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This subject is taught in undergrad computer architecture courses along with machine coding. As an EE, I learned it in grad school.

tempodox 3y ago
Not everyone does.
eesmith 3y ago
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.)
- mjan22640 3y ago
  Math was always a must for a scientist, todays computer science is also a must. The study programmes should reflect that.
  - th0ma5 3y ago
    I agree with this sentiment, like the majority of CS people are telling statisticians that a lot of Julia remains a kind of snake oil or otherwise mystical thinking, it is very unfortunate. Even in the first page of the documentation "No need to vectorize code for performance; devectorized code is fast" is some kind of category error redefinition of how programming languages work in my opinion.
    - newswasboring 3y ago
      > Even in the first page of the documentation "No need to vectorize code for performance; devectorized code is fast" is some kind of category error redefinition of how programming languages work in my opinion. Can you elaborate a bit? I don't really get what you are trying to say.
      - th0ma5 3y ago
        If the code can easily be vectorized then it has the potential to vectorize it incorrectly or there is some automation happening that is hidden. If they're just saying their non-vectorized operations are just as quick, then how quick could true vectorization be. Also, this is how Octave, NumPy, Matlab, R, etc work by making vectorized math operations happen with whole matrices using statements that look like simple non-vector operations. Further, usually when people are having these kinds of issues it's because they started with a non-parallelizable concept of their problem in our trying to redo it... And no amount of magic is going to fix a bad concept of the problem space.
        adgjlsfhk1 3y ago
        I think the problem here is that there are two very different meanings of "vectorized" at play. The first (and what the Julia docs are talking about here) is the pattern of writing "vector-operations" (i.e. rather than writing a loop, writing an expression that works across an entire array). The second meaning is using SIMD instructions (e.g. AVX2). What the julia docs are trying to say is unlike in languages like Python/R/Matlab where loops have a high overhead do to an interpreter, in Julia, loops are fast because the language is compiled. There are lots of algorithms that are easy to express in an iterative fashion that are pretty much impossible to vectorize efficiently (dataflow analyais/differential equations etc). The docs here aren't trying to talk about SIMD instructions at all here. (although Julia/LLVM are pretty good at producing SIMD instructions from loops where possible).
        th0ma5 3y ago
        But no one in science uses Python loops for example, they use NumPy / Jax / Polars etc so that is an unfair and disingenuous comparison.
  - eesmith 3y ago
    "Math" is such a wide topic that you certainly must qualify your statement. The standard entomologist curriculum does not require calculus, while a physics curriculum does. Both produce scientists. (For example, https://cals.cornell.edu/education/degrees-programs/entomology-major-minor https://cals.cornell.edu/education/degrees-programs/entomolo... under "Major Requirements" says "One semester of college statistics or biometry", and the listed physics requirement doesn't require calculus.) On the other hand, an entomologist interested in population ecology may need to know differential equations. Your use of "study program" suggests your experience is at the undergrad level, and not at the grad school level, which is how most scientists I know got their training. At the undergrad level the study programs do reflect what's needed for a solid education. If a student is interested in computational biology, that program will emphasize taking more CS courses than the program for a student interested in marine biology. But at the grad level, the "study program" is much less formalized. You might take graduate level classes the first couple of years, but then you are expected to pick up the missing bits on your own. Once you have your PhD and are a working scientist, you rarely have the luxury of following any study program. And if you've been a scientist for 20 years, any CS training you had likely did not cover SIMD, and emphasized practices which are no longer relevant. (For example, the link points out "That advice [about HDDs] is mostly outdated today [with SSDs]".) Those latter categories are who the linked-to piece is for, not undergrads in a well-defined study program.
    - AtlasBarfed 3y ago
      The article basically implies that some non-professional coder will be doing assembly and basically doing the work of an optimizing compiler. I think the point of the parent is that if you are at this point already, and you're in an academic setting, you might as well real a full computer architecture textbook front to back. I would be curious to know of all the "scientific coders" what percentage of them understood the entire article. I'd be similarly curious how much your typical "bootcamp" developers would understand of it. I know everything presented, so it basically comes off as a "lecture notes" for someone that already knows it. Someone that doesn't understand SIMD, CPU fundamentals, assembly, and compilers, I'd imagine their eyes would glaze right when the assembly code appeared. And while SSDs are MUCH FASTER than HDDs, the basics of interacting with storage is the same, just that rather than waiting a million years for data to arrive from the CPU's perspective, it comes in 10,000s of years. Latency numbers all programmers should be aware of: https://gist.github.com/jboner/2841832 https://gist.github.com/jboner/2841832
  - deleted 3y ago
    [deleted]
jschveibinz 3y ago
Well, apparently I offended a lot of people by merely providing a piece of information about computer architecture curriculum. I apologize for commenting.
- willprice89 3y ago
  With no context about your emotional state, it came off as a bit brusque, and I think most people read comments like that in a snarky, condescending tone. If you had prepended the comment with something like "I love this topic!" to show enthusiasm or approval, you probably would have gotten a much different response.
aeturnum 3y ago
While I could certainly put most of this together from my undergrad CS education, I would not say this "subject [was] taught" to me during undergrad. Instead, as with much of undergrad, you get pieces of it along the way - but collecting it together and writing for a somewhat-lay audience has a lot of value. This is also more up to date than my under grad education from ~14 years ago! It has clear explanations for things like Hyperthreading, which existed at the time I was in undergrad, but hadn't really made its way into the curriculum yet.

tempodox 3y ago

Not everyone does.

eesmith 3y ago

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.)

mjan22640 3y ago
Math was always a must for a scientist, todays computer science is also a must. The study programmes should reflect that.
- th0ma5 3y ago
  I agree with this sentiment, like the majority of CS people are telling statisticians that a lot of Julia remains a kind of snake oil or otherwise mystical thinking, it is very unfortunate. Even in the first page of the documentation "No need to vectorize code for performance; devectorized code is fast" is some kind of category error redefinition of how programming languages work in my opinion.
  - newswasboring 3y ago
    > Even in the first page of the documentation "No need to vectorize code for performance; devectorized code is fast" is some kind of category error redefinition of how programming languages work in my opinion. Can you elaborate a bit? I don't really get what you are trying to say.
    - th0ma5 3y ago
      If the code can easily be vectorized then it has the potential to vectorize it incorrectly or there is some automation happening that is hidden. If they're just saying their non-vectorized operations are just as quick, then how quick could true vectorization be. Also, this is how Octave, NumPy, Matlab, R, etc work by making vectorized math operations happen with whole matrices using statements that look like simple non-vector operations. Further, usually when people are having these kinds of issues it's because they started with a non-parallelizable concept of their problem in our trying to redo it... And no amount of magic is going to fix a bad concept of the problem space.
      - adgjlsfhk1 3y ago
        I think the problem here is that there are two very different meanings of "vectorized" at play. The first (and what the Julia docs are talking about here) is the pattern of writing "vector-operations" (i.e. rather than writing a loop, writing an expression that works across an entire array). The second meaning is using SIMD instructions (e.g. AVX2). What the julia docs are trying to say is unlike in languages like Python/R/Matlab where loops have a high overhead do to an interpreter, in Julia, loops are fast because the language is compiled. There are lots of algorithms that are easy to express in an iterative fashion that are pretty much impossible to vectorize efficiently (dataflow analyais/differential equations etc). The docs here aren't trying to talk about SIMD instructions at all here. (although Julia/LLVM are pretty good at producing SIMD instructions from loops where possible).
        th0ma5 3y ago
        But no one in science uses Python loops for example, they use NumPy / Jax / Polars etc so that is an unfair and disingenuous comparison.
- eesmith 3y ago
  "Math" is such a wide topic that you certainly must qualify your statement. The standard entomologist curriculum does not require calculus, while a physics curriculum does. Both produce scientists. (For example, https://cals.cornell.edu/education/degrees-programs/entomology-major-minor https://cals.cornell.edu/education/degrees-programs/entomolo... under "Major Requirements" says "One semester of college statistics or biometry", and the listed physics requirement doesn't require calculus.) On the other hand, an entomologist interested in population ecology may need to know differential equations. Your use of "study program" suggests your experience is at the undergrad level, and not at the grad school level, which is how most scientists I know got their training. At the undergrad level the study programs do reflect what's needed for a solid education. If a student is interested in computational biology, that program will emphasize taking more CS courses than the program for a student interested in marine biology. But at the grad level, the "study program" is much less formalized. You might take graduate level classes the first couple of years, but then you are expected to pick up the missing bits on your own. Once you have your PhD and are a working scientist, you rarely have the luxury of following any study program. And if you've been a scientist for 20 years, any CS training you had likely did not cover SIMD, and emphasized practices which are no longer relevant. (For example, the link points out "That advice [about HDDs] is mostly outdated today [with SSDs]".) Those latter categories are who the linked-to piece is for, not undergrads in a well-defined study program.
  - AtlasBarfed 3y ago
    The article basically implies that some non-professional coder will be doing assembly and basically doing the work of an optimizing compiler. I think the point of the parent is that if you are at this point already, and you're in an academic setting, you might as well real a full computer architecture textbook front to back. I would be curious to know of all the "scientific coders" what percentage of them understood the entire article. I'd be similarly curious how much your typical "bootcamp" developers would understand of it. I know everything presented, so it basically comes off as a "lecture notes" for someone that already knows it. Someone that doesn't understand SIMD, CPU fundamentals, assembly, and compilers, I'd imagine their eyes would glaze right when the assembly code appeared. And while SSDs are MUCH FASTER than HDDs, the basics of interacting with storage is the same, just that rather than waiting a million years for data to arrive from the CPU's perspective, it comes in 10,000s of years. Latency numbers all programmers should be aware of: https://gist.github.com/jboner/2841832 https://gist.github.com/jboner/2841832
- deleted 3y ago
  [deleted]

jschveibinz 3y ago

Well, apparently I offended a lot of people by merely providing a piece of information about computer architecture curriculum. I apologize for commenting.

willprice89 3y ago
With no context about your emotional state, it came off as a bit brusque, and I think most people read comments like that in a snarky, condescending tone. If you had prepended the comment with something like "I love this topic!" to show enthusiasm or approval, you probably would have gotten a much different response.

aeturnum 3y ago

While I could certainly put most of this together from my undergrad CS education, I would not say this "subject [was] taught" to me during undergrad. Instead, as with much of undergrad, you get pieces of it along the way - but collecting it together and writing for a somewhat-lay audience has a lot of value. This is also more up to date than my under grad education from ~14 years ago! It has clear explanations for things like Hyperthreading, which existed at the time I was in undergrad, but hadn't really made its way into the curriculum yet.