1. ITC Research Computing Support Using Matlab Effectively By: Ed Hall Research Computing Support Center Phone: 243-8800 Φ Fax: 243-8765 E-Mail: Res-Consult@Virginia.EDU http://www.itc.Virginia.edu/researchers

2. Matlab Overview

6. UVA Matlab Resource webpage www.itc.virginia.edu/research/matlab/ Matlab tutorial: Getting Started www.mathworks.com/access/helpdesk/help/techdoc/learn_matlab/le arn_matlab.shtml www.mathworks.com/access/helpdesk/help/techdoc/learn_matlab/le arn_matlab.shtml

7. Matlab Programming

8. Matlab M-files

9. Matlab Function M-files

12. Maximizing MATLAB Performance Techniques for Improving Performance Performance Acceleration Sample Accelerated Program Measuring Performance Making Efficient Use of Memory

13. Techniques for Improving Performance Vectorizing loops

14. Vectorization (cont.) Logical indexing of arrays

15. Vectorization (cont.)

16. Preallocating Arrays Avoids overhead of dynamic resizing Reduces memory fragmentation Use array appropriate preallocation

17. Preallocation (cont.) Preallocating a Nondouble Matrix Use repmat to enlarge arrays

18. Other Ways to Speed Performance Coding loops in a MEX-file for speed using Matlab compiler Functions M-files are faster than scripts Load and Save are faster than File I/O Functions Avoid large background processes

19. Performance Acceleration JIT-Accelerator, new in Matlab 6.5 Make MATLAB as fast as C and Fortran Most improvement in self-contained loops As fast as vectorized code Improvement may vary depending on code

20. What MATLAB Accelerates Performance enhancement supported on subset of Matlab

21. What Matlab Accelerates (cont.) Arrays shapes of <= 3 dimensions for loops faster as long as: Conditional statements (if, elseif, while, switch) that evaluate to a scalar Overhead of manipulating small arrays

22. What MATLAB Doesn’t Accelerate Some data types and array shapes Calls to other functions (M-File of MEX) More than one operation on a line First instruction disqualifies entire line

23. What MATLAB Doesn’t Accelerate Changing data types or variable shapes Using complex constants in scripts

24. Perfomance Acceleration (cont.) How Vectorization and Preallocation fit in Acceleration functionally equivalent to vectorization Choose method most understandable Preallocation still important Avoid running in debug mode Aviod command echoing

25. Measuring Performance Profiling measure where program spends the most time Create simple implementation dealing with obvious performance issues Use profiler to identify performance bottlenecks (80/20 rule) Identify time-costly functions and where they are called

26. Measuring Performance (cont.) Problems identified by profiler solved by: Avoiding unnecessary computation Changing algorithm to avoid time-costly functions Avoiding recomputation by storing results for future use Want most time spent on calls to small number of builtin functions

27. Matlab Profiler GUI interface to profile function See “Matlab News & Notes” example www.mathworks.com/company/newsletter/may03/profiler.shtml Process for Improving Performance

28. Matlab Profiler (cont.)

29. Matlab Profile Function Command line profiler >>profile on –detail builtin –history >>(Execute your M-file) >>profile report >>profile off Some features not in GUI Profiler

30. Making Efficient Use of Memory Memory management functions

31. Memory Management Functions (cont.)

32. Ways to Conserve Memory

33. Compressing data in memory Memory fragmentaion prevents contiguous memory block to store new large variable “Out of Memory” message Pack function compresses data in memory, freeing large contiguous blocks

34. Ways to Conserve Memory

35. Unix-Specific Memory Topics Freeing clear memory on Unix Matlab does not free up cleared memory in Unix until it Matlab exits Save workspace variables Exit Matlab Startup Matlab Load variables back in new workspace

36. Unix-Specific Memory Topics Additional Memory used to execute external commands “Out of Memory” error when executing Unix command from within Matlab (using shell escape (!) operator) Unix allocates as much memory to subprocess as parent process

37. Unix-Specific Memory Topics Reusing Heap Memory Matlab request memory from Unix when not enough available in Matlab heap

38. Matlab Compiler Converts function M-files to C or C++ source code Uses native compiler to create executables Creates MEX-file from function M-file, e.g. function.m >>mcc –x function Creates stand-alone executable from function M-file >>mcc –m function

39. Matlab Compiler

42. Unix-Specific Compiler Topics Setting LD_LIBRARY_PATH (e.g. in.kshrc) to locate shared libraries for stand-alone executables

43. Running Matlab in Batch Mode On a Unix platform

44. Further Resources Using Matlab: Programming and Data Types www.mathworks.com/access/helpdesk/help/techdoc/matlab_prog/matla b_prog.shtml Code Vectorization Guide www.mathworks.com/support/tech-notes/v5/1100/1109.shtml Matlab Compiler User’s Guide www.mathworks.com/access/helpdesk/help/pdf_doc/compiler/compi ler3.pdf www.mathworks.com/access/helpdesk/help/pdf_doc/compiler/compi ler3.pdf