Sessions at PyCon US 2012 matching your filters

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  • High Performance Python I

    by Ian Ozsvald

    At EuroPython 2011 I ran a very hands-on tutorial for High Performance Python techniques. This updated tutorial will cover profiling, PyPy, Cython, numpy, NumExpr, ShedSkin, multiprocessing, ParallelPython and pyCUDA. Here's a 55 page PDF write-up of the EuroPython material: http://ianozsvald.com/2011/07/25...

    At EuroPython 2011 I ran a very hands-on tutorial for High Performance Python techniques. This updated tutorial will cover:

    • profiling with cProfile, run_snake and line_profiler
    • PyPy
    • Cython
    • numpy with and without vectors
    • NumExpr
    • ShedSkin Py->C++ compiler
    • multiprocessing for multi-core
    • ParallelPython for multi-machine
    • pyCUDA demos

    I plan to expand the original material and to maybe also cover other tools like execnet and PyPy-numpy.

    At 9:00am to 12:20pm, Thursday 8th March

    In D2, Santa Clara Convention Center

  • Optimize Performance and Scalability with Parallelism and Concurrency

    by Robert Hancock

    From how the operating system handles your requests through design principles on how to use concurrency and parallelism to optimize your program's performance and scalability. We will cover processes, threads, generators, coroutines, non-blocking IO, and the gevent library.

    How processes, threads, coroutines, and non-blocking IO work from the operating system through code implementation and design principles to optimize Python programs. The difference between parallelism and concurrency and when to use each.

    The premise is that to make an informed decision you need to know what is happening under the hood. Once you understand the low level functionality, you can make the correct decision in the design phase.

    The emphasis is on practical application to solve real world problems.

    Outline

    • How the operating system handles traps and interrupts
    • Scheduling
    • Processes
    • Threads
    • The GIL
    • Generators
    • What is a coroutine?
    • What is a Python coroutine?
    • Blocking/Non-blocking I/O.
    • Parallelism versus Concurrency
    • How do these work with CPython, Pypy, and Stackless
    • Greenlets and libevent (gevent)
    • Design principles
    • Example networked application
    • Performance results
    • What are other the other options?

    At 9:00am to 12:20pm, Thursday 8th March

    In F2, Santa Clara Convention Center

  • High Performance Python II

    by Travis Oliphant

    In this tutorial, I will cover how to write very fast Python code for data analysis. I will briefly introduce NumPy and illustrate how fast code for Python is written in SciPy using tools like Fwrap / F2py and Cython. I will also describe interesting new approaches to creating fast code that is leading changes to NumPy on a fundamental level.

    In this tutorial, I will cover how to write very fast Python code for data analysis including making use of NumPy and using GPUs. I will largely focus on writing extensions to Python using hand-wrapping and Cython but will touch also on using tools like weave, Instant, ShedSkin and compare them to PyPy. I will also spend the last part of the tutorial on using GPUs with Python and discuss the performance trade-offs of the technology. This will be a high-level overview of the space with deep dives in Cython and GPUs

    Outline:

    • Brief Introduction to NumPy, SciPy and array-oriented computing with Python including exercises (1 hour)
    • Introduction to hand-wrapping and extending Python (1 hour)
    • Detailed description of Cython and how to use it to connect to machine-compiled code (1 hour)
    • Detailed description of GPUs and how to use them best with NumPy (45 minutes)
    • Summary and overview of using Python to write super fast code (15 minutes)

    At 1:20pm to 4:40pm, Thursday 8th March

    In D2, Santa Clara Convention Center

    Coverage video