[Crib-list] IAP: Performance Computing in a High Level Language (using Julia) (fwd)

[Shirley Entzminger]

Mathematics 18.S096 For Credit at MIT



Performance Computing in a High Level Language

Professors Steven G. Johnson, Alan Edelman, David Sanders



Tuesday-Friday, January 10-27, Room 2-135, 2pm-4pm (On Tues,Wed,Friday,
3:30-4 will be in class office hours)



MIT Registrar Page:
http://student.mit.edu/catalog/search.cgi?search=18.S096&style=verbatim



Many programmers are familiar with high-level dynamic/interactive computer 
languages such as Python, R, or Matlab. Traditionally, such languages 
approach the computer at a high level of abstraction, and performance 
optimization is mainly a matter of finding fast "black-box" library 
routines. In this course, we bridge the gap between high-level dynamic" 
languages and what is really happening at a low level. Using a new 
language called Julia, we show how one can simultaneously write 
high-level, generic, interactive programs that are also optimized for 
performance, and which implement their own "inner loops" without relying 
on external libraries.


Topics include how program objects are represented in memory (types 
"boxes," registers, etc.), processor architectures, memory locality,
metaprogramming and moving computations from runtime to
compile-time, parallel computing, sparse and dense linear algebra, machine
learning, gpu programming, and applications of numerical analysis.


Students should be comfortable with programming.


Syllabus

January 10:  Boxes, Types, and Registers (SJ)

January 11:  Types and Dispatch  (All)

January 12:  Lab Day

January 13:  Parallel Computing (AE)



January 17:  Memory Locality  (SJ)

January 18:  Metaprogramming (DS)

January 19:  Lab Day

January 20:  Sparse and Dense Linear Algebra (SJ)



January 24:   Machine Learning  (AE)

January 25:   GPU Programming (AE)

January 26:   Lab Day

January 27:   Applications to Numerical Programs (SJ)


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Massachusetts Institute of Technology
Cambridge, MA
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