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Programming on Parallel Machines Norm Matloff University of California, Davis GPU, Multicore, Clusters and More
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Document Date: 2017-12-04 01:44:46

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Los Angeles / /

Company

Use Parallel Systems / Message-Passing Systems / GPU / NUMA Systems / Shared-Memory Systems / IFIP Working Group / Synchronization Hardware / NVIDIA / Creative Commons / SMP Systems / /

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United States / /

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Employment Change / /

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Parallel Machines Norm Matloff University of California / University of California at Davis / /

IndustryTerm

statistical computing / parallel processing / communications latency / Web tutorials / database software developer / memory/network / Web Outlinks / database software security / /

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San Gabriel Valley / /

OperatingSystem

Linux / /

Organization

UCLA / University of California / Distinguished Teaching Award and Distinguished Public Service / Department of Statistics / University of California / Davis / UCD Computer Science Department / United Nations Educational Scientific and Cultural Organization / Parallel Machines Norm Matloff University / /

Person

Lars Seeman / Mikel McDaniel / Matt Butner / Marc Sosnick / Bill Hsu / Peter Salzman / Norm Matloff / Johan Wikstr / Stuart Hansen / Richard Minner / Sameer Khan / Stuart Ambler / /

Position

professor of statistics / text editor / author / Professor / professor of computer science / software developer / statistical consultant / Programmer / former database software developer / member / /

ProgrammingLanguage

R / C / Python / LaTeX / C++ / /

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California / /

RadioStation

3.1 What / Wait / Fine Tuning / /

Technology

Dijkstra Shortest-Path Algorithm / Linux / Shared Memory / MESI Cache Coherency Protocol / 60 3.8 Multicore Chips / pdf / load balancing / 3.2 Iterative Algorithms / http / virtual memory / parallel processing / /

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http /

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