Post on 11-Jun-2020
transcript
1© 2015 The MathWorks, Inc.
How to speed up MATLAB
Isaac NohApplication Engineer
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Agenda
Two coding tips to speed up MATLAB code
Leveraging multiple cores with MATLAB
MATLAB to C:– Generating C code from MATLAB– Integrate C functions in MATLAB
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Two Coding Tips for Speeding Up MATLAB
Pre-Allocation of Memory
Vectorization
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Pre-Allocation of Memory
for id = 1:10000a(id) = rand();
end
vs.
a = nan(1, 10000);for id = 1:10000
a(id) = rand();end
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Effect of Not Preallocating Memory
>> x = 4>> x(2) = 7>> x(3) = 12
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X(3) = 12X(2) = 7
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Benefit of Preallocation
>> x = zeros(3,1)>> x(1) = 4>> x(2) = 7>> x(3) = 12
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Data Storage of MATLAB Arrays
>> x = magic(3)x =
8 1 63 5 74 9 2
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See the June 2007 article in “The MathWorks News and Notes”: http://www.mathworks.com/company/newsletters/news_notes/june07/patterns.html
834159672
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Two Coding Tips for Speeding Up MATLAB
Pre-Allocation of Memory
Vectorization
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Vectorization
MATLAB is a matrix (vector)-based language supports vectorized operations (faster in general)
Process in column blocks, rather than row blocks
b = a.^a;
for id1 = 1:size(a,1)for id2 = 1:size(a,2)
b(id1, id2) = a(id1, id2)^a(id1, id2);end
end
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Agenda
Some coding techniques to speed up MATLAB code
Leveraging Parallel Computing with MATLAB
MATLAB to C:– Generating C code from MATLAB– Integrate C functions in MATLAB
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Going Beyond Serial MATLAB Applications
MATLAB Desktop (Client)
Worker
WorkerWorker
Worker
Worker
Worker
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Performance Gain with More Hardware
Using GPUs
Device Memory
GPU cores
Device Memory
Core 1
Core 3 Core 4
Core 2
Cache
Using More Cores (CPUs)
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Time
Independent Tasks or Iterations
Ideal problem for parallel computing No dependencies or communications between tasks Examples: parameter sweeps, Monte Carlo simulations
Time
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Agenda
Some coding techniques to speed up MATLAB code
Leveraging multiple cores with MATLAB
MATLAB to C:– Generating C code from MATLAB– Integrate C functions in MATLAB
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Why Engineers Translate MATLAB to C Today
Implement C code on processors or hand off to software engineers
Integrate MATLAB algorithms with existing C environment using source code and static/dynamic libraries
Prototype MATLAB algorithms on desktops as standalone executables
Accelerate user-written MATLAB algorithms
.exe
.lib
.dll
.c
MEX
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MEX
.lib
.dll
.exe
.c
With MATLAB Coder, design engineers can:
• Maintain one design in MATLAB • Design faster and get to C quickly• Test more systematically and frequently • Spend more time improving algorithms in MATLAB
With MATLAB Coder, design engineers can:
• Maintain one design in MATLAB • Design faster and get to C quickly• Test more systematically and frequently • Spend more time improving algorithms in MATLAB
Automatic Translation of MATLAB to C
itera
te
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Acceleration using MEX (MATLAB Executable)
Call C or Fortran code directly from MATLAB – Integrate existing code using MEX API– Auto-generate C-based MEX files from
MATLAB code using MATLAB Coder
Speed-up factor will vary– May see speedup for state-based for-loops– May not see a speedup when MATLAB code is
Using multithreaded computations Using optimized libraries (BLAS, FFTW, etc.)
c = myFcn(a,b)
myFcn.cvoid mexFunction(
int nlhs, mxArray *plhs[],int nrhs, const mxArray *prhs[])
{/* more C code ... */
}
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Questions?