4 ms·

I'm not bothered by "hello world" performance myself, and I my recent issues with Julia have been caused by rapid development meaning that when I had to put it

by sgt101 11y ago

I'm not bothered by "hello world" performance myself, and I my recent issues with Julia have been caused by rapid development meaning that when I had to put it down for a few months lots of things I had done (because I'm mortal) stopped working. I wrote this off to "it's 0.x, getoverit". I've never tried complex text formatting in Julia either! My concerns are more focused on the type system (this I love) and performance for massive computation (I've still not managed to persuade my Hadoop admin to put julia images across our cluster, but I suppose I might win the argument one day!)

vitaut 11y ago
I wouldn't be bothered about it either if the language, like Java, targeted more the development of long-running services rather than interactive applications (with some exceptions like mobile). But as far as I can see it is advertised for use in interactive applications, possibly as a Python alternative, where responsiveness is important.
- sgt101 11y ago
  I haven't got the same perception - I think it's aimed at things like simulator and solvers. Those things tend to have front ends (that is true) but not front ends that support 100's of users.
dnautics 11y ago
Yeah the poor performance in this blog post is a total misunderstanding of why and how julia is performant. Is printing "hello world" fast really that important? OK. Then don't use Julia. You pay for it by having the compiler JIT the code in a highly optimized fashion. If you have actual numerical calculations that are compile-once, run-many-many-many-times, then you will see a huge performance benefit, amortizing the cost of expensive compilation and optimization that happens once at the beginning of the program cycle. The very title of what he links to "How To Make Python run as fast as Julia" betrays the problem. The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance - the system will do it out of the box. I'll have to disagree with the notion that Julia is hard to read. I'm currently deploying Julia to run automated hardware verification on a computer chip. Effectively, I've written a DSL using Julia macros that generates assembly code files, compiles, and executes it, and my coworkers (who do not use julia) have found it easy to read my code and understand what's going on. Far easier, in any case, than the equivalent C code using asm blocks. I do agree about the one-based indexing. I get it, it's what matlab does. But it would be nice to say, be able to throw an option at the top of a program that forces the appropriate indexing.
- Xcelerate 11y ago
  > If you have actual numerical calculations that are compile-once, run-many-many-many-times, then you will see a huge performance benefit I agree, and I haven't found anything like this in other (non-exotic) languages. I recently wrote a function using the @generated macro to produce Wigner-D matrices via the recursion relations. The function dispatches on the size of the matrix (using Type{Val{N}}) and after it compiles once for a particular value of N, all future calls are blazing fast (since the machine code is essentially just a long list of multiply and add instructions).
- acomjean 11y ago
  >The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance As someone who is new to python (for bioinformatics), and find python is a fine language... but.. The do it "this way not that way" method of implementation of the same algorithms to get it to run fast makes writing performant python a tedious exercise in research and profiling. The article cited suggests Cpython, numby and numpy [1] as ways to make it faster. Why not C using GPU acceleration as the time spent coding would probably be the same? Thats what I love about plain python, its fast to write and has some good data structures. I haven't tried Julia, but someday its on my list of languages to learn more about. [1]https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Meets_Julia_Micro_Performance?lang=en https://www.ibm.com/developerworks/community/blogs/jfp/entry...
  - dagw 11y ago
    Why not C using GPU acceleration as the time spent coding would probably be the same? As someone who does that sort of thing I can assure you it isn't. And when I do use C and GPU acceleration, doing so via cython and pyCUDA (and the myriad of libraries that build on cython and pyCUDA) saves massive amounts of time and effort. That being said I do agree that writing fast python is quite different from writing python, probably more so than in most other languages.
- pizlonator 11y ago
  But this is a false choice. Mature JIT-based systems give you the best of both worlds by starting in an interpreter and then switching to a JIT on a per-function basis as code gets hot.

by sgt101 11y ago

vitaut 11y ago

I wouldn't be bothered about it either if the language, like Java, targeted more the development of long-running services rather than interactive applications (with some exceptions like mobile). But as far as I can see it is advertised for use in interactive applications, possibly as a Python alternative, where responsiveness is important.

sgt101 11y ago
I haven't got the same perception - I think it's aimed at things like simulator and solvers. Those things tend to have front ends (that is true) but not front ends that support 100's of users.

dnautics 11y ago

Yeah the poor performance in this blog post is a total misunderstanding of why and how julia is performant. Is printing "hello world" fast really that important? OK. Then don't use Julia. You pay for it by having the compiler JIT the code in a highly optimized fashion. If you have actual numerical calculations that are compile-once, run-many-many-many-times, then you will see a huge performance benefit, amortizing the cost of expensive compilation and optimization that happens once at the beginning of the program cycle. The very title of what he links to "How To Make Python run as fast as Julia" betrays the problem. The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance - the system will do it out of the box. I'll have to disagree with the notion that Julia is hard to read. I'm currently deploying Julia to run automated hardware verification on a computer chip. Effectively, I've written a DSL using Julia macros that generates assembly code files, compiles, and executes it, and my coworkers (who do not use julia) have found it easy to read my code and understand what's going on. Far easier, in any case, than the equivalent C code using asm blocks. I do agree about the one-based indexing. I get it, it's what matlab does. But it would be nice to say, be able to throw an option at the top of a program that forces the appropriate indexing.

Xcelerate 11y ago
> If you have actual numerical calculations that are compile-once, run-many-many-many-times, then you will see a huge performance benefit I agree, and I haven't found anything like this in other (non-exotic) languages. I recently wrote a function using the @generated macro to produce Wigner-D matrices via the recursion relations. The function dispatches on the size of the matrix (using Type{Val{N}}) and after it compiles once for a particular value of N, all future calls are blazing fast (since the machine code is essentially just a long list of multiply and add instructions).
acomjean 11y ago
>The goal of Julia is to not have to do that sort of boilerplate/arcane tweaking to get really good performance As someone who is new to python (for bioinformatics), and find python is a fine language... but.. The do it "this way not that way" method of implementation of the same algorithms to get it to run fast makes writing performant python a tedious exercise in research and profiling. The article cited suggests Cpython, numby and numpy [1] as ways to make it faster. Why not C using GPU acceleration as the time spent coding would probably be the same? Thats what I love about plain python, its fast to write and has some good data structures. I haven't tried Julia, but someday its on my list of languages to learn more about. [1]https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Meets_Julia_Micro_Performance?lang=en https://www.ibm.com/developerworks/community/blogs/jfp/entry...
- dagw 11y ago
  Why not C using GPU acceleration as the time spent coding would probably be the same? As someone who does that sort of thing I can assure you it isn't. And when I do use C and GPU acceleration, doing so via cython and pyCUDA (and the myriad of libraries that build on cython and pyCUDA) saves massive amounts of time and effort. That being said I do agree that writing fast python is quite different from writing python, probably more so than in most other languages.
pizlonator 11y ago
But this is a false choice. Mature JIT-based systems give you the best of both worlds by starting in an interpreter and then switching to a JIT on a per-function basis as code gets hot.