WSP 368 — Introduction to High-Performance Computing (ICME Workshop)
Big data has changed the way we work, live, and play. Data science—developing and testing models and algorithms—helps us gain knowledge for ourselves and provide insights to others.
Introduction to High-Performance Computing is one of ten workshops included in Fundamentals of Data Science, a series of one-day workshops offered by the Stanford Institute for Computational and Mathematical Engineering (ICME). Fundamentals of Data Science provides an introduction to multiple aspects of data science for those who are new to the field and those seeking to broaden their education and skills in data science. Students can sign up for one workshop, or several throughout the week. Students who complete four workshops will qualify for the Stanford ICME Fundamentals of Data Science Summer Workshops Certificate of Completion.
These workshops are not eligible for tuition discounts through Stanford Continuing Studies, but ICME offers discounts for eligible affiliates and partners. See below for details:
Stanford staff and full-time Stanford students: You may be eligible for a tuition discount if you register directly through ICME. The tuition for Stanford staff is $100 and the tuition for full-time Stanford students is $75. For more information and to register with these discounts, visit https://sto.stanfordtickets.org/icme2020/homepage.
ICME partners and affiliates qualify for a discount. If you think you qualify and have not received a discount code separately, email email@example.com. You will need to register through ICME's website.
Workshop: Introduction to High-Performance Computing
Instructor: Cindy Orozco, PhD Student, ICME, Stanford
In the past 50 years, supercomputers have achieved what was once considered only possible in Sci-Fi movies. The key to the tremendous success of supercomputers has been a combination of outstanding architectures plus software that uses all the available resources and makes parallelization possible. This secret sauce has led to different implementations across fields. A mechanical engineer would use MPI and OpenMP to have a balance between computations and memory load to deal with millions of nodes in physical simulations, whereas a data scientist would use MapReduce and Spark to have an adaptable and resilient algorithm for the challenges of big data. This workshop explores the key features of these two approaches, explaining their underground philosophy and how they use the architecture. The final goal is to give the student a taste of the different programming paradigms and the tools to decide which is the best approach.
Please note: Although the enrollment limit for this workshop is set to 30 Continuing Studies students, this course is designed for the entire Stanford community, and enrolled Continuing Studies students will be joined in the classroom by Stanford graduates and undergraduates. Students should expect a large class.