PyCompArch: Python-Based Modules for Exploring Computer Architecture

Concepts

Dan Connors, Kyle Dunn, Ryan Bueter Department of Electrical Engineering

University of Colorado Denver

Workshop on Computer Architecture Education 2015

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

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

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

Example: Jupyter (www.jupyter.org) 5

PyCompArch

Matplotlib

Visualization

Raspberry Pi Explore

Collect Results

DVFS

Concepts Explore

Code Box

Parallel

Amdahl’s Law

Parameters

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.

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

Mandelbrot Calculation : Block Size

Ø Overhead of work assignment

Parallelism – Python’s Multiprocessing Module

Speedup and Efficiency

Base Graph

Parallelism Exploration

Slider control in IPython figure

Parallel Exploration

Ø Serial fraction

Ø Overhead

Execution Time

Speedup

Efficiency

Amdahl Law’s Evaluation

Amdahl Law’s Evaluation

Single Job Timeline

Parallel Job Scheduling Timeline (8 cores)

Worker Workload Summary

Computer Architecture Education and Raspberry PI

Ø http://www.raspberrypi.org Ø Center piece of course

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

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: 720x480 1280x720 1920x1080

3840x2160

Raspberry Pi- DFS (Dynamic Frequency Scaling)

Code Box Ø Code compiled

Code Box Ø Execution

time observed

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.