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PyCompArch: Python-Based Modules for Exploring Computer Architecture - - PowerPoint PPT Presentation
PyCompArch: Python-Based Modules for Exploring Computer Architecture - - PowerPoint PPT Presentation
PyCompArch: Python-Based Modules for Exploring Computer Architecture Concepts Workshop on Computer Architecture Education 2015 Dan Connors, Kyle Dunn, Ryan Bueter Department of Electrical Engineering University of Colorado Denver Overview
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Motivation
Ø Visualization of concepts of parallelism
Ø Explore concepts from learner’s perspective versus single static graph of parameters
Ø Explore/perform experiments on hands-on devices
Ø Raspberry PI Ø Streamline collection, comparison, and visualization
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IPython Notebook Overview
Ø The IPython Notebook is a web-based interactive computational environment Ø A well-structured code development environment Ø A framework for observing and recording and results of code execution Ø Linking text such as comments, equation generators for mathematics Ø Embedded plots and other rich media formatting options Ø The cloud coding advantage is that the IPython Notebook Viewer renders the code as a web page and users can read and interact with a remote system without having to install anything on their device. Ø Changes can be rolled back also, encouraging experimentation without creating excessive copies of source material
At Scale Learning – MOOC
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Example: Jupyter (www.jupyter.org)
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PyCompArch
Matplotlib Visualization
Raspberry Pi Explore
Collect Results
DVFS
Concepts Explore
Code Box
Parallel Amdahl’s Law
Parameters
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Python
Ø IPython Notebooks
Ø Interactive lectures Ø Code and see: embed visualization of parallel performance trade-offs Ø Assignment template
Ø Related work: ISCA 2015 Workshop PyMTL and Pydgin Tutorial: Python Frameworks for Highly Productive Computer Architecture Research
Ø PyMTL is a hardware modeling framework Ø Pydgin is a framework for rapidly developing instruction-set simulators (ISSs) from a Python- based architecture description language.
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IPython
Ø Computer Architecture Concepts - Python Parallelism
Ø Multithreading and multiprocessing support Ø Speedup and efficiency Ø Amdahl’s law Ø Overhead
Ø Explore Experiments
Ø Benchmarking (Example: OpenCV) Ø Collecting, displaying, and comparing results Ø Dynamic frequency scaling on Raspberry Pi
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Mandelbrot Calculation : Block Size
Ø Overhead of work assignment
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Parallelism – Python’s Multiprocessing Module
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Speedup and Efficiency
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Base Graph
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Parallelism Exploration
Slider control in IPython figure
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Parallel Exploration
Ø Serial fraction Ø Overhead
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Execution Time
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Speedup
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Efficiency
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Amdahl Law’s Evaluation
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Amdahl Law’s Evaluation
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Single Job Timeline
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Parallel Job Scheduling Timeline (8 cores)
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Worker Workload Summary
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Computer Architecture Education and Raspberry PI
Ø http://www.raspberrypi.org Ø Center piece of course
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Raspberry PI Stats
Ø $35 is inexpensive…must be a toy? Ø “It’s just a slightly under- powered computer without a screen, and anything you can do on it you could do on a laptop” Ø True, but also:
Ø Students can’t destroy the systems with software. Ø It changes mindsets because the systems are easily accessible.
Ø What about Arduino?
Ø Similar aims, but Raspberry Pi runs Linux- full operating system, is a modern 32-bit ARM processor
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OpenCV Algorithm Evaluation on Raspberry Pi
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Ø Evaluate selected set of various OpenCV functions
Ø rotate, convolution, sobel, median blur, resize, histogram, erosion, etc Ø Benchmark various image file sizes: 720x480 1280x720 1920x1080 3840x2160
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Raspberry Pi- DFS (Dynamic Frequency Scaling)
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Code Box
Ø Code compiled
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Code Box
Ø Execution time
- bserved
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