University:
Boston University
Course:
MET CS 201 | Introduction to Programming
Academic year:

2024
Views:

257

Pages:

17
Author:

iou1234yvov

Tuning MATLAB for Better Performance SOR Update Function How to vectorize it ? 1. Remove the for-loops 2. Define i = ib:2:ie; 3. Define j = jb:2:je; 4. Use sum for del % original code fragment jb = 2; je = n+1; ib = 3; ie = m+1; for i=ib:2:ie for j=jb:2:je up = ( u(i ,j+1) + u(i+1,j ) + ... u(i-1,j ) + u(i ,j-1) )*0.25; u(i,j) = (1.0 - omega)*u(i,j) +omega*up; del = del + abs(up-u(i,j)); end end % equivalent vector code fragment jb = 2; je = n+1; ib = 3; ie = m+1; i = ib:2:ie; j = jb:2:je; up = ( u(i ,j+1) + u(i+1,j ) + ... u(i-1,j ) + u(i ,j-1) )*0.25; u(i,j) = (1.0 - omega)*u(i,j) + omega*up; del = sum(sum(abs(up-u(i,j)))); Tuning MATLAB for Better Performance Solution Contour Plot Tuning MATLAB for Better Performance SOR Timing Benchmarks Tuning MATLAB for Better Performance Summation  For global sum of 2D matrices: sum(sum(A)) or sum(A(:)) Example: which is more efficient ? A = rand(1000); tic,sum(sum(A)),toc tic,sum(A(:)),toc No appreciable performance difference; latter more compact. Your application calls for summing a matrix along rows (dim=2) multiple times (inside a loop). Example: A = rand(1000); tic, for t=1:100,sum(A,2);end, toc  MATLAB matrix memory ordering is by column. Better performance if sum by column. Swap the two indices of A at the outset. Example: B=A’; tic, for t=1:100, sum(B,1);end, toc (See twosums.m) Tuning MATLAB for Better Performance Logical Array helpful for Vectorization  Sometimes, logical array is used to retain target array’s shape Scalar example: a = rand(4,3); b =rand(size(a)); c = zeros(size(b)); b(1,3) = 0; b(3,2) = 0; for j=1:3 for i=1,4 if (b(i,j) ~= 0) then c(i,j) = a(i,j)/b(i,j); end end end Vector example: % e is true (1) for all b not = 0 e = b~=0 e = 1 1 0 1 1 1 1 0 1 1 1 1 c(e) = a(e)./b(e) % c = 0 ∀ b = 0 c = 0.9768 2.3896 0.7821 11.3867 1.4940 0.4487 0 0.0400 0 0.0943 0.2180 1.2741 Tuning MATLAB for Better Performance Other Tips  Generally better to use function rather than script  Script m-file is loaded into memory and evaluate one line at a time.     Subsequent uses require reloading.  Function m-file is compiled into a pseudo-code and is loaded on first application. Subsequent uses of the function will be faster without reloading.  Function is modular; self cleaning; reusable. Global variables are expensive; difficult to track. Don’t reassign array that results in change of data type or shape Limit m-files size and complexity Structure of arrays more memory-efficient than array of structures Tuning MATLAB for Better Performance Memory Management  Maximize memory availability.  32-bit systems < 2 or 3 GB  64-bit systems running 32-bit MATLAB < 4GB  64-bit systems running 64-bit MATLAB < 8TB (96 GB on some Katana nodes)  Minimize memory usage. (Details to follow …) Tuning MATLAB for Better Performance Minimize Memory Usage  Use clear, pack or other memory saving means when possible. If double precision (default) is not required, the use of ‘single’ data type could save substantial amount of memory. For example, >> x=ones(10,'single'); y=x+1; % y inherits single from x  Use sparse to reduce memory footprint on sparse matrices >> n=3000; A = zeros(n); A(3,2) = 1; B = ones(n); >> tic, C = A*B; toc % 6 secs >> As = sparse(A); >> tic, D = As*B; toc % 0.12 secs; D not sparse  Be aware that array of structures uses more memory than structure of arrays. (pre-allocation is good practice too for structs!) Tuning MATLAB for Better Performance Minimize Memory Uage  For batch jobs, use “matlab –nojvm …” saves lots of memory  Memory usage query For Linux: scc1% top For Windows: >> m = feature('memstats'); % largest contiguous free block Use MS Windows Task Manager to monitor memory allocation.  On multiprocessor systems, distribute memory among processors Tuning MATLAB for Better Performance Compilers  mcc is a MATLAB compiler:  It compiles m-files into C codes, object libraries, or stand-alone executables.  A stand-alone executable generated with mcc can run on compatible platforms without an installed MATLAB or a MATLAB license.  On special occasions, MATLAB access may be denied if all licenses are checked out. Running a stand-alone requires NO licenses and no waiting.  It is not meant to facilitate any performance gains.  coder ― m-file to C code converter Tuning MATLAB for Better Performance mcc example How to build a standalone executable on Windows >> mcc –o twosums –m twosums How to run executable on Windows’ Command Promp (dos) Command prompt:> twosums 3000 2000 Details: • twosums.m is a function m-file with 2 input arguments • Input arguments to code are processed as strings by mcc. Convert with str2double: if isdeployed, N=str2double(N); end • Output cannot be returned; either save to file or display on screen. • The executable is twosums.exe Tuning MATLAB for Better Performance MATLAB Programming Tools  profile - profiler to identify “hot spots” for performance enhancement.  mlint - for inconsistencies and suspicious constructs in m-files.  debug - MATLAB debugger.  guide - Graphical User Interface design tool. Tuning MATLAB for Better Performance MATLAB Profiler To use profile viewer, DONOT start MATLAB with –nojvm option >> profile on –detail 'builtin' –timer 'real' >> serial_integration2 % run code to be profiled >> profile viewer % view profiling data Turn on profiler. Time reported >> profile off % turn off profiler in wall clock. Include timings for built-in functions. Tuning MATLAB for Better Performance How to Save Profiling Data Two ways to save profiling data: 1. Save into a directory of HTML files Viewing is static, i.e., the profiling data displayed correspond to a prescribed set of options. View with a browser. 2. Saved as a MAT file Viewing is dynamic; you can change the options. Must be viewed in the MATLAB environment. Tuning MATLAB for Better Performance Profiling – save as HTML files Viewing is static, i.e., the profiling data displayed correspond to a prescribed set of options. View with a browser. >> profile on >> serial_integration2 >> profile viewer >> p = profile('info'); >> profsave(p, ‘my_profile') % html files in my_profile dir Tuning MATLAB for Better Performance Profiling – save as MAT file Viewing is dynamic; you can change the options. Must be viewed in the MATLAB environment. >> profile on >> serial_integration2 >> profile viewer >> p = profile('info'); >> save myprofiledata p >> clear p >> load myprofiledata >> profview(0,p) Tuning MATLAB for Better Performance MATLAB Editor MATLAB editor does a lot more than file creation and editing …  Code syntax checking  Code performance suggestions  Runtime code debugging