High Performance Programming Fundamentals

Dr. Devangi Parikh has established herself as an accomplished computer scientist at the University of Texas at Austin, where she currently serves as Assistant Professor of Instruction in the Department of Computer Science. Her academic journey includes a PhD in Electrical and Computer Engineering from Georgia Institute of Technology (2006-2012) and early career experience as a System Engineer at Texas Instruments, where she developed high-performance dense linear algebra libraries. Her research interests span high-performance computing, numerical software, and digital signal processing for speech and audio enhancement

Prof. Dr. Robert van de Geijn has established himself as a distinguished leader in computer science at the University of Texas at Austin, where he served until becoming Professor Emeritus in 2021. Born in the Netherlands in 1962, he earned his BS in Mathematics and Computer Science from the University of Wisconsin-Madison and PhD in Applied Mathematics from the University of Maryland, College Park. As leader of the Science of High-Performance Computing group and member of the Oden Institute for Computational Engineering and Science, he has made groundbreaking contributions to linear algebra, high-performance computing, and parallel processing. His pioneering work includes developing the Formal Linear Algebra Method (FLAME) and creating widely-adopted open-source software libraries. His research excellence has been recognized with numerous awards, including the 2020 SIAM Activity Group on Supercomputing Best Paper Prize and the 2007-2008 President's Associates Teaching Excellence Award. His influential publications, including "Anatomy of High-Performance Matrix Multiplication" and "The Science of Deriving Dense Linear Algebra Algorithms," have significantly advanced the field of computational science. Though officially retired, he maintains 25% research activity, continuing his influential work in algorithm development and high-performance computing education.

Dr. Maggie Myers has established herself as a distinguished educator and researcher at the University of Texas at Austin, where she served as a lecturer in both the Department of Computer Science and the Department of Statistics and Data Sciences. Her expertise spans probability and Bayesian statistical methods, mathematics education, curriculum development, informal learning, and formal derivation of algorithms. Throughout her career, she has made significant contributions to both education and research, co-authoring important works including "Linear Algebra: Foundations to Frontiers" and "The Science of Deriving Dense Linear Algebra Algorithms." Her earlier career included roles as a senior research scientist with the Charles A. Dana Center and consultant to the Southwest Educational Development Lab (SEDL). Her teaching portfolio encompasses undergraduate and graduate courses in Bayesian Statistics, while her research activities range from informal learning opportunities in mathematics education to formal derivation of linear algebra algorithms. Her innovative work in educational development, including the creation of MOOCs and comprehensive electronic textbooks, earned her the 2015 CNS Teaching Award. Her ongoing collaboration with Prof. van de Geijn, both in marriage and research, has produced numerous significant contributions to the field of high-performance computing and mathematics education.