high performance computing on graphics processing units: hgpu.org

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Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

Corey Lammie, Wei Xiang, Mostafa Rahimi Azghadi

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Tags: Computer science, Deep learning, FPGA, Heterogeneous systems, Neural networks, OpenCL

May 19, 2019 by hgpu

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

Daeyeon Kim

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Tags: Computer science, Deep learning, FPGA, Neural networks, OpenCL, Performance, Thesis

May 19, 2019 by hgpu

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

Benjamin Ferrell, Jun Duan, Kevin W. Hamlen

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, PTX

May 15, 2019 by hgpu

A Unified Approach to Variable Renaming for Enhanced Vectorization

Prasanth Chatarasi, Jun Shirako, Albert Cohen, Vivek Sarkar

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Tags: Benchmarking, Compilers, Computer science, CUDA, Intel Xeon Phi, nVidia, Tesla V100

May 15, 2019 by hgpu

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

Pramod Kumbhar, Omar Awile, Liam Keegan, Jorge Blanco Alonso, James King, Michael Hines, Felix Schuermann

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Tags: Biophysics, Code generation, Compilers, Computer science, CUDA, HPC, Intel Xeon Phi, LLVM, nVidia, OpenACC, OpenMP, Package

May 15, 2019 by hgpu

Improving Resource Efficiency in Virtualized Datacenters

Marcelo Amaral

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Tags: Cloud, Computer science, CUDA, nVidia, nVidia DGX-1, Thesis, Virtualization

May 12, 2019 by hgpu

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Muhammad Mohid Nabil

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Tags: Computer science, CUDA, Deep learning, FPGA, HLS, Image recognition, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Precision, Thesis

May 12, 2019 by hgpu

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Richard Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, Thomas Blumensath

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Tags: Algorithms, Computed tomography, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, Package

May 12, 2019 by hgpu

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

Husheng Zhou

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 620, nVidia GeForce GTX 660, nVidia Jetson TX2, nVidia Quadro 6000, Tesla K80, Thesis

May 12, 2019 by hgpu

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Dominik Ernst, Georg Hager, Jonas Thies, Gerhard Wellein

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Tags: Benchmarking, Code generation, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Package, Performance, Tesla V100

May 12, 2019 by hgpu

Charactering and Detecting CUDA Program Bugs

Mingyuan Wu, Husheng Zhou, Lingming Zhang, Cong Liu, Yuqun Zhang

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Tags: Computer science, CUDA, LLVM, nVidia, Package, Software Engineering

May 8, 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

Accelerating Deterministic and Stochastic Binarized Neural Networks on FPGAs Using OpenCL

OpenDNN: An Open-source, cuDNN-like Deep Learning Primitive Library

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

A Unified Approach to Variable Renaming for Enhanced Vectorization

An optimizing multi-platform source-to-source compiler framework for the NEURON MODeling Language

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

Predictable GPGPU Computing in DNN-Driven Autonomous Systems

Performance Engineering for a Tall & Skinny Matrix Multiplication Kernel on GPUs

Charactering and Detecting CUDA Program Bugs

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

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