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I think i can answer that, first of all Julia isnt as fast as C/C++/Nim etc. in most cases Julia is just fast in scientific computing that's all. (there is only

I think i can answer that, first of all Julia isnt as fast as C/C++/Nim etc. in most cases Julia is just fast in scientific computing that's all. (there is only one "scientific" benchmark on kostya benchmarks) Second to write very fast julia u need to knew a lot of "tricks" and in most cases u won't be doing it as easy as writing normal code. And all people writing this benchmark is measuring compilation time (XD?) or not including jitting time they could just look at code/readme for 5s before commenting. Julia is fast and can be as fast as C but not in all cases and not as easy at it seems.

socialdemocrat 6y ago
To be fair Julia gives you better tools to analyze your code and figure out how to write more efficient. Being able to look at all the steps a JIT compiler will perform on an individual function helps a lot in building an intuition about what you should and should not do while writing high performance Julia code
snicker7 6y ago
> Second to write very fast julia u need to knew a lot of "tricks" and in most cases u won't be doing it as easy as writing normal code. That's true in literally any language. Some languages require inlined assembly. Others require preprocessor directives. In almost all languages, you need to understand the difference between stack and heap, know how to minimize allocations, know how to minimize dynamic dispatch, know how to efficiently structure cache-friendly memory layouts. And of course, data structures & algorithms 101. In terms of performance, Julia provides the following: 1. Zero-cost abstractions. And since it has homoiconic macros, users can create their own zero-cost abstractions, e.g. AoS to SoA conversions, auto-vectorization. Managing the complexity-performance trade-off is critical. But you don't see that in micro-benchmarks. 2. Fast iteration speed. Julia is optimized for interactive computing. I can compile any function into its SSA form, LLVM bytecode, or native assembler. And I can inspect this in a Pluto notebook. Optimizing Julia is fun, which is less true in other languages.
- RyEgswuCsn 6y ago
  > That's true in literally any language. Some languages require inlined assembly. Others require preprocessor directives. In almost all languages, you need to understand the difference between stack and heap, know how to minimize allocations, know how to minimize dynamic dispatch, know how to efficiently structure cache-friendly memory layouts. And of course, data structures & algorithms 101. I think what s/he meant to say is that Julia is not "magically" faster than other languages. The real questions are: 1. Can unoptimised Julia code run as fast as unoptimised c/c++ code? I think the linked benchmark suggests this is not really the case. 2. Can optimised Julia code run faster than comparably (i.e. requiring similar amount of effort and expertise) optimised c/c++ code? If not, then why use Julia?
  - mbauman 6y ago
    > Julia is not "magically" faster than other languages That's somewhat true, and is at the end-point of some mismatched expectations when folks come to Julia. Julia is a high-level dynamic language whose semantics are conducive to creating the ~same performance as static languages. So if your unoptimized Julia program relies upon traditional "dynamic" features like `Any[]` arrays, then you should expect to see dynamic- (read: python-) like performance out of Julia. Julia should match performance of other dynamic languages here, but the complier doesn't have all the typical dynamic optimizations because, well, it's often easy to write your code in a manner that ends up hitting the happy path that gets the static-like performance. Conversely, if your dynamic language baseline is just glue to an optimized static library, then you should expect to see static-like (read: C/C++-like) performance out of your dynamic language. Julia really should match performance here, and if it doesn't, open an issue: it's a bug. Where Julia truly excels are the cases where you don't have a library implementation (like numpy) to lean on and find yourself writing a hot `for` loop in a dynamic language. Further, it excels at facilitating library creation, leading to more and more first-class ecosystems that are best-in-class like DiffEq.
    - snicker7 6y ago
      > So if your unoptimized Julia program relies upon traditional "dynamic" features like Dynamic dispatch is slow in any language, including C/C++ (provided that the compiler can't devirtualize the method). This is why such things are never done in an inner loop. In C++, its harder to "accidentally" use dynamic dispatch because you have to explicitly annotate a function as being virtual. In Julia, which is much more concise, type stability or instability is implicit. But it can be inspected statically via @code_warntype. Good IDE plug-ins can make it easier.

socialdemocrat 6y ago

To be fair Julia gives you better tools to analyze your code and figure out how to write more efficient. Being able to look at all the steps a JIT compiler will perform on an individual function helps a lot in building an intuition about what you should and should not do while writing high performance Julia code

snicker7 6y ago

> Second to write very fast julia u need to knew a lot of "tricks" and in most cases u won't be doing it as easy as writing normal code. That's true in literally any language. Some languages require inlined assembly. Others require preprocessor directives. In almost all languages, you need to understand the difference between stack and heap, know how to minimize allocations, know how to minimize dynamic dispatch, know how to efficiently structure cache-friendly memory layouts. And of course, data structures & algorithms 101. In terms of performance, Julia provides the following: 1. Zero-cost abstractions. And since it has homoiconic macros, users can create their own zero-cost abstractions, e.g. AoS to SoA conversions, auto-vectorization. Managing the complexity-performance trade-off is critical. But you don't see that in micro-benchmarks. 2. Fast iteration speed. Julia is optimized for interactive computing. I can compile any function into its SSA form, LLVM bytecode, or native assembler. And I can inspect this in a Pluto notebook. Optimizing Julia is fun, which is less true in other languages.

RyEgswuCsn 6y ago
> That's true in literally any language. Some languages require inlined assembly. Others require preprocessor directives. In almost all languages, you need to understand the difference between stack and heap, know how to minimize allocations, know how to minimize dynamic dispatch, know how to efficiently structure cache-friendly memory layouts. And of course, data structures & algorithms 101. I think what s/he meant to say is that Julia is not "magically" faster than other languages. The real questions are: 1. Can unoptimised Julia code run as fast as unoptimised c/c++ code? I think the linked benchmark suggests this is not really the case. 2. Can optimised Julia code run faster than comparably (i.e. requiring similar amount of effort and expertise) optimised c/c++ code? If not, then why use Julia?
- mbauman 6y ago
  > Julia is not "magically" faster than other languages That's somewhat true, and is at the end-point of some mismatched expectations when folks come to Julia. Julia is a high-level dynamic language whose semantics are conducive to creating the ~same performance as static languages. So if your unoptimized Julia program relies upon traditional "dynamic" features like `Any[]` arrays, then you should expect to see dynamic- (read: python-) like performance out of Julia. Julia should match performance of other dynamic languages here, but the complier doesn't have all the typical dynamic optimizations because, well, it's often easy to write your code in a manner that ends up hitting the happy path that gets the static-like performance. Conversely, if your dynamic language baseline is just glue to an optimized static library, then you should expect to see static-like (read: C/C++-like) performance out of your dynamic language. Julia really should match performance here, and if it doesn't, open an issue: it's a bug. Where Julia truly excels are the cases where you don't have a library implementation (like numpy) to lean on and find yourself writing a hot `for` loop in a dynamic language. Further, it excels at facilitating library creation, leading to more and more first-class ecosystems that are best-in-class like DiffEq.
  - snicker7 6y ago
    > So if your unoptimized Julia program relies upon traditional "dynamic" features like Dynamic dispatch is slow in any language, including C/C++ (provided that the compiler can't devirtualize the method). This is why such things are never done in an inner loop. In C++, its harder to "accidentally" use dynamic dispatch because you have to explicitly annotate a function as being virtual. In Julia, which is much more concise, type stability or instability is implicit. But it can be inspected statically via @code_warntype. Good IDE plug-ins can make it easier.