|Day 1 of 2|
Objective: This section introduces a parallel approach to running MATLAB code through the use of multiple MATLAB sessions. Interactive techniques for prototyping in a parallel environment are highlighted. The concepts in this section also introduce several ideas explored throughout the course.
|Speeding up Computations||
Objective: This section outlines the key steps for running parallel computations in a batch environment. The emphasis is on interacting with the various Parallel Computing Toolbox objects to create and run jobs that run in batch.
Objective: This section identifies important considerations for programming task-parallel jobs including decomposing a problem and partitioning input. Through use of a hands-on example, it also explores various techniques typically employed to achieve speedup.
|Day 2 of 2|
|Working with Large Data Sets||
Objective: This section explores working with arrays in a parallel environment, with an emphasis on parallel algorithms. Splitting large datasets across multiple instances of MATLAB, as well as simultaneously performing the same operation on the various portions, will be key themes. This chapter concludes by running prototyped code in a batch parallel job.
Objective: This section explores the important programming considerations for parallel jobs. In addition, this section introduces using the communication features in parallel jobs for creating special architectures to solve specific types of parallel problems.
|Increasing Scale with Multiple Systems||
Objective: This section demonstrates tools for harnessing the power of multiple systems on a network for running code. Highlighted in the chapter are techniques for working with a heterogeneous mix of systems, and special features that are available to a cluster of computer systems.