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Python for Scientific and High Perf
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Overview We seek to cover: Python l
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1. Introduction Introductions Tutor
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Why Use Python for Scientific Compu
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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
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N-dimensional homogeneous arrays (n
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Slicing Arrays >>> a = np.array([[1
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SciPy Extends NumPy with common sci
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Python Threading Python threads rea
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Implementation Example: Calculating
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Subprocess The subprocess module al
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Calculating pi with multiprocessing
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mpi4py wraps your native mpi prefer
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from mpi4py import MPI import numpy
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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 73 and 74: Python Interface to BLACS and ScaLA
- Page 75 and 76: Python Interface to BLACS and ScaLA
- Page 77 and 78: 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 and 86: Python for plotting and visualizati
- Page 87 and 88: From Lib to App (overview) Numerica
- 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: Upload DNA Seq. {{=form}} web2py a
- 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