Tutorial slides (PDF) - Clemson University

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From a Scientific Library To a Scientific Application Massimo Di Pierro
From Lib to App (overview) Numerical Algorithms
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Python for Scientific and High Perf
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Overview We seek to cover: Python l
Page 5 and 6:
1. Introduction Introductions Tutor
Page 7 and 8:
Why Use Python for Scientific Compu
Page 9 and 10:
Please login to the Tutorial Enviro
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Interpreters CPython Standard pytho
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CPython Interpreter Notes Compilati
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Built-in Numeric Types int, float,
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Gotchas with Built-in Numeric Types
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Built-in Sequence Types str, unicod
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Built-in Sequence & Mapping Type Go
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Control Structures for - iterative
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List Comprehensions List Comprehens
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File I/O Basics Most I/O in Python
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Let's write ten integers to disk wi
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Modules import - load module, defin
Page 33 and 34:
N-dimensional homogeneous arrays (n
Page 35 and 36: Slicing Arrays >>> a = np.array([[1
Page 37 and 38: SciPy Extends NumPy with common sci
Page 39 and 40: Python Threading Python threads rea
Page 41 and 42: Implementation Example: Calculating
Page 43 and 44: Subprocess The subprocess module al
Page 45 and 46: Calculating pi with multiprocessing
Page 47 and 48: mpi4py wraps your native mpi prefer
Page 49 and 50: from mpi4py import MPI import numpy
Page 51 and 52: Python Best Practices for Performan
Page 53 and 54: Outline a massively parallel Python
Page 55 and 56: GPAW Strong-scaling Results
Page 57 and 58: KS-DFT is a complex algorithm!
Page 59 and 60: Performance Mantra People are able
Page 61 and 62: NumPy - Memory How big is your bina
Page 63 and 64: NumPy - FLOPS WARNING: If you make
Page 65 and 66: Profiling Mixed Python-C code Numbe
Page 67 and 68: Parallel Python Interpreter and Deb
Page 69 and 70: Profiling Mixed Python-C code Flat
Page 71 and 72: Python Interface to BLACS and ScaLA
Page 79 and 80: Python Interface in BLACS and ScaLA
Page 81 and 82: Python Interface in BLACS and ScaLA
Page 83 and 84: The Bad & Ugly: NumPy cross-compile
Page 85: Python for plotting and visualizati
Page 89 and 90: From Lib to App (overview) Storage
Page 91 and 92: Ruby on Rails Django TurboGears Pyl
Page 93 and 94: matplotlib gallery
Page 95 and 96: application1 application 2 applicat
Page 97 and 98: application 1 application=” dna
Page 99 and 100: web2py and Dispatching hostnam e
Page 101 and 102: web2py and Dispatching controller
Page 103 and 104: Upload DNA Seq. {{=form}} web2py a
Page 105 and 106: web2py and Authentication authentic
Page 107 and 108: web2py web based IDE web based IDE
Page 109 and 110: Before we start download web2py fro
Page 111 and 112: Define some algorithms def find_gen
Page 113 and 114: Define actions in controllers/defau
Page 115 and 116: Try it
Page 117 and 118: Define Actions in controllers/defau
Page 119 and 120: Try it
Page 121 and 122: Yet More Resources Tau http://www.c
Page 123 and 124: Let's review!
Page 125: This work is supported in part by t
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