high performance computing on graphics processing units: hgpu.org

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Lectures on Parallel Computing

Jesper Larsson Träff

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Tags: Computer science, Education, parallel computing

August 4, 2024 by hgpu

Evaluating the Wide Area Classroom After 24,000 HPC Students

John Urbanic, Thomas Maiden, Valerie Rossi

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Tags: Education, HPC, Machine learning, MPI, OpenACC, OpenMP, Physics

February 12, 2024 by hgpu

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

Nathan George

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Tags: Computer science, CUDA, Deep learning, Education, GPU cluster, HPC, nVidia, nVidia GeForce GTX 1080 Ti, nVidia Quadro RTX 5000, Overview

May 24, 2020 by hgpu

EASYPAP: a Framework for Learning Parallel Programming

Alice Lasserre, Raymond Namyst, Pierre-André Wacrenier

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Source codes

Tags: Algorithms, AVX, Computer science, Education, MPI, OpenCL, OpenMP, Package, Pthreads

February 16, 2020 by hgpu

Deep Learning by Doing: The NVIDIA Deep Learning Institute and University Ambassador Program

Xi Chen, Gregory S. Gutmann, Joe Bungo

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Tags: Cloud, Computer science, CUDA, Deep learning, Distributed computing, Education, Neural networks, nVidia, OpenACC

December 23, 2018 by hgpu

A University-Industry Collaboration Case Study: Intel Real-Time Multi-View Face Detection Capstone Design Projects

Vincent Chang, Bohua Gan, Guanying Wang, Xiuli Pan, Guan Wang, Naihai Zou, Fleming Feng

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Tags: Computer science, Education, Face detection, Intel HD Graphics 4000, OpenCL

August 1, 2015 by hgpu

OPNET: An Integrated Design Paradigm for Simulations

Sparsh Mittal

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Tags: curriculum, Education, Network communications, Networking and Internet Architecture, simulation, survey

November 20, 2014 by sparsh0mittal

Teaching Parallel Programming Using Java

Aamir Shafi, Aleem Akhtar, Ansar Javed, Bryan Carpenter

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Tags: Computer science, CUDA, Education, Hadoop, Java, MapReduce, MPI, nVidia, Programming techniques, Software Engineering

October 4, 2014 by hgpu

Improving Student Learning in Computer Science Courses by Using Virtual OpenCL Laboratory

Diming Zhang, Yuanjiang Li

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Tags: Computer science, Education, OpenCL

January 16, 2014 by hgpu

Making the case of GPUs in courses on computational physics

Knut Skogstrand Gjerden

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Tags: Computational Physics, CUBLAS, CUDA, Education, FFT, Mathematical Software, nVidia, nVidia GeForce GTX 320 M, Physics, Tesla C2050

May 17, 2013 by hgpu

Exploring Computer Vision and Image Processing Algorithms in Teaching Parallel Programming

Dan Connors

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Tags: Computer science, Computer vision, CUDA, Education, Image processing, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL

May 11, 2013 by hgpu

Adding GPU Computing to Computer Organization Courses

David Bunde, Karen L. Karavanic, Jens Mache

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Tags: Computer science, CUDA, Education, nVidia

May 2, 2013 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Lectures on Parallel Computing

Evaluating the Wide Area Classroom After 24,000 HPC Students

Literature Review and Implementation Overview: High Performance Computing with Graphics Processing Units for Classroom and Research Use

EASYPAP: a Framework for Learning Parallel Programming

A University-Industry Collaboration Case Study: Intel Real-Time Multi-View Face Detection Capstone Design Projects

OPNET: An Integrated Design Paradigm for Simulations

Teaching Parallel Programming Using Java

Improving Student Learning in Computer Science Courses by Using Virtual OpenCL Laboratory

Making the case of GPUs in courses on computational physics

Exploring Computer Vision and Image Processing Algorithms in Teaching Parallel Programming

Adding GPU Computing to Computer Organization Courses

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)