Systems, architectures, algorithms, programming models, languages and software tools. Topics covered include parallelization and distribution models (MPI, Map-Reduce, etc.); Parallel architectures; Cluster and parallel and distributed computing systems, distributed and parallel algorithms, data structures and programming methodologies; applications; And performance analysis.
- Georg Hager and Gerhard Wellein. Introduction to High-Performance Computing for Scientists and Engineers. CRC Press, Boca Raton, FL, 1 edition, July 2010.
- Victor Eijkhout. Introduction to High-Performance Scientific Computing. lulu.com, Raleigh, N.C., January 2015.
- K. G. Srinivasa and Anil Kumar Muppalla. Guide to High-Performance Distributed Computing: Case Studies with Hadoop, Scalding, and Spark. Springer, New York, NY, 2015 edition, February 2015.
- Mahmoud Parsian. Data Algorithms: Recipes for Scaling Up with Hadoop and Spark. O’Reilly Media, Sebastopol, 1 edition, July 2015.
- Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills. Advanced Analytics with Spark: Patterns for Learning from Data at Scale. O’Reilly Media, Beijing, 1 edition, April 2015.
- Pethuru Raj, Anupama Raman, Dhivya Nagaraj, and Siddhartha Duggirala. High-Performance Big-Data Analytics: Computing Systems and Approaches. Springer, S.l., 2015 edition, August 2015.
- Vijay Srinivas Agneeswaran. Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives. Pearson FT Press, Upper Saddle River, 1 edition, May 2014.
This course is regularly offered once a year at CEFET/RJ (PPCIC).
Slides and schedule available at Moodle.