PyCompArch: Python-Based Modules for Exploring Computer Architecture Concepts
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1 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
2 Overview Ø Motivation Ø IPython Notebook Ø PyCompArch Concept Notebooks Ø Multiprocessing: Mandelbrot, Amdahl s, Speedup, and Efficiency Ø ParallelSchedule: Job Scheduling Ø Synthetic Speedup: Interactive Ø PyCompArch Concept Notebooks Ø Dynamic frequency scaling Ø Computer Vision (CV) Ø Code Box
3 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
4 IPython Notebook Overview Ø Ø Ø Ø Ø Ø Ø The IPython Notebook is a web-based interactive computational environment A well-structured code development environment A framework for observing and recording and results of code execution Linking text such as comments, equation generators for mathematics Embedded plots and other rich media formatting options 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. Changes can be rolled back also, encouraging experimentation without creating excessive copies of source material At Scale Learning MOOC
5 Example: Jupyter ( 5
6 Concepts PyCompArch Raspberry Pi Explore Parallel Explore Amdahl s Law DVFS Code Box Parameters Collect Results Matplotlib Visualization
7 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 Pythonbased architecture description language.
8 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
9 Mandelbrot Calculation : Block Size Ø Overhead of work assignment
10 Parallelism Python s Multiprocessing Module
11 Speedup and Efficiency
12 Base Graph
13 Parallelism Exploration Slider control in IPython figure
14 Parallel Exploration Ø Serial fraction Ø Overhead
15 Execution Time
16 Speedup
17 Efficiency
18 Amdahl Law s Evaluation
19 Amdahl Law s Evaluation
20 Single Job Timeline
21 Parallel Job Scheduling Timeline (8 cores)
22 Worker Workload Summary
23 Computer Architecture Education and Raspberry PI Ø Ø Center piece of course
24 Raspberry PI Stats Ø $35 is inexpensive must be a toy? Ø It s just a slightly underpowered 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
25 OpenCV Algorithm Evaluation on Raspberry Pi 25 Ø Evaluate selected set of various OpenCV functions Ø rotate, convolution, sobel, median blur, resize, histogram, erosion, etc Ø Benchmark various image file sizes: 720x x x x2160
26 Raspberry Pi- DFS (Dynamic Frequency Scaling)
27 Code Box Ø Code compiled
28 Code Box Ø Execution time observed
29 Summary and Future Plans Ø New opportunities for exploring concepts related to computer architecture Ø Theoretical concepts Amdahl s law Ø Evaluation of Python multiprocessing module for parallelism Ø Code Box Evaluation of performance of small code examples Ø Sharing content between developers [github] Ø Raspberry Pi support Ø Enhance development of projects and independent learning by having set functions for gathering performance results Ø Dynamic frequency scaling Ø OpenCV evaluation Ø Future work: Python-based Numba generates optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Ø Numba supports compilation of Python to run on either CPU or GPU hardware.
PyCompArch: Python-Based Modules for Exploring Computer Architecture Concepts
PyCompArch: Python-Based Modules for Exploring Computer Architecture Concepts Dan Connors, Kyle Dunn, and Ryan Bueter Department of Electrical Engineering University of Colorado Denver Denver, Colorado
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