atcoder numpy pypy 10

That's not how progress happens in practice. As other comments pointed out, Google did not hire Rob Pike to "do" Go. I think it's still a relatively low effort way (just need to write a scraper) to create a benchmark on a diverse set of algorithmic tasks that have clearcut criteria on AC/TLE/WA. They tried to avoid library algorithms but not completely discard them. Even though the intended solutions of the majority of problems in these contests use ACL, the main part will be the thinking part. Right - but if you can get that with better performance that's good isn't it? If you want to win a competition which involves general matching you anyway should have it implemented. It's a shame how the core maintainers see CPython as a "reference implementation", rather than an opportunity to make a world-class programming language implementation. As an example, here is the code that computes the convolution of two given arrays: As you see, it makes the code much cleaner. Jim Macaulay Jim Macaulay. I'm not talking about other parts of your comment, just this one thing bothers me very much. Anaconda helps to an extent, but it's far from ideal especially compared to the default options for other languages. I think the idea that everyone should prepare their own library to the point where they have all the implementations they know/need is idealistic and is in practice false. I'm curious how this is working out, both implementation and performance-wise compared to LLVM (used in Pyston v1). Rewriting your stuff in C++ might allow you to save 95% of your server costs, but development cost is a lot higher. We are trying to make librarish problems less librarish. Numpy has fft. This is why I see little hope for Python, which is to say that while I'm sure it will continue to have a large following for many years a la C, C++, etc, I don't have hope for it being an exciting language or one that is particularly productive. All dynamic languages are generic by default. CPython libraries have always been pretty good on AtCoder. Here's one way to implement a template for lazy propagation. Look at this update function on a segment tree: I agree that some expressions can be written simpler in Kotlin than in Java. Always go back to python (and c) when in doubt. Having some knowledge doesn't differ so much from knowing some common tricks, which ofc. The paper is gone. Not that it's necessarily a bad thing, but it looks like the Pyston project isn't getting a lot of updates: I updated the repo in the middle of this comment thread; it used to show the v1 code, but since that was confusing people now it's just a stub that redirects you to the blog. Please try enabling it if you encounter problems. I was saddened by the path Python3 chose. It should be a[p] = mapping(f, a[p]) instead of a[p] = f(a[p]) in documentation. Trying to learn C++ and Java before, having to type everything was an impedement. When I include fenwicktree library I get following error [Error] 'enable_if_t' in namespace 'std' does not name a template type. R is another language that is mainly duct tape for stringing pieces of compiled code together. P.S. You don't have to compete with C++. But these lack the scientific programming ecosystem that's helped drive Python recently. But if you stopped naming servers a long time ago, and if the pricing structure is favorable, it could mean a huge cost saving without an expensive rewrite. You might want to add the version of pip, git, and your OS to the question for some better help. Personally I consider competitive programming to be it's own use case. > I don't get why people object to performance work on languages not intended for performance. is resting on its laurels, as evidenced by the very active, albeit sprawling, package ecosystem. Ah, sunk costs. I'm not sure if it's the good way. If you have more information, please let me know. Looks like inline caching of some specific ops with certain types. AtCoder is a programming contest site for anyone from beginners to experts. Aren't all of those mostly implemented in C or C++ extensions? appear to be useful in your problems as well as in every other problems and both of them require preparation. But then, 80% of that painfully long run-time is actually IO-bound operations, and even that is totally irrelevant when those 2 minutes of machine time have replaced 20 minutes of manual work. What is the correct order of integration when summing up the potential of a charge distribution? A large number of people already use implementations from public libraries, so it's not as if everyone would develop their own libraries if AtCoder doesn't provide one. I like R, but there is nothing elegant or consistent about its standard library. Pyston v2 is closed source (for now?). Note to future language maintainers: don't burn everyone's house down. Asking because if there's an interface trap I'm unaware of I'd like to learn about it before walking into it blindly. From the blog post: I think it's private. I agree performance is important, and I think we have reason to be optimistic, but with the understanding that that level of improvement, even if currently underway, will just take a lot of effort and time. E.g. But hasn't numba made some big progress in JITting CPython? I agree that matplotlib has a sprawling interface, and this can be annoying, but I'm still not sure what "guess the caller's intent by inspecting the types of the arguments" means. The Overflow #47: How to lead with clarity and empathy in the remote world, Feature Preview: New Review Suspensions Mod UX, Calling a function of a module by using its name (a string). Sign in to comment. If I'm off base please let me know. update:I was able to do it only after modifying the prod function of acl library. It's not the message of the Pyston v2 release blog entry, but then that blog entry isn't making an argument in the debate that you seem to want everything to be about. We started with an LLVM-based JIT in Pyston v1. Well that still goes in the "ease-of-use" bucket in my opinion. unused variables cause compilation errors, so things like this slow me down. The two ACL contests will be ARC-rated (rated for 1200-2799, 150 minutes, but we may change the lower bound / duration later). Memory usage looks better, too. Not just the package ecosystem, but you also have to have a large number of developers and jobs, so you can do hiring. It doesn’t sound like a fully-fledged JIT, but that might be a distinction without a difference. It makes simple things feel like a chore. PyOxidizer avoids this, but includes the entire standard library in every binary, making them too large by an order of magnitude. The first release will target NumPy binary and unary ufuncs. PyPy is the awesomest thing created since sliced bread, but C extension interoperability is still a source of perf problems in a few scenarios AIUI. It would be amazing to have Python bindings for this library. In this case, the ultimate score is 3 correct answers and Spent Time is 88:22. All the codes were implemented by yosupo, and testings were done by rng_58, maroonrk, DEGwer. The position that CPython has to be kept simple as a reference implementation is untenable in the absence of an active collaboration or effort to produce a performance oriented implementation. Or is it just code masturbation basement nerds whose idea of productivity is playing with internal guts in pursuit of some trivial abstract benchmarks? Wow, this requires both pre-written code (written in a correct way, so in a modifiable way), a knowledge on how to use/modify it and some amount of thinking. Runtime error on problem name princess farida on spoj. Currently we are not sure what's the best way to handle precision issues. Where the future goes to die. I've heard Go described this way several times, but I've found it to be a significantly lower-level language than Python. What to do with this situation? I would also expect community-built libraries to have closer-to-optimal perf as more people contribute. int a[10]; printf("%d", a[10]); When RE occurs, in some cases you may be able to see the compile message by going to the Results on the top of the screen then show Details on the right side of the screen. Straight off the bat that’s three key design decisions that make for a slow interpreter. An issue. The V8 javascript engine was there.

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