6 ms·

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.
        adgjlsfhk1 3y ago
        That's exactly the point. People are changing their coding style to work around the fact that loops in the language are slow.
        th0ma5 3y ago
        This existed quite a while before Julia, how is it that Julia claims this differentiation then? "We pride ourselves by not using the slow thing you're probably not actually using."
        eesmith 3y ago
        Because Julia claims to get usability and performance in one language, rather than the two Python needs (Python for the user-facing API, C/C++/Fortran/etc. for performance). This should also help with optimization, and a larger amount of code can be optimized together, while (C)Python can only optimize up to the Python/C boundary.
        th0ma5 3y ago
        I can't imagine anything worse than something that is not appropriate for the abstraction... eg https://en.m.wikipedia.org/wiki/Leaky_abstraction https://en.m.wikipedia.org/wiki/Leaky_abstraction Again redefining these things here... if the language has tools for all of inline raw chip specific instructions, compiler optimized versions of those instructions, virtualized and then optimized instructions, a jit compiler, and then also high level interpreter operations, then how can it be in anyway an encompassing system and how can that be coherent among all levels without requiring someone to know all levels at which point, I'm going to say no thanks and stick to tools at their respective levels of abstraction where I can reason them to be coherent rather than a dynamically allocated string which is sometimes tied to only working on amd64 because someone wanted an assembler way of pattern matching for some reason. Maybe within the scope of scientific computing this seems logical if you just restrict yourself to matrix multiplication or whatever, but I don't see how that makes a "language" and it can be coherent, composable, etc... I've seen a ton of examples, however, over the years of "stunt driven" technological advantages that people thought would be interesting but turned out to be the wrong solution for the wrong problem, but none that claim to break the laws of physics and reason with their evangelism than Julia. Even this article starts with "you must understand the quirks" and I hope that isn't a goal for their design because they are also claiming that I shouldn't have to know the quirks so which is it.
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]

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.
      - adgjlsfhk1 3y ago
        That's exactly the point. People are changing their coding style to work around the fact that loops in the language are slow.
        th0ma5 3y ago
        This existed quite a while before Julia, how is it that Julia claims this differentiation then? "We pride ourselves by not using the slow thing you're probably not actually using."
        eesmith 3y ago
        Because Julia claims to get usability and performance in one language, rather than the two Python needs (Python for the user-facing API, C/C++/Fortran/etc. for performance). This should also help with optimization, and a larger amount of code can be optimized together, while (C)Python can only optimize up to the Python/C boundary.
        th0ma5 3y ago
        I can't imagine anything worse than something that is not appropriate for the abstraction... eg https://en.m.wikipedia.org/wiki/Leaky_abstraction https://en.m.wikipedia.org/wiki/Leaky_abstraction Again redefining these things here... if the language has tools for all of inline raw chip specific instructions, compiler optimized versions of those instructions, virtualized and then optimized instructions, a jit compiler, and then also high level interpreter operations, then how can it be in anyway an encompassing system and how can that be coherent among all levels without requiring someone to know all levels at which point, I'm going to say no thanks and stick to tools at their respective levels of abstraction where I can reason them to be coherent rather than a dynamically allocated string which is sometimes tied to only working on amd64 because someone wanted an assembler way of pattern matching for some reason. Maybe within the scope of scientific computing this seems logical if you just restrict yourself to matrix multiplication or whatever, but I don't see how that makes a "language" and it can be coherent, composable, etc... I've seen a ton of examples, however, over the years of "stunt driven" technological advantages that people thought would be interesting but turned out to be the wrong solution for the wrong problem, but none that claim to break the laws of physics and reason with their evangelism than Julia. Even this article starts with "you must understand the quirks" and I hope that isn't a goal for their design because they are also claiming that I shouldn't have to know the quirks so which is it.

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]