Suh et al., 2013 - Google Patents

Accelerating MATLAB with GPU computing: a primer with examples

Suh et al., 2013

Document ID: 8836176673376692861
Author: Suh J; Kim Y
Publication year: 2013

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Snippet

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing …

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Publication	Publication Date	Title
Suh et al.	2013	Accelerating MATLAB with GPU computing: a primer with examples
Anzt et al.	2022	Ginkgo: A modern linear operator algebra framework for high performance computing
Farber	2016	Parallel programming with OpenACC
Chandrasekaran et al.	2017	OpenACC for programmers: concepts and strategies
Löff et al.	2021	The NAS parallel benchmarks for evaluating C++ parallel programming frameworks on shared-memory architectures
Rupp et al.	2016	ViennaCL---linear algebra library for multi-and many-core architectures
Abadi et al.	2016	Tensorflow: Large-scale machine learning on heterogeneous distributed systems
KR101962484B1 (en)	2019-03-26	Extensible data parallel semantics
Al-Mouhamed et al.	2020	A review of CUDA optimization techniques and tools for structured grid computing
Bruce et al.	2021	Enabling reproducible and agile full-system simulation
US8260598B2 (en)	2012-09-04	Size vector sharing in code generated for variable-sized signals
Nugteren et al.	2013	Algorithmic species: A classification of affine loop nests for parallel programming
Pemberton et al.	2021	Firemarshal: Making hw/sw co-design reproducible and reliable
Sandrieser et al.	2012	Using explicit platform descriptions to support programming of heterogeneous many-core systems
Mantas et al.	2016	An introduction to GPU computing for numerical simulation
Mehta et al.	2020	Evaluating performance portability of openmp for snap on nvidia, intel, and amd gpus using the roofline methodology
Vaidya	2018	Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective techniques for processing complex image data in real time using GPUs
Ernstsson et al.	2023	Assessing application efficiency and performance portability in single-source programming for heterogeneous parallel systems
Rodriguez-Gutiez et al.	2019	Toward a BLAS library truly portable across different accelerator types: E. Rodriguez-Gutiez et al.
Carratalá-Sáez et al.	2023	Open SYCL on heterogeneous GPU systems: A case of study
Calvert	2010	Parallelisation of java for graphics processors
Sandrieser et al.	2011	Explicit platform descriptions for heterogeneous many-core architectures
GÖBEL et al.	2022	G: A modern linear operator algebra framework for high performance computing
Athrij	2024	Vectorizing Memory Access on HammerBlade Architecture
Banchelli et al.	2024	Batched DGEMMs for scientific codes running on long vector architectures

Suh et al., 2013 - Google Patents

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