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1 田中昌宏 Masahiro Tanaka 1
2 NArray の開発者 NArray developer 昨年度からつくば市在住 I live in Tsukuba since Apr
3 NArray を使ったサイエンス Science using NArray NArray の概要 NArray overview 詳しい話はしません No details. 並列分散ワークフロー実行システム Parallel Distributed Workflow System 3
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6 GPhys 多次元物理量表現ライブラリ multi-dimensional physical quantity library RubyDCL グラフ描画ライブラリ Plotting Graph RubyNetCDF 物理データフォーマットNetCDFのIOライブラリ Physical data format library 6
7 Gtk ベース格子点可視化 解析ツール Gtk-base visualize & analysis tool for lattice data 7
8 Rails ベースの地球流体データベース 解析 可視化ツール Rails-base visualize & analysis tool for Geophysical database 8
9 9
10 物質科学における 多次元試料データの管理を効率的に行う For managing multi-dimensional sample data in material science. 10
11 光の強さ 位置 波長 Shin-ichi Todoroki, Meas. Sci. Technol. 16 (2005)
12 12
13 13
14 電子顕微鏡写真から 生体微小管などの三次元構造を構築するためのプログラム Program set for Helical image analysis in combination with electron microscopy used to study threedimensional structures of microtubules etc. 14
15 画像から構築した 3 次元構造 3-D structure constructed from the EM image 電子顕微鏡写真 Electron Microscope image 15
16 16
17 A book in dynamics for general readers 17
18 Program and explanation 飛跡の計算結果 Result flight trajectory 18
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21 処理を簡単に書きたい write processing codes shortly 結果をすぐ確認したい confirm processing result immediately インタラクティブな操作もしたい manipulate interactively 21
22 プログラムが書ける Programmable インタラクティブな実行 Interactive execution データ配列操作 Data Array Manipulation 数値計算ライブラリ Library for Numerical Algorithms グラフ表示機能 Plotting Graphs Ruby irb NArray 22
23 多次元数値配列 Numerical N-dimensional Array
24 p (current version) ?? (next version, under development) 24
25 IDL (Interactive Data Language) Yorick Python Numeric 25
26 rank 次元数 the number of dimension shape 配列の 形 array shape type 要素の型 element type memory block データを格納するメモリーブロック 26
![Ruby Array : VALUE *ptr; value[0] value[1] value[2] value[3] NArray void *ptr; or](/docs-images/104/161975807/images/27-2.jpg "struct RVALUE { } double[0] double[1] double[2] double[3] int[0] int[1] int[2]")
27 Ruby Array : VALUE *ptr; value[0] value[1] value[2] value[3] NArray void *ptr; or struct RVALUE { } double[0] double[1] double[2] double[3] int[0] int[1] int[2] int[3] 27
28 整数 BYTE (8bit) SINT (16bit) INT (32bit) 浮動小数点数 SFLOAT (32bit) FLOAT = DFLOAT (64bit) 複素数 SCOMPLEX (64bit) COMPLEX = DCOMPLEX (128bit) Ruby オブジェクト ROBJECT 28
29 現版では ビルトインのみ Current version has Built-in types only 次版 Next version 後から付け足すことが可能 Append-able types 29
![shape = [4] 1 次元配列 one-dimensional array a[0] a[1] a[2] a[3] shape = [4,4] 2 次元配列 two-dimensional array a[0,0] a[1,0] a[2,0] a[3,0] a[0,1] a[1,1] a[2,1] a[3,1] a[0,2] a[1,2] a[2,2] a[3,2] a[0,3]](/docs-images/104/161975807/images/30-2.jpg "a[1,3] a[2,3] a[3,3] shape = [4,4,4] 3 次元配列 three-dimensional array a[0,0,0] a[1,0,0] a[2,0,0] a[3,0,0] a[0,1,0] a[1,1,0] a[2,1,0] a[3,1,0] a[0,2,0] a[1,2,0] a[2,2,0] a[3,2,0] a[0,3,0] a[1,3,0]")
30 shape = [4] 1 次元配列 one-dimensional array a[0] a[1] a[2] a[3] shape = [4,4] 2 次元配列 two-dimensional array a[0,0] a[1,0] a[2,0] a[3,0] a[0,1] a[1,1] a[2,1] a[3,1] a[0,2] a[1,2] a[2,2] a[3,2] a[0,3] a[1,3] a[2,3] a[3,3] shape = [4,4,4] 3 次元配列 three-dimensional array a[0,0,0] a[1,0,0] a[2,0,0] a[3,0,0] a[0,1,0] a[1,1,0] a[2,1,0] a[3,1,0] a[0,2,0] a[1,2,0] a[2,2,0] a[3,2,0] a[0,3,0] a[1,3,0] a[2,3,0] a[3,3,0] 行列でいう次元とは異なる Different from Matrix dimension 30
![a[i,j] 左の次元 (i) がはやく回る Left dimension(i) is contiguous Fortran-order Row-major order データが連続する順番 contiguous order a[0,0] a[1,0] a[2,0] a[3,0] a[0,1] a[1,1] a[2,1] a[3,1] If a.](/docs-images/104/161975807/images/31-2.jpg "shape == [m,n] then a[i,j] == a[i+j*m] a[0,2] a[1,2] a[2,2] a[3,2] a[0,3] a[1,3] a[2,3] a[3,3] a[j][i] と逆 reverse order 次版では変更予定 This will be changed for the next version.")
31 a[i,j] 左の次元 (i) がはやく回る Left dimension(i) is contiguous Fortran-order Row-major order データが連続する順番 contiguous order a[0,0] a[1,0] a[2,0] a[3,0] a[0,1] a[1,1] a[2,1] a[3,1] If a.shape == [m,n] then a[i,j] == a[i+j*m] a[0,2] a[1,2] a[2,2] a[3,2] a[0,3] a[1,3] a[2,3] a[3,3] a[j][i] と逆 reverse order 次版では変更予定 This will be changed for the next version. 31
![NArray.float(3,2) => NArray.float(3,2): [ [ 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0 ] ] NArray.float(3,2).indgen! => NArray.float(3,2): [ [ 0.0, 1.0, 2.0 ], [ 3.](/docs-images/104/161975807/images/32-2.jpg "0, 4.0, 5.0 ] ] NArray[[1,2,3],[4,5,6]] => NArray.int(3,2): [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] NArray[1..10] => NArray.")
32 NArray.float(3,2) => NArray.float(3,2): [ [ 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0 ] ] NArray.float(3,2).indgen! => NArray.float(3,2): [ [ 0.0, 1.0, 2.0 ], [ 3.0, 4.0, 5.0 ] ] NArray[[1,2,3],[4,5,6]] => NArray.int(3,2): [ [ 1, 2, 3 ], [ 4, 5, 6 ] ] NArray[1..10] => NArray.int(10): [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] 32
![a[1] 要素参照 reference by element a[1.](/docs-images/104/161975807/images/33-2.jpg ".2] 範囲参照 reference by range a[[1,3]] インデックス配列参照 reference by index array a[0] a[1]")
33 a[1] 要素参照 reference by element a[1..2] 範囲参照 reference by range a[[1,3]] インデックス配列参照 reference by index array a[0] a[1] a[2] a[3] a[0] a[1] a[2] a[3] a[0] a[1] a[2] a[3] 33
34 演算は要素ごと Operation is element-wise 演算 Operations 四則演算 arithmetic operations +, -, *, /, %, ** 数学関数演算 math operations NMath module a * b = a[0]*b[0] a[1]*b[1] a[2]*b[2] a[3]*b[3] 34
35 Float Array with 10 6 elements Ruby : (0...n).map{ i a[i]*b[i]} elapsed time : 180 ms NArray with Ruby : a*b elapsed time : 6.4 ms NArray is ~ 28 times faster 28 倍速い 8 times less characters 8 倍短く書ける 35
36 速度向上の倍率 Speedup factor NArray による性能向上 Speedup by NArray 0.E+00 2.E+05 4.E+05 6.E+05 8.E+05 1.E+06 データサイズ data size コードは NArray のサイトにあります Speedup by NArray 36
37 NArray のメモリアクセス Memory access for NArray 実際の PC アーキテクチャ Actual PC architechture CPU core Main Memory NArray s Bottleneck ボトルネック CPU core L1 cache L2 cache Main Memory fast slow 37
38 CPU Socket CPU core CPU core CPU Socket CPU core CPU core GPU cache cache cache cache cache cache Node Main Memory Main Memory Storage Node Node Network 38
39 Ruby and NArray コードを書きやすい Easy to write code 複雑さの隠蔽 Hide complexity アーキテクチャの違い different architecture データの分散 data distribution 39
40 プログラムが書ける Ruby Programmable インタラクティブな実行 irb Interactive execution データ配列操作 NArray Data Array Manipulation 数値計算ライブラリ Ruby/GSL, etc. Library for Numerical Algorithms グラフ表示機能? Plotting Graphs 40
41 NArray の各機能 NArray functions NArray の使い方 NArray usage 次版 NArray Next version of NArray 他の類似数値配列との比較 Comparison with other numerical arrays, e.g. Numpy Python が科学分野で主流になりつつある件 Python is becoming major in Science field. 41
42 田中昌宏 Masahiro Tanaka 筑波大学 University of Tsukuba 42
43 科学データ処理 Scientific data processing 複雑なワークフローである is a complicated workflow. ワークフロー記述言語が必要 It needs definition language Makefile による記述が提案 Use of Makefile has been proposed e.g. GXP make ワークフローのグラフ Workflow graph 43
44 Rake によるワークフロー記述を提案 We propose defining workflows Rake Make より記述力が高い More descriptive power than Make Rake の並列分散拡張として Pwrake ( プレイク ) を開発 We are developing Pwrake, a Parallel workflow extension for Rake Process Pwrake Process Process 44
45 NArray を使った科学計算の例について紹介 Examples of Scientific computing using NArray NArray とその特徴を紹介 NArray and its features 並列分散ワークフロー実行システム Pwrake Parallel Distributed Workflow System Pwrake 45
46 ご質問 Questions? 46
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並列分散ワークフロー処理 システムの研究開発 田中昌宏 ( 筑波大 ) 1 発表内容 並列分散処理の必要性 ワークフローの記述について 並列分散ワークフロー実行システムPwrake 分散ファイルシステム Gfarm 天文データ処理ワークフローの記述 Montage SDFRED1 2 並列処理の必要性 データ量の増加 観測装置の進化 アーカイブの蓄積 並列処理の必要性 1コアあたりの性能が限界 計算機の進化は
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