Tutorial slides (PDF) - Clemson University

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Source Code History Mostly Python-code, 10% C-code. 90% of wall-clock time spend in C, BLAS, and LAPACK.
Performance Mantra People are able to code complex algorithms in much less time by using a high-level language like Python (e.g., also C++). There can be a performance penalty in the most pure sense of the term. "The best performance improvement is the transition from the nonworking to the working state." --John Ousterhout "Premature optimization is the root of all evil." --Donald Knuth "You can always optimize it later." -- Unknown
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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
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: KS-DFT is a complex algorithm!
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 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 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
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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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