Tutorial slides (PDF) - Clemson University

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Introductions Your presenters: William R. Scullin wscullin@alcf.anl.gov James B. Snyder jbsnyder@northwestern.edu Nick Romero naromero@alcf.anl.gov Massimo Di Pierro mdipierro@cs.depaul.edu
Overview We seek to cover: Python language and interpreter basics Popular modules and packages for scientific applications How to improve performance in Python programs How to visualize and share data using Python Where to find documentation and resources Do: Feel free to interrupt the <strong>slides</strong> are a guide - we're only successful if you learn what you came for; we can go anywhere you'd like Ask questions Find us after the tutorial
Page 1: Python for Scientific and High Perf
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
Page 11 and 12: Interpreters CPython Standard pytho
Page 13 and 14: CPython Interpreter Notes Compilati
Page 15 and 16: Built-in Numeric Types int, float,
Page 17 and 18: Gotchas with Built-in Numeric Types
Page 19 and 20: Built-in Sequence Types str, unicod
Page 21 and 22: Built-in Sequence & Mapping Type Go
Page 23 and 24: Control Structures for - iterative
Page 25 and 26: List Comprehensions List Comprehens
Page 27 and 28: File I/O Basics Most I/O in Python
Page 29 and 30: Let's write ten integers to disk wi
Page 31 and 32: 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
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NumPy - Memory How big is your bina
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NumPy - FLOPS WARNING: If you make
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Profiling Mixed Python-C code Numbe
Page 67 and 68:
Parallel Python Interpreter and Deb
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Profiling Mixed Python-C code Flat
Page 71 and 72:
Python Interface to BLACS and ScaLA
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Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Python Interface in BLACS and ScaLA
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Python Interface in BLACS and ScaLA
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The Bad & Ugly: NumPy cross-compile
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Python for plotting and visualizati
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From Lib to App (overview) Numerica
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From Lib to App (overview) Storage
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Ruby on Rails Django TurboGears Pyl
Page 93 and 94:
matplotlib gallery
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application1 application 2 applicat
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application 1 application=” dna
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web2py and Dispatching hostnam e
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web2py and Dispatching controller
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Upload DNA Seq. {{=form}} web2py a
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web2py and Authentication authentic
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web2py web based IDE web based IDE
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Before we start download web2py fro
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Define some algorithms def find_gen
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Define actions in controllers/defau
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Try it
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Define Actions in controllers/defau
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Try it
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Yet More Resources Tau http://www.c
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Let's review!
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This work is supported in part by t
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Tutorial slides (PDF) - Clemson University

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