Many scientific modelling programs heavily involve iterations, which often are the bottleneck of the code's performance.

OpenMP is a directive-based API (Application Programming Interface) for writing parallel programs on a shared-memory system. The common usage case is to accelerate nested loops by sharing workloads between multiple threads.

The workshop is designed to introduce some most common yet powerful OpenMP practices to scientists to quickly turn a serial iterative C code into parallel on a multi-core processor.

Prerequisites

Basic experience with C/C++ is required
Basic experience with bash or similar Unix shells.
User experience on Gadi is ideal. (Seminar on Getting Started with Gadi)

Learning Outcomes

At the completion of this training, you will be able to

know when to use OpenMP,
create Parallel Construct,
create a team of threads,
identify potential data race conditions,
distinguish data storage attributes,
understand how to split loop iterations to improve efficiency,
understand the limitations of multithreaded programming,
feel confident to advance to next-level parallel programming.

Contact the host of OpenMP Parallelism I: Managing Threads and Shared Memory

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