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ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

Fabian Knorr, Peter Thoman, Thomas Fahringer

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Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, Package, SYCL, Tesla V100

August 8, 2021 by hgpu

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Jiannan Tian, Sheng Di, Xiaodong Yu, Cody Rivera, Kai Zhao, Sian Jin, Yunhe Feng, Xin Liang, Dingwen Tao, Franck Cappello

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Tags: Algorithms, Compression, Computer science, CUDA, Heterogeneous systems, nVidia, Package, Tesla A100, Tesla V100

May 30, 2021 by hgpu

Efficient Video Compression via Content-Adaptive Super-Resolution

Mehrdad Khani, Vibhaalakshmi Sivaraman, Mohammad Alizadeh

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Tags: Compression, Computer science, Deep learning, Neural networks, nVidia, Package, Tesla V100

April 11, 2021 by hgpu

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

Sian Jin, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

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Tags: Compression, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 22, 2020 by hgpu

Parallel acceleration of CPU and GPU range queries over large data sets

Mitchell Nelson, Zachary Sorenson, Joseph M. Myre, Jason Sawin, David Chiu

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Tags: Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080

August 9, 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

Inline Vector Compression for Computational Physics

Will Trojak, Freddie Witherden

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Tags: Compression, Computational Physics, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan V, Package, Physics, Tesla V100

March 8, 2020 by hgpu

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Sangkyun Lee, Jeonghyun Lee

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Tags: Algorithms, ARM, Compression, Computer science, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Package

May 5, 2019 by hgpu

Surface Compression Using Dynamic Color Palettes

Ayub A. Gubran, Felix Huang, Tor M. Aamodt

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Tags: 3D Graphics and Realism, Compression, OpenGL, Rendering

March 24, 2019 by hgpu

Hierarchical Matrix Operations on GPUs: Matrix-Vector Multiplication and Compression

Wajih Halim Boukaram, George Turkiyyah, David E. Keyes

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Tags: Compression, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Tesla P100

February 10, 2019 by hgpu

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

Sian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, Franck Cappello

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Tags: Compression, Computer science, Deep learning, Neural networks, nVidia, Performance, Tesla V100

February 3, 2019 by hgpu

A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

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Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

High Performance GPU Code Generation for Matrix-Matrix Multiplication using MLIR: Some Early Results

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

Performance Portability and Evaluation of Heterogeneous Components of SeisSol Targeted to Upcoming Intel HPC GPUs

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

Parallel time integration using Batched BLAS (Basic Linear Algebra Subprograms) routines

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

ScaleHLS Project

ScaleHLS: Scalable High-Level Synthesis through MLIR

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high performance computing on graphics processing units: hgpu.org

ndzip-gpu: Efficient Lossless Compression of Scientific Floating-Point Data on GPUs

cuSZ(x): Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Efficient Video Compression via Content-Adaptive Super-Resolution

A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

Parallel acceleration of CPU and GPU range queries over large data sets

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

Inline Vector Compression for Computational Physics

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Surface Compression Using Dynamic Color Palettes

Hierarchical Matrix Operations on GPUs: Matrix-Vector Multiplication and Compression

DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

A Framework for Fast and Efficient Neural Network Compression

Recent source codes

HPCToolkit

NVPTX-SPIR-V Translator

Taichi Lattice Boltmann

WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

MLIR: Multi-Level Intermediate Representation

SeisSol: High Resolution Simulation os Seismic Wave Propagation in Realistic Media with Complex Geomerty

perf4sight: A toolflow to model CNN training performance on Edge GPUs

Parament: Parallelized Matrix Exponentiation for Numerical Time evolution

ndzip: A High-Throughput Parallel Lossless Compressor for Scientific Data

ScaleHLS Project

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