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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

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Zijing Gu

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, Package, Sparse matrix, Tesla T4

August 2, 2020 by hgpu

Darknet on OpenCL: a multi-platform tool for object detection and classification

Piotr Sowa, Jacek Izydorczyk

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Tags: ARM, Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Titan RTX, OpenCL, Package

July 26, 2020 by hgpu

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava, Muhammad Aleem

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Tags: Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GT 740 M, nVidia GeForce GTX 760, OpenCL

July 19, 2020 by hgpu

Deep Graph Library Optimizations for Intel(R) x86 Architecture

Sasikanth Avancha, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Package

July 19, 2020 by hgpu

Investigating Input Representations and Representation Models of Source Code for Machine Learning

Alexander Brauckmann

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Tags: AMD Radeon HD 7970, ATI, Code generation, Computer science, LLVM, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 970, OpenCL, Thesis

July 12, 2020 by hgpu

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Yuanhang Yu, Dong Wen, Ying Zhang, Xiaoyang Wang, Wenjie Zhang, Xuemin Lin

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Tags: Algorithms, Computer science, CUDA, Data mining, Factorization, Heterogeneous systems, Machine learning, nVidia, nVidia Quadro P 4000

July 5, 2020 by hgpu

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

Min Li, Yulong Ao, Chao Yang

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Tags: Computer science, CUDA, Machine learning, nVidia, Sparse matrix, Tesla K40, Tesla P100, Tesla V100

July 5, 2020 by hgpu

Sparse GPU Kernels for Deep Learning

Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, Sparse matrix, Tesla V100

June 28, 2020 by hgpu

Neural Architecture Search without Training

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

June 14, 2020 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

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

Optimizing Block-Sparse Matrix Multiplications on CUDA with TVM

Darknet on OpenCL: a multi-platform tool for object detection and classification

A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster

Deep Graph Library Optimizations for Intel(R) x86 Architecture

Investigating Input Representations and Representation Models of Source Code for Machine Learning

Efficient Matrix Factorization on Heterogeneous CPU-GPU Systems

Adaptive SpMV/SpMSpV on GPUs for Input Vectors of Varied Sparsity

Sparse GPU Kernels for Deep Learning

Neural Architecture Search without Training

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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