high performance computing on graphics processing units: hgpu.org

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GPU Computing with Python: Performance, Energy Efficiency and Usability

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 840 M, OpenCL, Performance, Python, Tesla K20, Tesla M2090, Tesla P100, Tesla V100

December 8, 2019 by hgpu

FBLAS: Streaming Linear Algebra Kernels on FPGA

Tiziano De Matteis, Johannes de Fine Licht, Torsten Hoefler

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Tags: BLAS, Computer science, FPGA, Linear Algebra, OpenCL, Package

December 1, 2019 by hgpu

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

Robert Dietrich

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Tags: Computer science, CUDA, Data acquisition, Heterogeneous systems, MPI, nVidia, OpenACC, OpenCL, Tesla K80, Tesla V100, Thesis

December 1, 2019 by hgpu

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini

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Tags: Algorithms, Computer science, IoT, Machine learning, Neural networks, Package

December 1, 2019 by hgpu

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Guoping Long, Jun Yang, Wei Lin

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Tags: Computer science, CUDA, Deep learning, nVidia, Performance, Tesla V100

December 1, 2019 by hgpu

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Jacob Austin, Rafael Corrales-Fatou, Sofia Wyetzner, Hod Lipson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, Robotics

December 1, 2019 by hgpu

Understanding the Performance of HPC Applications

Brian Gravelle

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Tags: Computer science, HPC, Performance

November 24, 2019 by hgpu

Benchmarking Deep Learning Models on Jetson TX2

Lucas Pedro Bordignon, Aldo von Wangenheim

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Image recognition, nVidia, nVidia Jetson TX2, Overview, Package, Tutorial

November 24, 2019 by hgpu

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Keshav Pingali

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Tags: Computer science, CUDA, Graph theory, nVidia, nVidia GeForce GTX 1080 Ti, Tesla V100

November 24, 2019 by hgpu

FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Ke He, Bo Liu, Yu Zhang, Andrew Ling, Dian Gu

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Tags: Caffe, Computer science, Deep learning, FPGA, Neural networks, OpenCL

November 24, 2019 by hgpu

Hacking Neural Networks: A Short Introduction

Michael Kissner

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

November 24, 2019 by hgpu

A Computing Kernel for Network Binarization on PyTorch

Xianda Xu, Marco Pedersoli

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

November 17, 2019 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Applications

GPU Computing with Python: Performance, Energy Efficiency and Usability

FBLAS: Streaming Linear Algebra Kernels on FPGA

Scalable Applications on Heterogeneous System Architectures: A Systematic Performance Analysis Framework

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

FusionStitching: Boosting Execution Efficiency of Memory Intensive Computations for DL Workloads

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Understanding the Performance of HPC Applications

Benchmarking Deep Learning Models on Jetson TX2

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)