high performance computing on graphics processing units: hgpu.org

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Deep Learning for Compilers

Chris Cummins

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Tags: AMD Radeon HD 7970, ATI, Benchmarking, Compilers, Computer science, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 970, OpenCL, Package, Thesis

April 5, 2020 by hgpu

PyMatting: A Python Library for Alpha Matting

Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling

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Tags: Computer vision, CUDA, Image processing, nVidia, OpenCL, Package, PyOpenCL, Python

April 5, 2020 by hgpu

Parallelization of the Honeybee Search Algorithm for Object Tracking

Oscar E. Perez-Cham, Cesar Puente, Carlos Soubervielle-Montalvo, Gustavo Olague, Carlos A. Aguirre-Salado, Alberto S. Nuñez-Varela

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Tags: AMD Radeon R9 270, Artificial intelligence, ATI, Computer science, Computer vision, nVidia, nVidia GeForce GTX 1050, OpenCL, Particle swarm optimization

April 5, 2020 by hgpu

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Balazs Nagy, Philipp Foehn, Davide Scaramuzza

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Tags: Algorithms, Computer vision, CUDA, Image processing, nVidia, nVidia GeForce GTX 960 M, nVidia Jetson TX2, OpenCV, Package

April 5, 2020 by hgpu

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

Sian Jin, Pascal Grosset, Christopher M. Biwer, Jesus Pulido, Jiannan Tian, Dingwen Tao, James Ahrens

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Tags: Astrophysics, Compression, Computational Physics, Cosmology, CUDA, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2080 Ti, nVidia Quadro P 6000, Package, Physics, Tesla K80, Tesla P100, Tesla V100

April 5, 2020 by hgpu

Large-Scale Data Computing Performance Comparisons on SYCL Heterogeneous Parallel Processing Layer Implementations

Woosuk Shin, Kwan-Hee Yoo, Nakhoon Baek

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Tags: Computer science, CUDA, DSL, Heterogeneous systems, nVidia, nVidia GeForce GTX 960, OpenCL, SYCL

March 29, 2020 by hgpu

Characterizing Optimizations to Memory Access Patterns using Architecture-Independent Program Features

Aditya Chilukuri, Josh Milthorpe, Beau Johnston

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Tags: Computer science, Heterogeneous systems, Memory, nVidia, OpenCL, Performance, Tesla P100

March 29, 2020 by hgpu

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

Nicolas Weber, Felipe Huici

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V100, nVidia Quadro P 4000, PyTorch

March 29, 2020 by hgpu

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather

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Tags: Computer science, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 970, OpenCL, Package, Performance, Programming Languages

March 29, 2020 by hgpu

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Bastian Hagedorn, Archibald Samuel Elliott, Henrik Barthels, Rastislav Bodik, Vinod Grover

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce RTX 2080 Ti, nVidia Quadro GV100, Programming Languages

March 22, 2020 by hgpu

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Peng Zhang, Jianbin Fang, Canqun Yang, Chun Huang, Tao Tang, Zheng Wang

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Tags: Analytical model, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, OpenMP, Package

March 22, 2020 by hgpu

Learnergy: Energy-based Machine Learners

Mateus Roder, Gustavo Henrique de Rosa, João Paulo Papa

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Tags: Computer science, CUDA, Deep learning, Energy-efficient computing, Machine learning, Neural networks, nVidia, Package, Python, PyTorch

March 22, 2020 by hgpu

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

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Programming

Deep Learning for Compilers

PyMatting: A Python Library for Alpha Matting

Parallelization of the Honeybee Search Algorithm for Object Tracking

Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

Understanding GPU-Based Lossy Compression for Extreme-Scale Cosmological Simulations

Large-Scale Data Computing Performance Comparisons on SYCL Heterogeneous Parallel Processing Layer Implementations

Characterizing Optimizations to Memory Access Patterns using Architecture-Independent Program Features

SOL: Effortless Device Support for AI Frameworks without Source Code Changes

ProGraML: Graph-based Deep Learning for Program Optimization and Analysis

Fireiron: A Scheduling Language for High-Performance Linear Algebra on GPUs

Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures

Learnergy: Energy-based Machine Learners

Recent source codes

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Cluser of smartphones for edge computing application using TensorFlow

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

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