COMP6464 High Performance Scientific Computing

COMP6464 is only available under certain award programs.

Offered By	Department of Computer Science
Academic Career	Graduate Coursework
Course Subject	Computer Science
Offered in	First Semester, 2010
Unit Value	6 units
Course Description	This course provides an introduction to High Performance Computing with an orientation towards applications in science and engineering. Aspects of numerical computing and the design and construction of sophisticated scientific software will be considered. The focus will be on the C and C++ programming languages, although reflecting the reality of modern scientific computation this course will also touch on other languages such as Python, Java and FORTRAN95. The course will study high performance computer architectures, including modern parallel processors, and will describe how an algorithm interacts with these architectures. It will also look at practical methods of estimating and measuring algorithm/architecture performance. The following topics will be addressed: the C++ programming language; basic numerical computing from aspects of floating point error analysis to algorithms for solving differential equations; the engineering of scientific software; general high performance computing concepts and architectural principles; modern scalar architectures and their memory structure; performance and programmability issues, and program analysis techniques for high performance computing; parallel computing paradigms and programming using the OpenMP standard; trends in HPC systems.
Learning Outcomes	Upon completion of the course, students should: appreciate the building blocks of scientific and engineering software. be able to apply a basic knowledge of numerical computing using an appropriate programming language. be competent in experimental computing in a numerical context and of the optimisation of algorithms on high performance architectures. be able to reason about the accuracy of mathematical and numerical models of real physical phenomena. have an awareness of the modern field of computational science and engineering and of the impact of high performance computing on science and industry. have an understanding of the various paradigms of high performance computing and their potential for performance and programmability. be able to write algorithms yielding good performance on high-performance architectures, and to be able to estimate and evaluate their performance.
Indicative Assessment	Assignment (40%); Mid semester exam (10%); Final Exam (50%)
Course Classification(s)	AdvancedAdvanced courses are designed for students having reached 'first degree' level of assumed knowledge, which provide a deep understanding of contemporary issues; or 'second degree' and higher levels of knowledge; or for transition to research training programs. and TransitionalTransitional courses are designed for students from a broad range of backgrounds and learning achievements, which provide for the acquisition of generic skills; or an informed understanding of contemporary issues; or fundamental knowledge for transition to Advanced or Specialist courses.
Areas of Interest	Computer Science and Information Technology
Prescribed Texts	Buyya, R. High Performance Cluster Computing: Programming and Applications, Prentice Hall, Upper Saddle River, New Jersey 1999. Dowd, K. & Severance, C. High Performance Computing, 2nd edition, O'Reilly & Associates Inc, 1998. Fosdick, L.D. Jessup, E.R., Schauble, C.J.C. & Domik,G., An Introduction to High-Performance Scientific Computing, The MIT Press, 1996. Heath, M.T. Scientific Computation - An Introductory Survey, McGraw-Hill, 1997.

The information published on the Study at ANU 2009 website applies to the 2009 academic year only. All information provided on this website replaces the information contained in the Study at ANU 2008 website.