Parallel computing is the business of breaking a large problem into tens, hundreds, or even thousands of smaller problems which can then be solved at the same time using a cluster of computers, or supercomputer. It can reduce processing time to a fraction of what it would be on a desktop or workstation, or enable you to tackle larger, more complex problems. It’s widely used in big data mining, AI, time-critical simulations, and advanced graphics such as augmented or virtual reality. It’s used in fields as diverse as genetics, biotech, GIS, computational fluid dynamics, medical imaging, drug discovery, and agriculture.

Parallel programming techniques will be introduced as a natural extension to sequential programming using the message-passing interface (MPI). MPI allows usage of more than one computer/machine to solve a single problem by facilitating communication among these several computers over the network. By breaking up a problem into smaller chunks, each machine can work on a smaller subset of the bigger problem. Moreover, MPI also provides a way for different machines to communicate and synchronize the results as and when needed. MPI has been very successfully used with compiler-based languages such as C, Fortran, and C++.

Prerequisites: Before you take this training you should...

• have taken ACENET's training "Overview of Parallel Computing"
• have familiarity with either C, C++, or Fortran

This session will take place on:

• Tuesday, June 18, 2:00--4:00 pm
• Thursday, June 20, 2:00--4:00 pm

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