High Performance Computing And Big Data

Catalog description

High Performance Computing (HPC) has played an important role in the field of Artificial Intelligence due to its computation ability to the large size of data. This course will cover the current techniques applied to HPC and applications to Big Data analysis problems. The topics that will be covered in this course are parallel computing concepts and techniques and distributed Machine Learning using open-source distributed Machine Learning software packages. Permission of instructor required. Typically offered Fall Spring. Course Learning Outcomes 1. Compare and contrast the Implicit and Explicit Parallelism. 2. Explain the explicit parallel platforms including dichotomy of parallel computing platforms, communication model, network topologies, and communication costs. 3. Explain and implement the task dependency graph, task interaction graph, task decomposition, and mapping. 4. Understand the current technologies of modern Big Data landscape. 5. Understand the distributed file system and its functionality in Data Science. 6. Understand and implement HDFS and MapReduce. 7. Explain and compare Hadoop and Apache Spark. 8. Develop skills to use cloud based HPC in Data Science. 9. Explore, absorb, and retain the knowledge concerning leading-edge research in HCP and Data Science. View Class Schedule