High-Performance Computing in R for Genomic Research**
Author(s): Jiefei Wang
Affiliation(s): University at Texas Medical Branch
High-performance computing(HPC) has become an essential topic for handling large high-throughput data and bringing complex algorithms to life. However, the intricate nature of parallelization structures often hinders people from implementing the correct parallel computing cluster in R. In this talk, we will introduce the modern parallel framework package BiocParallel and its utility packages SharedObject and RedisParam. During our exploration of High-performance computing in R, we will dive into a variety of important topics, covering both foundational principles and practical applications. These include: 1. Understanding the core concepts of parallel computing 2. Creating a mini computing cluster utilizing home and office computing resources 3. Enhancing computational efficiency, managing errors, and debugging code 4. Harnessing the power of cloud computing Several real-study examples will be provided to illustrate the power of parallel computing. By the end of the talk, attendees should have a foundational understanding of parallel computing and be capable of creating a simple cluster for research purposes. We warmly invite R users of all skill levels to join us in expanding their knowledge in this dynamic field.