What scientists must know about hardware to write fast code (2020)
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What scientists must know about hardware to write fast code (2020)
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Re: What scientists must know about hardware to write fast code (2020)
#2This subject is taught in undergrad computer architecture courses along with machine coding. As an EE, I learned it in grad school.
Re: What scientists must know about hardware to write fast code (2020)
#3Related:
What scientists must know about hardware to write fast code (2020) - https://news.ycombinator.com/item?id=29601342 - Dec 2021 (29 comments)
Re: What scientists must know about hardware to write fast code (2020)
#4This subject is taught in undergrad computer architecture courses along with machine coding. As an EE, I learned it in grad school.
Not everyone does.
Re: What scientists must know about hardware to write fast code (2020)
#5This subject is taught in undergrad computer architecture courses along with machine coding. As an EE, I learned it in grad school.
Congratulations. Studying one field means you know that field.
This link is not meant for you. It is meant for a scientist, and most scientists do not also have an EE degree or CS degree.
How much graduate level biology, oceanography, physics, geology, chemistry, meteorology, or other scientific field do you know?
All of those have subfields where computational performance is important. My experience is scientists are more likely to pick up the software skills than EEs are willing to pick up the science background. (In part because scientific software development generally pays less well than commercial software development.)
Re: What scientists must know about hardware to write fast code (2020)
#6It is quite refreshing to see software optimization be explained so simply and elegantly.
Re: What scientists must know about hardware to write fast code (2020)
#7Solid post. It also shows how powerful Julia is: allowing to operate at different levels of abstractions (down to seeing the assembly) using the same set of tools.
Re: What scientists must know about hardware to write fast code (2020)
#8Related: What scientists must know about hardware to write fast code (2020) - https://news.ycombinator.com/item?id=29601342 - Dec 2021 (29 comments)
FYI the underlying link in that previous discussion post seems to be defunct and kind of suspicious.
Re: What scientists must know about hardware to write fast code (2020)
#9This subject is taught in undergrad computer architecture courses along with machine coding. As an EE, I learned it in grad school.
Congratulations. Studying one field means you know that field. This link is not meant for you. It is meant for a scientist, and most scientists do not also have an EE degree or CS degree. How much graduate level biology, oceanography, physics, geology, chemistry, meteorology, or other scientific field do you know? All of those have subfields where computational performance is important. My experience is scientists ar…
Math was always a must for a scientist, todays computer science is also a must. The study programmes should reflect that.
Re: What scientists must know about hardware to write fast code (2020)
#10Solid post. It also shows how powerful Julia is: allowing to operate at different levels of abstractions (down to seeing the assembly) using the same set of tools.
I feel the biggest misleading statements around Julia is that for true speed you can somehow ignore the lower abstractions, or that there is some kind of free lunch, but always what you gain in performance you'll spend in development time. Julia has some neat tricks, but they are not generally and universally applicable at least not like other languages. I dunno. These arguments against Julia are many, but I'm still appalled they have so many handy wavy misleading statements in just their introductory text, I don't think it be less than a decade before they recover.