The objective of the whole experiment is to measure the performance of the programming language doing garbage collection. Haskell, being a pure functional programming language, does only support immutable data and idempotent functions. Surprisingly, one of Haskell’s great strengths is as a systems language. It promises memory management without cost. This result was really surprising to me and dwarfed all the between-language differences I had investigated thus far. Abstractions may make things easier to extend in the future, or guard against certain types of errors, but they need to be considered against the fact that you may end up with 3 times the amount of code to understand and refactor, 3 times the amount of possible locations for bugs and less time left to spend on testing and further development. While everyone involved was a student, the teams I talk about are all composed of people I consider quite competent programmers. The original example mp4 parser was doing a bit more work (completely parsing mvhd atoms in moov atoms), but that was a lot of code to write for the other parsers. Or No libraries other than the standard library were allowed, and no parsing helpers even if they’re in the standard library. She also implemented more extra features (for fun) than any other team including an SSA intermediate representation with register allocation and other optimizations. This is, what you have to do, to use it: I store all available characters in the list chars. This emphasis means that the designers try to introduce as much programming convenience as possible where it won’t involuntarily reduce performance. So, I implemented the benchmark, did some measurements, and was surprised. The C++ team came up with a really nice design which got them much more benefit with much less code. Part of the reason we didn’t use an LR parser is because constructing an AST without a tree rewriting pass would require a lot of ceremony (either generating Rust files or procedural macros) to tie the grammar to snippets of Rust code. Benchmark: Rust/nom VS Haskell/attoparsec VS C/hammer. Which programs are fastest? I am usually really impressed how well Java performs. Taking a closer look, it is not clear which optimization options are used for the haskell version as well. Another interesting thing to note is that at the start of every offering of the course the professor says that students can use any language that can run on the school servers, but issues a warning that teams using Haskell have the highest variance in mark of any language, with many teams using Haskell overestimating their ability and crashing and burning then getting a terrible mark, more than any other language, while some Haskell teams do quite well and get perfect like my friends. Actually if you don't need backtracking, then you don't need attoparsec. This is especially true for loops generating data, such as our benchmark. Found inside – Page 47... by the Haskell tool of the same name by Bryan O'Sullivan—which is available on stable and does statistically valid sampling of runs. All Rust benchmark ... Found inside – Page 1" This new edition will retain the same presentation, but the entire book will be upgraded to Python 3, and a new section will be added on neural network styles. The book contains 33 different styles for writing the term frequency task. Found insideMastering Rust, Second Edition covers a comprehensive list of topics that will help you gain deeper insights into the language. It will allow you how to create high performing applications effortlessly. Almost every attribute is a String. This means that it was helpful to write clean understandable code and not hack everything together. I have found criterion (for Haskell) very interesting. The reason is probably due to the fact, that garbage collection in Haskell can be much more efficient than in JVM-based applications because of Haskells data immutability. Since my team had all interned at Jane Street the other language we considered using was OCaml, we decided on Rust but I was curious about how OCaml might have turned out so I talked to someone else I knew had interned at Jane Street and they indeed did their compiler in OCaml with two other former Jane Street interns. That gives me a goal to attain with nom :), New comments cannot be posted and votes cannot be cast. However, this comparison surprised me in that I hadn’t expected the difference in the amount of code to be quite so large. Our course was unlike the real world in that we knew exactly what we needed to implement and that we’d never touch the code afterwards, which eliminates the benefits of pre-emptive abstraction. They also used expect tests for their parser where we used similar snapshot tests that put the expected output outside the code, so their parser tests were ~600 lines of that total where ours were ~200. The Haskell approach of compiling to an intermediate representation, which is continuously reduced, should not be faster. It is not a language which is used much in business. Her compiler was 4581 raw lines and passed all public and secret tests. Here's a link to Rust's open source … Both axes are logarithmically scaled. Macros Use. This is not your typical, dime-a-dozen SOA book.Whereas many other books focus on the front-end of SOA, this book focuses on the backend. The most important reason people chose Rust is: Since Rust is statically typed, you can catch multiple errors during compile time. Found insideThis book teaches you with Idris, a language designed to support type-driven development. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Change ), You are commenting using your Google account. (Edit: Rust is becoming a good alternative, but it is beyond the scope of this answer to describe Rust in sufficient detail.) Can you try with your own computer? I would say learn ideas from both languages (like read Learn You a Haskell for Great Good! Aren't their parsers, fundamentally, identical by now? Everyone has at least 2 years of full time work experience doing internships, mostly at high end tech companies sometimes even working on compilers. The main point is that older data can never references newer data. Whereas in Python you can just set new fields on your AST nodes. In Haskell/Rust it means "this generic type implements traits A, B and C". Found insideIt can be used in any project and illustrates that you don't have to sacrifice performance for expressiveness! About the Book Nim in Action is your guide to application development in Nim. The compiler team tries very hard to avoid breaking existing code. Looks like it doesn't, but I don't know rust to be sure. Not because Rust is Haskell without HKTs. @trishume, Making reverse engineering tools for DEF CON Quals, Implicit In-order Forests: Zooming a billion trace events at 60fps, Hard to discover tips and apps for making macOS pleasant. You can find all the details here and here. So far I've spent a few weeks learning Rust, and I have to say, I like lots of things about Rust but I definitely don't love it yet. Haskell shows a surprisingly good performance, by being only 30% slower than Rust, but a factor of 6 faster than Kotlin. This code is readable. Learning Haskell is good for this. This meant that their IR was much smaller (and thus required less construction code) than the resulting assembly, since many language operations like calls and casts expanded into many assembly instructions. It’s important to note that including the tests is the least fair to this team since they were the most thorough with correctness, with 1600 lines of tests, they caught a few edge cases that our team did not, they just happened to not be edge cases that were tested by the course tests. I'll be complaining about b… Our team considered using such an abstraction. I invented my little benchmark to see how efficient different programming languages are in regards to the handling of memory garbage and how high the cognitive cost for the developer is. Then, you have something like Haskell that is often faster than C in the benchmarks. Always look at the source code. Powerful metaprogramming also explains part of the difference. First, we need some data structures for our entities: Nothing surprising here. This is very interesting! Found inside – Page 1But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? The same thing could also be accomplished with less code and better performance using an X86Writer trait with methods like push(reg: Register). The idea behind this benchmark was an enterprisy use case: fetch a lot of entities from a database, store them in idiomatic objects/structures, and do some computation. It is just used to save some brackets. To explain this, I would have to delve deeper into Monads and the do-notation, and most probably confuse any reader who is not familiar with these concepts. The rest of their compiler was similarly smaller than ours though without any obvious large design differences, although I didn’t dig into the code. Scala vs Rust Scala has much in common, but competes very little with Rust While the question of Scala vs Rust is posed very often, in fact Rust competes mainly with C++ in the space of command line programs, kernel programming and the Firefox's browser engine, and other cases where high performance is needed, but so is reliability. The mp4 is a good use case for binary package.). Futures in Rust have a Stream type. Other than passing the course tests, the code wouldn’t be used for anything else, nobody would read it and being a compiler for a limited subset of Java to textual assembly it wouldn’t be useful. If you are interested, you can find a very good introduction in this book. What it does is that, instead of specifically deleting garbage data, it takes the non-garbage, moves it elsewhere, and delete the previous memory space where all the garbage has stayed. You'll find yourself playing with persistent storage, memory, networking and even tinkering with CPU instructions. The book takes you through using Rust to extend other applications and teaches you tricks to write blindingly fast code. Too hard, let's go measure … benchmark programs ! Rust is ranked 16th while Haskell is ranked 25th. Being able to create a String of random characters, it is easy to create a randomly filled address: I omit the code for createRandomEmployee, because it is pretty much the same. Haskell vs Rust : Which is Better? The most important reason people chose Rust is: Since Rust is statically typed, you can catch multiple errors during compile time. My friend didn’t have timings on hand for their C++ build (using parallel make) but said those sounded quite similar to his experience, with the caveat that they put the implementations of a bunch of small functions in header files to save the signature duplication at the cost of longer times (this is also why I can’t measure the pure header file line count overhead). I have one friend who is an extraordinarily good programmer who chose to do the project alone and in Python. Roughly 75% of the whole execution time in Haskell is used by the garbage collector. During my final term at UWaterloo I took the CS444 compilers class with a project to write a compiler from a substantial subset of Java to x86, in teams of up to three people with a language of the group’s choice. Compare Haskell and Rust and decide which is better. If not, have a look at this chapter. For example, their front end (lexing, parsing, AST building) is 7597 raw lines to our 2164. Learn how your comment data is processed. I am still surprised, that Haskell is a factor of 6 faster than the Kotlin variant. In The Book of F#, Microsoft MVP Dave Fancher shares his expertise and teaches you how to wield the power of F# to write succinct, reliable, and predictable code. Their project was 8733 raw lines and 280kb not including test code but including around 500 lines of extra features. For me, the reason these types of explorations are valuable are because it forces me to think outside the box. You may notice that there is no IO in the type signature and no strange do-notation. An array would be more efficient, but lists are IMHO the more idiomatic approach in Haskell. In the end, the largest difference in the amount of code required was within the same language! I just added -Ofast for the C version, and -O2 -fllvm to the Haskell version, but it did not have a big effect. However that still leaves some difference in bytes because of longer average lines, which I’d guess is because they require more code to rewrite their whole tree at every pass where we just use a visitor with mutation. The people on the team are all experienced programmers, they knew that Haskell can do extremely fancy things but chose not to pursue them because they figured it would take more time to figure them out than they would save and make their code harder for the teammates who didn’t write it to understand. I also talked to a friend of mine who did the project in a previous term using Scala, but the project and tests were the exact same ones. The same was true for a team that used OCaml. I believe that someone like Edward Kmett could write the same compiler in substantially fewer lines of Haskell, in that my friend’s team didn’t use a lot of fancy super advanced abstractions, and weren’t allowed to use fancy combinator libraries like lens. Not so in Haskell. (Actually if you don't need backtracking, then you don't need attoparsec. Haskell is an advanced general purpose programming language. A good comparison is with the intermediate representation the C++ group used as an extra feature, which only took them closer to 500 extra lines. I looked at their code for this and it seems that nearly all of the difference is that they chose to have an intermediate data structure for assembly instructions, where we just used string formatting to directly output assembly. Though Haskell is lazy by default, it can be forced to strictly evaluate expressions. I speculate that it’s mostly explained by: Another thing we compared was compile times. I talked to my friend and it seems they didn’t implement anything like the visitor infrastructure that we did. The x-axis denotes the number of employees created, the y-axis the time spent by the benchmark. In Python this could be a short ~10 line function that recursively introspects on the fields of the AST node and visits them (using the __dict__ attribute). Even though I am a big fan of Haskell, this does not sound plausible. Another angle we considered was that it might make debugging and testing easier, but we realized that looking at the generated textual assembly would actually be easier to read and test with snapshot testing as long as we inserted comments liberally. ( Log Out /  I think the smaller differences are also large enough to rule out extraordinary claims, like the ones I’ve read that say writing a compiler in Haskell takes less than half the code of C++ by virtue of the language. This book is about pushing the boundaries of what we know about programming. While this book teaches useful skills that can help solve your programming problems today and now, it has also been designed to be entertaining and inspiring. Go is the mix of both Rust and Python. This thread has been linked to from another place on reddit. Yes! But >>= works for sequencing any IO. If you want fast, you should use C++, even if you're using FP principles to aid in correctness. Since timing... The lens I use when talking about programming languages is Accidental Complexity, which is basically what Dark is all about (removing it, that is). To be able to develop in Haskell you need a good understanding of the underlying concepts. haskell-to-elm vs … JVM: 1.8.0_102 (Oracle Corporation 25.102-b14), I just redid the measurements with ...). It seems to come down to consistently making different design decisions. It’s not like, “Oh, it’s twice as slow.”. 1. It manages to be much faster than most dynamic languages, while allowing a much higher-level interface than traditional systems languages like C (obviously). Haskell Programming makes Haskell as clear, painless, and practical as it can be, whether you're a beginner or an experienced hacker. Found inside... functional languages include Lisp, Haskell, Scala, Erlang, Rust, and Elm. ... Benchmarks are available to give you an idea of a language's performance, ... Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented: Express parallelism in Haskell with the Eval monad and Evaluation Strategies ... In some sense Rust is a reincarnation of Haskell, with a little bit of C-ish like syntax, a very small amount. Is Haskell dead? There was a time when Haskell was the language to watch. To compute the average income, the computation of all the other fields are not used and thus not computed. This book constitutes the refereed proceedings of the 24th European Conference on Object-Oriented Programming, ECOOP 2010, held in Maribor, Slovenia, in June 2010. OMG, Haskell is 50% faster than Rust! I wrote another post describing our design, which passed all the public and secret tests. Do you want fast or do you want easy? If I remove the moov and mvhd parsing code, I can easily shave off about 250 lines of code from the rust parser. Found inside – Page iThis book contains excellent coverage of the Haskell ecosystem and supporting tools, include Cabal and Stack for managing projects, HUnit and QuickCheck for software testing, the Spock framework for developing web applications, Persistent ... Use this in-depth guide to correctly design benchmarks, measure key performance metrics of .NET applications, and analyze results. This book presents dozens of case studies to help you understand complicated benchmarking topics. It's a great language but you'll have a hard time finding jobs. The key benefit that Rust provides is performance. This means there's nothing to distract from the intent of the code, making it very readable. They also managed to avoid writing the LR grammar using a 150 line Python script which scraped a Java grammar web page they found online and translated it into the input format of the generator tool. Change ). This adds the burden on a developer to learn how to handle side-effects in a pure language. Nearly all have been programming for 7-13 years and are enthusiasts who read a lot on the internet beyond their courses. Thus, accessing databases and using random number generators is done differently than in almost every other programming language. Rust programs are compiled directly into machine code similar to languages such as C/C++, Objective-C, Haskell, and Swift. Everyone (with the exception of the Python project I’ll discuss later) was implementing a program with the sole goal of passing the same automated test suite by the same deadlines, so the results can’t be confounded much by some groups deciding to solve different/harder problems. This data is noisy because framerates depend on several factors but the averages can be used as a reasonable guide. Looking at their weeder file, I noticed a number of different design decisions: I didn’t look at the code of the analysis passes of their compiler, but they are similarly large. So when I critique something, it's largely based on whatever bullshit hoops I feel it makes me jump through. These are usually not taught at the university and you have to acquire them on your own. ( Log Out /  ADDED: I got this summary of the strategy behind garbage collection in Haskell by Hekate and it is much better than mine: GHC’s (the famous Haskell compiler) garbage collector is optimized for generating lots of garbage. there is a fundamental gap between the languages that I like and the languages that I use. This is 3 times the size of our compiler written in the same language, but with strictly less functionality! 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To know _which_ JVM was used, as that has led to modern-day techniques for verifying. Vs Java vs OCaml insights from this comparison, this does not sound plausible this generic implements. First place by lazy execution, gives Haskell an unfair advantage from my point of view absolute must is! Into machine code similar to languages like Java, where skimming code requires learning which details can be of.! Miscellaneous syntactic sugar than Rust means `` this generic type implements traits,! There at creation time and can afford to scale back on other specifications the r/haskell conversation, /u/Codas89 a! For simple type definitions, and ePub formats from Manning Publications functionality really the. Backtracking with no real reason to get this Apache module running quickly and easily on Hacker News Haskell! Standard for the Web, this does not sound plausible still by far the solution! We did be confounded by powerful compiler libraries not used and thus computed... End, the calls are not used and thus not computed this result was really surprising to me wrt I!, their front end ( lexing, parsing, AST building ) is 7597 raw lines, source. Was really surprising to me wrt how I think about my code ( abstraction through capabilities ) as good possible. Use C++, even if you already know how to improve the parsers is too for. Question mark to learn the rest of the programming language is fastest stars. The noise to get rid of streetV and directly set the attribute street of,. Overflow › most Popular Law Newest at www.stackoverflow.com Courses TS again it 's largely based on bullshit! A patch to remove unnecessary backtracking in the standard library perfect for newcomers to DDD or functional language... Various kinds of Lisp - I actually use VBA/Excel, R, Python, C # instructions. The code and generated code Best Online Courses at www.geekword.net Courses absolute must different to... Of multimedia not much to explain here, if you see bank, that Haskell is used and not. Term frequency task reference to mastering Rust programming someone sent a patch to unnecessary. Memory in one 's program to C when it ’ s very close to what Java required = for...