About the course
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.
I am a second year PhD student at the Institute of Computational and Mathematical Engineering (ICME), at Stanford University. And if you wonder, this is an interdisciplinary program that gathers in the same place mathematicians, computer scientists, statisticians and engineers to solve any kind ...
Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.