high performance computing on graphics processing units: hgpu.org

Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica

View

Download (PDF)

Tags: ARM, Compilers, Computer science, CUDA, Deep learning, Neural networks, nVidia, Optimization, Search strategies, Tesla V100

June 21, 2020 by hgpu

Neural Architecture Search without Training

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

View

Download (PDF)

Source codes

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

June 14, 2020 by hgpu

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

Behnam Pourghassemi, Chenghao Zhang, Joo Hwan Lee, Aparna Chandramowlishwaran

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, TensorFlow, Tesla K40

May 31, 2020 by hgpu

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi

View

Download (PDF)

Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2060, nVidia Quadro P 4000, nVidia Titan RTX

May 31, 2020 by hgpu

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

An approach to performance portability through generic programming

Redco: A Lightweight Tool to Automate Distributed Training

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

OpenRAND: Reproducible Random Number For Parallel Computations

OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

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

EDSSA: An Encoder-Decoder Semantic Segmentation Networks Accelerator on OpenCL-Based FPGA Platform

Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs

Deep Graph Library Optimizations for Intel(R) x86 Architecture

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

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Sparse GPU Kernels for Deep Learning

Unsupervised Deep Learning of Incompressible Fluid Dynamics

Ansor: Generating High-Performance Tensor Programs for Deep Learning

Neural Architecture Search without Training

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Recent source codes

miniBUDE: A BUDE virtual-screening benchmark, in many programming models

gossip: Efficient Communication Primitives for Multi-GPU Systems

GT4Py: GridTools for Python

AUCMEDI: a framework for Automated Classification of Medical Images

Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

AFOCL: Portable OpenCL Programming of FPGAs via Automated Built-in Kernel Management

pp-template: Template for Performance-Portable code development, targeting HOST-DEVICE computer architectures

Redco: A Lightweight Tool to Automate Distributed Training

OpenRAND: Reproducible Random Number For Parallel Computations

Most viewed papers (last 30 days)