Machine Learning in Compilers: Past, Present and Future

AIPerf: Automated machine learning as an AI-HPC benchmark

Deep Learning Application in Plant Stress Imaging: A Review

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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