high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Abhinav Muta, Prabhu Ramachandran, Pawan Negi

View

Download (PDF)

Source codes

Tags: Benchmarking, Computational Physics, Fluid dynamics, Laplace and Poisson equation, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1070 Ti, OpenCL, Package, Poisson equation

August 11, 2019 by hgpu

Performance Comparison for Neuroscience Application Benchmarks

Andreas Herten, Thorsten Hater, Wouter Klijn, Dirk Pleiter

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Neuroscience, nVidia, Performance, Tesla P100, Tesla V100

August 11, 2019 by hgpu

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Carl Yang, Aydin Buluc, John D. Owens

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Package, Tesla K40

August 11, 2019 by hgpu

Parallelization of Coherent Point Drift for patient registration

Erik Smistad

View

Download (PDF)

Source codes

Tags: Algorithms, AMD Radeon R9 Fury, ATI, Computer science, OpenCL, Package, Thesis

August 5, 2019 by hgpu

Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

Thomas Faict, Erik H. D'Hollander, Bart Goossens

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, FPGA, Heterogeneous systems, OpenCL

August 5, 2019 by hgpu

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

Luiz H. Sena, Iury V. Bessa, Mikhail R. Gadelha, Lucas C. Cordeiro, Edjard Mota

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Neural networks, nVidia, Package

August 5, 2019 by hgpu

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

Shaoyi Peng, Sheldon X.-D. Tan

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Sparse matrix

August 5, 2019 by hgpu

GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

July 28, 2019 by hgpu

NNS: The Case For Neural Network-based Sorting

Xiao-ke Zhu, Tai-ning Chen, Jing He, Wei Zhou

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Sorting, TensorFlow

July 28, 2019 by hgpu

Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Yu (Emma)Wang, Gu-Yeon Wei, David Brooks

View

Download (PDF)

Tags: Benchmarking, Cloud, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100, TPU

July 28, 2019 by hgpu

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

Daniel Nichols, Nathalie-Sofia Tomov, Frank Betancourt, Stanimire Tomov, Kwai Wong, Jack Dongarra

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, HPC, Linear Algebra, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1050Ti, Package

July 26, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Programming

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Simple Iterative Incompressible Smoothed Particle Hydrodynamics

Performance Comparison for Neuroscience Application Benchmarks

GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

Parallelization of Coherent Point Drift for patient registration

Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL

Incremental Bounded Model Checking of Artificial Neural Networks in CUDA

GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

GPU-Accelerated Atari Emulation for Reinforcement Learning

NNS: The Case For Neural Network-based Sorting

MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing

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)