High Performance or Extreme Scale Computing and

Big Data Driven Systems

Professor Craig C. Douglas

http://www.mgnet.org/~douglas/Classes/hpc-xtc

 

General Descriptions

Courses on high performance computing, extreme technical computing, Big Data, and dynamic data driven application simulation are collected here since they have overlapping interests and resources.

High Performance Computing - eXtreme Technical Computing

Course Description

A course in high performance computing technology, with an emphasis on using research computing systems, focusing primarily on hardware architectures. History of high performance computing. Hardware architectures, processor design, cache and memory architectures. Hardware counters. Processing benchmarks. Energy aspects of HPC systems. Performance of real applications.

Prerequisites

Programming experience and familiarity with basic discrete and numerical algorithms.

Taught at and when

Designing and Building Applications for Extreme Scale Systems

Course Description

Learn how to design and implement applications for extreme scale systems, including analyzing and understanding the performance of applications, the primary causes of poor performance and scalability, and how both the choice of algorithm and programming system impact achievable performance. The course covers multi-and many-core processors, interconnects in HPC systems, parallel I/O, and the impact of faults on program and algorithm design.

Prerequisites

Strong knowledge of C, C++, or Fortran, including writing, debugging, and optimizing an application. Some parallel programming experience is desirable. An understanding of basic computer architecture is strongly recommended.

Taught at and when

Big Data and Dynamic Data Driven Apps

Course Description

Dynamic Data-Driven Application Systems (DDDAS) is a paradigm whereby applications and measurements become a symbiotic feedback control system with the ability to dynamically incorporate additional Big Data into executing applications and dynamically steer the measurement process, which provides more accurate analysis and prediction, more precise controls, and more reliable outcomes.

Prerequisites

An eclectic group of students with varied backgrounds so that a computational science project can be completed as one or more teams. Some programming experience is helpful.

Taught at and when

Cheers,
Craig C. Douglas

Last modified: