■_
インタプリタ本読んでる余裕もない。
2012年02月19日
■_
今日も今日とて Tokyo.Lang.R #0 : ATND に参加。 宿題つきw
↑の帰りに池袋のジュンク堂にいってあれやらこれやら購入。
紹介されている本にも興味があるけれど
紹介者の文章が結構面白い。arton さんのとか。
■_
10年後も世界で通じるために - Nothing ventured, nothing gained. Takuya Oikawa - Google+ - 木曜日と金曜日に通称デブサミ、Developers Summit… Makoto Anjo - Google+ - 木曜日と金曜日に通称デブサミ、Developers Summit…
■_ 新言語
なんやら伸びていたのでチェック。 Why we created julia - a new programming language for a fresh approach to technical computing : programming The Julia Language
訳は…あとで
The Julia Language The Julia Language Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, mostly written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, FFTs, and string processing. More libraries continue to be added over time. Julia programs are organized around defining functions, and overloading them for different combinations of argument types (which can also be user-defined). For a more in-depth discussion of the rationale and advantages of Julia over other systems, see the following highlights or read the introduction in the online manual. High-Performance JIT Compiler Julia's LLVM-based just-in-time (JIT) compiler combined with the language's design allow it to approach and often match the performance of C/C++. To get a sense of relative performance of Julia compared to other languages that can or could be used for numerical and scientific computing, we've written a small set of micro-benchmarks in a variety of languages. The source code for the various implementations can be found here: C++, Julia, Python, Matlab/Octave, R, and JavaScript. We encourage you to skim the code to get a sense for how easy or difficult numerical programming in each language is. The following micro-benchmark results are from a MacBook Pro with a 2.53GHz Intel Core 2 Duo CPU and 8GB of 1066MHz DDR3 RAM: C++ (GCC) Julia NumPy Matlab Octave R JavaScript 4.2.1* 7183d97a 1.5.1 R2011a 3.4 2.9.0 V8 3.6.6.11 fib 0.200 1.97 30.74 1360.47 2463.97 334.94 1.48 parse_int 0.242 1.22 16.49 827.13 6871.04 1082.67 2.14 quicksort 0.416 1.25 61.73 135.51 3357.78 971.06 6.66 mandel 0.249 7.15 31.98 66.27 870.55 253.10 6.03 pi_sum 53.524 0.74 18.77 1.09 356.08 269.19 0.74 rand_mat_stat 7.347 3.94 40.98 12.20 57.39 32.39 8.30 rand_mat_mul 227.331 1.01 1.20 0.78 1.69 2.65 291.83 Figure: C++ numbers are absolute benchmark times in milliseconds; (略)
んで、なぜ新言語を作ったのかという理由がこちら
…facial hair ってナニ? Facial hair - Wikipedia, the free encyclopedia
Why We Created Julia The Julia Blog Why We Created Julia 14 Feb 2012 | Viral Shah, Jeff Bezanson, Stefan Karpinski, Alan Edelman In short, because we are greedy. We are power Matlab users. Some of us are Lisp hackers. Some are Pythonistas, others Rubyists, still others Perl hackers. There are those of us who used Mathematica before we could grow facial hair. There are those who still can't grow facial hair. We've generated more R plots than any sane person should. C is our desert island programming language. わたしたちは Matlab の power user です。中には Lisp ハッカーも何人かいます。 Pythonista の人たちもいますし、Rubyist や Perl hacker の人たちもいます。 We love all of these languages; they are wonderful and powerful. For the work we do — scientific computing, machine learning, data mining, large-scale linear algebra, distributed and parallel computing — each one is perfect for some aspects of the work and terrible for others. Each one is a trade-off. わたしたちはこういった言語全部が好きです。それらの言語はすばらしく、かつ強力なものです。 わたしたちが行う作業、たとえば科学的コンピューティング、機械学習、データマイニング、 large-scale の線形代数、分散コンピューティングや並列プログラミング といったものの ひとつに対しては完璧であるのに、別のものに対しては terrible なものだったりするのです。 それぞれがトレードオフになっています。 #ダメダメな訳文だ○|￣|＿ We are greedy: we want more. わたしたちは貪欲なのです。もっともっと欲しいのです。 略
■_
そして reddit での反応から
Why we created julia - a new programming language for a fresh approach to technical computing : programming TLDR: We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled.These benchmarks are totally bogus. For instance, the c++ version of random matrix multiplication uses the c-bindings to BLAS: they then bemoan "how complicated the code is". This only goes to show that they are not experts in their chosen field: their are numerous BLAS-oriented libraries with convenient syntax that are faster than that binding. For instance, blitz++, which is celebrating its 14th annivesary. The MTL4 is upwards of 10x faster than optimized FORTRAN bindings, and is even faster than Goto DGEMM.Looks very cool! Why PCRE and not the re2 library? re2 lacks back-tracking but does have a linear-time guarentee. I think it would be better suited for scientific computing. If you're really feeling like you have to have it all, support both :-)Only because PCRE is widely used, and some of the regex stuff is pretty core to the language now. I personally did not know about re2, but looks interesting. We should certainly try it out and see how it compares, especially since PCRE now has a jit as well.
どうにも議論が発散しっぱなしな気が。
■_ 開発機
Mac Book Air (例の略称は使わん) を primary machine として使えるか? という問いかけ
Any developers using latest Macbook Air as primary machine? : ruby Any developers using latest Macbook Air as primary machine? (self.ruby)I am a Ruby and iOS developer. I want to get the latest MBA i7 w/ 256GB SSD. My only problem with it is the 4GB RAM limit, which sucks. I just want to get other's opinions about using the Macbook Air as a main machine for writing code.If the guys at 37signals can do it, so can you.I would advise against it if you're running VMs. I used to have only 4GB on my 13-inch Macbook Pro and it was painful whenever I wanted to run a couple of VMs (using Vagrant/VirtualBox). Maybe wait for the next (Ivy-Bridge based) MB Air? It will probably support adding more RAM (I hope!). If it's not an option, a 13-inch MacBook Pro?
■_ この人はどうして
んでまあ、ジュンク堂で買った本の一冊が↓この本なんですが
なんでこう人を挑発するような書きかたしますかね。
例の本に比べればよっぽどおとなしいですが、
こういうところははっきりいってこの人の欠点だと思っています。
前作は一部にわりといいこと書くなあと思った部分もあったのですが
(結局買ってないんですけどね。だから書評も感想も書いてない)、
自分の中で上がりかけた評価が以下略
■_
- Amazon.co.jp： Excel VBA 実用マクロ800連発 [中・上級編]: 永井 善王: 本
- Amazon.co.jp： 〔速攻入門〕 C#プログラミング すぐに現場で使える知識: 中 博俊, 猪股 健太郎, 岩永 信之, 山本 康彦: 本
- Amazon.co.jp： C言語によるリアルタイム組込みOS自作講座: 宇野 俊夫: 本
- Amazon.co.jp： 人気「美姫」絵師に聞いてきた彼らが巧くなった理由: べたにゃ: 本
- 古いソフトを合法的に配布する方法 | OKWave
- 初音ミクとネギの組み合わせはどこが起源なんですか？ | OKWave
- UNIX | OKWave
- 自分戦略
- 皆さんの質問、疑問を募集中です：こってり系オープンソースライセンス勉強会 #osslicence | オープンソース・ライセンスの談話室 『IBMのメインフレームを見に行こう』(第二回) - Togetter
- As mind is suitable...:「第2弾:IBMのメインフレームを見に行こう！」といイベントに参加してきました ＃IBM
- [IT] IBM テクノロジーセンター見学 (第2弾:IBMのメインフレームを見に行こう！) | FX FROG
- sudo-1.8のバグ - てきとうなメモ
- Effective C++やC++11やテンプレートを教えてみた。 - krustf の雑記
- 第一回ゆるふわーる
- InfoQ: The Role of Emotions in Software Engineering
- Ideone.com | Online Haskell Compiler & Debugging Tool
- H-Yamaguchi.net: 「朝9時から仕事」について
- wrong, rogue and booklog - Julia’s LLVM-based just-in-time (JIT) compiler...
