[machineko]

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.

[snicker7]

> 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]

> 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]

> 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]

> 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.

[bananaquant]

Julia optimizes for a different thing. You can get your result, as in the actual useful thing that the code does/produces, much faster than with C/C++. You can skip type annotations, not worry about the memory usage, and write your code interactively using REPL or the excellent Revise.jl package.

If you have saved a couple of minutes or hours of coding and are only going to run that code a handful of times, it should not matter if it runs a second or two slower than C/C++. This is the same rationale that Python and other scripting languages have. But unlike Python, you should be able to match the speed of C/C++ or get pretty close by optimizing your code.

[RyEgswuCsn]

Yes I get your point. I guess I should have phrased my first question like the following

1. Can unoptimised Julia code run faster than unoptimised Python code (with numpy being used to do the heavy lifting)?

Let's say one is prototyping some algorithm so iteration speed is more relevant than running speed. Then one can choose either Julia or Python (with the help of numpy perhaps) and get an implementation in similar timeframes. So Julia won't necessarily be more attractive here.

Now if the prototype proved that running speed is very critical to the successful application of the algorithm, then it would mean the developer now has to optimise the hell out of it. One can either:

1. Optimise the Julia codebase, if Julia was used to prototype, following the many tips and tricks available (e.g. type stability, various macros, etc.).

2. Port the algorithm to C/C++, applying the many performance best practices that people have accumulated over the years.

So if the optimised C/C++ port is capable of being any faster than the optimised Julia code, then the rational choice would be to port the implementation using C/C++; it would also mean Python would have some advantage over Julia in the prototyping phase too due to its popularity. Otherwise I'd agree that using a single language to both do prototyping and production is the best.

[DNF2]

This depends on what you mean by '(un)optimized code'. Because there's a difference between unoptimized and naive code.

'Unoptimized' code should still observe most of the performance tips in the manual (such as avoiding globals and type instability), while 'naive' code frequently does not. With some experience, you never write naive code, even for quick prototypes.

In those cases, Julia should outperform other dynamic lanuages significantly, and approach static languages in most cases.

Proper optimization means going in and removing allocations, ensuring that operations vectorize (simd), tailoring data structures for performance, adding parallelism etc. In the latter case Julia should virtually _always_ match static languages closely, otherwise it merits investigation.

[RyEgswuCsn]

Well there is no type stability or scoping rules to worry about in Python, so just for the sake of comparing the two, I was indeed thinking of 'naive' Julia code vs 'naive' Python code.

The thing with Python is that 'naive' Python code is already pretty close to 'unoptimised' Python code, so one can write naive Python code with numpy and still ends up with not-too-shabby performance, provided they chose an efficient algorithm, of course. In other words, there are not as many performance mistakes one can make with Python (perhaps because it can't get any worse). I imagine that's also why so many Python users who tried Julia were disappointed that direct translations of their Python program fail to perform as fast as advertised.

[socialdemocrat]

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