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Towards automated kernel selection in machine learning systems: A SYCL case study

John Lawson

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Tags: AMD R9 Nano, ATI, Computer science, Heterogeneous systems, Machine learning, Package, Performance, SYCL

March 22, 2020 by hgpu

The Deep Learning Compiler: A Comprehensive Survey

Mingzhen Li, Yi Liu, Xiaoyan Liu, Qingxiao Sun, Xin You, Hailong Yang, Zhongzhi Luan, Depei Qian

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Tags: Compilers, Computer science, Deep learning, Machine learning, Performance

February 16, 2020 by hgpu

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

Andi Drebes, Lorenzo Chelini, Oleksandr Zinenko, Albert Cohen, Henk Corporaal, Tobias Grosser, Kanishkan Vadivel, Nicolas Vasilache

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Tags: Compilers, Computer science, CUDA, Machine learning

February 9, 2020 by hgpu

Non-Determinism in TensorFlow ResNets

Miguel Morin, Matthew Willetts

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Tags: Computer science, Deep learning, Machine learning, nVidia, Package, TensorFlow, Tesla M60

February 2, 2020 by hgpu

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

Xiaolong Ma, Wei Niu, Tianyun Zhang, Sijia Liu, Fu-Ming Guo, Sheng Lin, Hongjia Li, Xiang Chen, Jian Tang, Kaisheng Ma, Bin Ren, Yanzhi Wang

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Tags: ARM, Computer science, Computer vision, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package

January 26, 2020 by hgpu

GPU Tensor Cores for fast Arithmetic Reductions

Cristóbal A. Navarro, Roberto Carrasco, Ricardo J. Barrientos, Javier A. Riquelme, Raimundo Vega

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, Tesla V100, TPU

January 19, 2020 by hgpu

Towards High Performance Java-based Deep Learning Frameworks

Athanasios Stratikopoulos, Juan Fumero, Zoran Sevarac, Christos Kotselidis

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Tags: Computer science, Computer vision, CUDA, Data mining, Deep learning, Heterogeneous systems, Java, Machine learning, nVidia, OpenCL, Package

January 19, 2020 by hgpu

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

Jiajia Li, Mahesh Lakshminarasimhan, Xiaolong Wu, Ang Li, Catherine Olschanowsky, Kevin Barker

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Tags: Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia DGX-1, Package, Performance, Tesla P100, Tesla V100

January 12, 2020 by hgpu

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

Lifu Zhang, Tarek S. Abdelrahman

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1060, PyTorch

January 5, 2020 by hgpu

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

Akrem Benatia, Weixing Ji, Yizhuo Wang, Feng Shi

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X, Sparse matrix

January 5, 2020 by hgpu

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

Samuel S. Schoenholz, Ekin D. Cubuk

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Tags: Biochemistry, Chemistry, Computational Physics, CUDA, Fortran, Machine learning, Materials Science, Molecular dynamics, Neural networks, nVidia, Package, Physics, Python

December 15, 2019 by hgpu

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

Jocelyn Sunseri, David Ryan Koes

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Tags: Chemistry, Computer science, CUDA, Deep learning, Machine learning, Molecular modeling, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce GTX Titan X, Package, PyTorch, TensorFlow

December 15, 2019 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

Towards automated kernel selection in machine learning systems: A SYCL case study

The Deep Learning Compiler: A Comprehensive Survey

TC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory

Non-Determinism in TensorFlow ResNets

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

GPU Tensor Cores for fast Arithmetic Reductions

Towards High Performance Java-based Deep Learning Frameworks

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms

JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

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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