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Tips for the scientific programmer

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Title
Tips for the scientific programmer
Subtitle
Case studies for parallelism, data storage, memory and performance
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118
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CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor and the work or content is shared also in adapted form only under the conditions of this
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Abstract
This is a talk for people who need to perform large numeric calculations. They could be scientists, developers working in close contact with scientists, or even people working on finance and other quantitative fields. Such people are routinely confronted with issues like 1 parallelism: how to parallelize calculations efficiently 2 data: how to store and manage large amounts of data efficiently 3 memory: how to avoid running out of memory 4 performance: how to be fast The goal of the talk is to teach some lessons learned after several years of doing numeric simulations in a context were micro-optimizations are the least important factor, while overall architecture, design choices and good algorithms are of paramount importance.
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