high performance computing on graphics processing units: hgpu.org

hgpu.org » Mixed precision

Efficient Quantized Sparse Matrix Operations on Tensor Cores

Shigang Li, Kazuki Osawa, Torsten Hoefler

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Mixed precision, nVidia, nVidia A100, Package, Sparse matrix, Tesla V100

October 2, 2022 by hgpu

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Moritz Lehmann, Mathias J. Krause, Giorgio Amati, Marcello Sega, Jens Harting, Stephan Gekle

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Tags: AMD Radeon Instinct MI100, AMD Radeon VI, ATI, Fluid dynamics, lattice Boltzmann, Mixed precision, nVidia, OpenCL, Tesla K20, Tesla K40, Tesla K80, Tesla P100, Tesla V100

December 19, 2021 by hgpu

Mixed precision in Graphics Processing Unit

Quentin Gallouédec

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Tags: Computer science, Machine learning, Mixed precision, Neural networks, nVidia, Tesla V100

October 31, 2021 by hgpu

A Study of Mixed Precision Strategies for GMRES on GPUs

Jennifer A. Loe, Christian A. Glusa, Ichitaro Yamazaki, Erik G. Boman, Sivasankaran Rajamanickam

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, performance portability, Tesla V100

September 19, 2021 by hgpu

tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Binrui Li, Shenggan Cheng, James Lin

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Tags: Computer science, FFT, Mixed precision, nVidia, nVidia DGX-2, nVidia DGX-A100, Tesla V100

May 2, 2021 by hgpu

Mixed-Precision Embedding Using a Cache

Jie (Amy)Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang, Andrew Tulloch

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Tags: Artificial intelligence, Computer science, CUDA, Machine learning, Mixed precision, nVidia, Tesla V100

October 25, 2020 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

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Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Orestis Zachariadis, Nitin Satpute, Juan Gómez-Luna, Joaquín Olivares

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2070, nVidia Titan RTX, Package, Performance, Sparse matrix

October 4, 2020 by hgpu

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Mawussi Zounon, Nicholas J. Higham, Craig Lucas, Françoise Tisseur

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Tags: Algorithms, Computer science, CUDA, Factorization, Mixed precision, nVidia, Tesla P100, Tesla V100

September 27, 2020 by hgpu

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang

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Tags: Computational Physics, CUDA, HPC, Machine learning, Mixed precision, Molecular dynamics, Molecular simulation, MPI, nVidia, OpenMP, Physics, Tesla V100

May 10, 2020 by hgpu

Tools for Reduced Precision Computation: A Survey

Stefano Cherubin, Giovanni Agosta

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Tags: Approximate computing and storage, Computer science, CUDA, HPC, Mixed precision, nVidia, OpenCL, Precision, survey

May 3, 2020 by hgpu

Tensor Processing Units for Financial Monte Carlo

Francois Belletti, Davis King, Kun Yang, Roland Nelet, Yusef Shafi, Yi-Fan Chen, John Anderson

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Tags: Cloud, Deep learning, Finance, Mixed precision, Neural networks, nVidia, Option pricing, Tesla V100, TPU

June 12, 2019 by hgpu

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TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

A Study on Neural-based Code Summarization in Low-resource Settings

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

iGniter: an interference-aware GPU resource provisioning framework for achieving predictable performance of DNN inference in the cloud

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

pyGSL: A Scalable Library for Graph Structure Learning

pyGSL: A Graph Structure Learning Toolkit

See all packages

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high performance computing on graphics processing units: hgpu.org

Efficient Quantized Sparse Matrix Operations on Tensor Cores

On the accuracy and performance of the lattice Boltzmann method with 64-bit, 32-bit and novel 16-bit number formats

Mixed precision in Graphics Processing Unit

A Study of Mixed Precision Strategies for GMRES on GPUs

tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Mixed-Precision Embedding Using a Cache

Flexible Performant GEMM Kernels on GPUs

Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

Performance Evaluation of Mixed Precision Algorithms for Solving Sparse Linear Systems

Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning

Tools for Reduced Precision Computation: A Survey

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TorchOpt: an efficient library for differentiable optimization built upon PyTorch

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mt4g: Memory Topology for GPUs

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