For more experience with thread-level parallelism, see Old project 2 on Java threading, which would be good practice.

Office hours

Person Email Location Hours
Daniel Baker dnb@cs.jhu.edu Malone 263/222 Monday/Wednesday 6-7
Randal Burns randal@cs.jhu.edu Malone 227 Thursday 3-4:30
Rohan Tilva rtilva1@jhu.edu Malone 1 ugrad lab Tuesday 5-6
Sofya Freyman sfreyma2@jhu.edu Malone 1 ugrad lab Tuesday 5-6
Will David wdavid2 Malone 1 ugrad lab Tuesday 5-6
James Lubowsky jlubows1@jhu.edu Malone 1 ugrad lab Monday 6-7:30
Jason Zhang jzhan127@jhu.edu Malone 1 ugrad lab Monday 6-7:30
Joseph Naness rnaness1 Malone 1 ugrad lab Monday 1:30-2:30
  • Note: All times are P.M.

Schedule, Lectures, and Assignments

The Jupyter notebooks for lectures

(28 and 30 January) Introduction, Moore’s Law, Amdahls Law

The impact of multi-core and pipelines on processing power. Moore’s law reloaded. Overview of modern parallel architectures. Comparing concurrency and parallelism.

  • Reading:
    • Chapter 1, Patterns for Parallel Programming
  • Lectures 1-3

(4 February) Metrics for Parallelism

Factors against parallelism, scaleup and parallel efficiency.

  • Reading:
    • Chapter 2, Patterns for Parallel Programming
  • Lectures 4-5

(6 February) OpenMP

An introduction to parallelizing serial programs based on compiler directives. Also serial equivalence, loop parallel constructs, and independent loops.

(11 February) Class cancelled (snow)

(13 February) OpenMP Loop Optimization (Lecture 8 and openmp/omp_c/stencil.c)

(18 Februrary) Loop Dependencies and Cache Hierarchy

(20 February) Parallel Architectures and OS Constructs

Flynn’s taxonomy. The differences between message passing and shared memory machines. SMPD. Clusters. Hybrid architectures. The parallel memory model and cache hierarchy.

  • Lecture 11

Operating system process abstraction: contexts and context switching, virtual memory, program organization in memory. Operating system threads, thread context, thread memory model.

  • Lecture 12

(25 February) Java Threads and Java Synchronize

Asynchrony, waiting on threads, volatile variables, and synchronized functions.

  • Lectures 13 and 14

(27 February) Assessment #1 and Introduction to Synchronization

Guidance on Assessment 1

MILK!

  • Lectures 14 and 15

(6 March) Top500 and MPI Introduction

Supercomputers of the world and trends in architecture. MPI Overview, the MPI Runtime Environment.

  • Reading:
  • Project 2 released.
  • Lectures 15, 16, 17.

(11 March) MPI Messaging and Barriers

MPI Synchronous Messaging, Asynchronous I/O, and Barriers.

(13 March) TOP 500 Priority Inversion and Deadlock

(18 and 20 March) Spring Break

(25 March More MPI I/O) Lecture 21

(27 March) HPC Checkpoints and I/O Performance. Lecture 22.

(April 1) Assessment #2

(3 April) Introduction to Map/Reduce and Hadoop!

The Google Parallel computing environment, functional programming concepts applied to large-scale parallelism, text processing.

(8 April) Map Reduce Systems and Semantics!

Map/Reduce runtime, scalability and reliability, the Google File System, HDFS. Sorting, partitions, and combiners.

(10 April) Spark

Spark and RDD’s.

(15 April) Parallel and Distributed Computing in Python with Dask

Dask parallelizes Python libraries like NumPy, pandas, and scikit-learn, bringing a popular data science stack to the world of distributed computing. This talk will provide an overview of how Dask works then demonstrate Dask in action, showing how it can be used to transparently scale from a single thread of execution on a laptop to many parallel processes running across a cluster. Dask has been used on production systems with over 1000 cores, and works well for computational units in the millisecond range.

Lecturer: Ian Stokes-Rees

Ian Stokes-Rees is a Principal Analytics Consultant at BCG. He is a professional software engineer and computational scientist. Prior to joining BCG Ian spent 5 years at Anaconda where he worked as a product manager, engineer, and evangelist for open source Python data science. Ian has a PhD in Particle Physics from Oxford, where he worked on the CERN LHCb computing team to develop the experiment’s distributed computing framework. This is where Ian first started using Python and he’s been in love with it ever since. Ian hails from Canada and has an undergraduate and Master’s degree in Electrical Engineering from the University of Waterloo.

(17 April) Introduction to CUDA and GPU

Lectures forthcoming.

(22 April) Assessment #3: Hadoop! and Spark

(24 April) Roofline