High Performance Parallel Computing

Computational methods are growing increasingly important in many areas of science, and the solution to many problems depend on computers that are vastly faster and holds more memory than what a single high-end server can offer. Top supercomputers consist of more than a hundred million processor cores working in parallel, and soon a top machine will host more than a billion processors. Programming such highly parallel computers is difficult, and ensuring both program correctness and high performance is non-trivial. In this course, students will learn how computers are build, from the individual CPU and up to millions of processor cores. Students will learn to program high performance applications with both accelerators (GPUs), shared memory and explicit message passing paradigms. The theory is put in to practice through hands-on exercises, and students will learn to map algorithms to parallel architectures and how to decompose problems for parallel execution. We will use the ERDA MODI cluster to execute the programs on a real high performance computing infrastructure and evaluate both performance, scalability and correctness of the programs. The hands-on exercises use examples from astrophysics, biophysics, geophysics, high-energy physics, and solid-state physics. The numerical methods for each week are chosen to be well-suited to each parallel architecture. During the exercises we will each week introduce a new tool, such as debuggers, profilers, and parallel correctness checkers, to aid in the development of high performing programs.

We will use C++ as the course language, and small python programs for visualizing the data. The first week is dedicated to introducing C++ and general programming.