![]() % x4 loop size, x4 execution timeĬlear f, tic, f= for idx=3:10000, f(idx)=f(idx-1)+f(idx-2) end, toc % x2 loop size, x2 execution time clear f, tic, f= for idx= 3: 40000, f (idx )=f (idx- 1 )+f (idx- 2 ) end, toc % baseline loop size, & exec time clear f, tic, f= for idx= 3: 20000, f (idx )=f (idx- 1 )+f (idx- 2 ) end, toc This means that multiplying the array size by N only degrades performance by N, not N 2 – an impressive achievement: The execution run-times improved significantly, and in addition have become linear in nature. In Matlab 7.12 (R2011a), a major improvement was done in the Matlab engine (JIT?). The behavior was still quadratic in nature, and so in these releases, using any of the above-mentioned solutions could prove very beneficial. In Matlab 7.11 (R2010b), the short Fibonacci snippet used in last week’s article showed executed about 30% faster compared to Matlab 7.1 R14SP3. ![]() JIT was consistently improved since that release, giving a small improvement with each new Matlab release. ![]() Over the years, MathWorks has consistently improved the efficiency of its computational engine and the JIT Accelerator in particular. ![]() The introduction of JIT Acceleration in Matlab 6.5 (R13) caused a dramatic boost in performance (there is an internal distinction between the Accelerator and JIT: JIT is apparently only part of the Matlab Accelerator, but this distinction appears to have no practical impact on the discussion here). I believe that readers who will read this utility’s source code and understand its underlying logic will gain insight into several performance tricks that could be very useful in general. The source code may seem complex, but the resulting performance gain is well worth the extra complexity. It is based in part on ideas mentioned in the aforementioned 2005 newsgroup thread, where growdata is also discussed in detail.Īs an interesting side-note, John D’Errico also recently posted an extremely fast implementation of the Fibonacci function. John D’Errico has posted a well-researched utility called growdata that optimizes dynamic array growth for maximal performance. Different situations may favor using either the cell arrays method or the factor growth mechanism. Like factor growth, using cell arrays is faster than quadratic behavior (although not quite as fast enough as we would have liked, of course). Note that this relies on the internal implementation of cell arrays in Matlab, and may possibly change in some future release. Cell elements are implemented as references to distinct memory blocks, so concatenating an object to a cell array merely concatenates its reference when a cell array is reallocated, only its internal references (not the referenced data) are moved. The idea here is to use cell arrays to store and grow the data, then use cell2mat to convert the resulting cell array to a regular numeric array. You might say that this is on-the-fly preallocation. It is similar to preallocation in the sense that we are preparing a chunk of memory for future array use in advance.
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