> It's not fully pass-by-value, a copy is only made if the matrix/array > is changed, but not if only accessed for getting the data. And from > the comments I have seen they keep improving lazy copying.
Yes it is copy-on-write. But that means we must restrict ourselves to read-only access. It also means that we cannot memory map a file. If there is a write operation, we get a copy of the whole file into RAM. Matlab also takes a copy if we create an array slice. It means for example that a wavelet transform written in Python will be O(n) with respect to memory, whereas it will be O(n log n) in Matlab. I am working on data that are recorded at 48 kHz, 16-32 channels, for several minutes. Memory issues like these do matter. >> 4. Matlab is single-treaded. Python allows threads, although there is a >> GIL (which can be freed when running C or Fortran library code). Matlab >> use MKL for multi-cores, NumPy/SciPy can be compiled against MKL or >> ACML. >> Threads are not just for parallel processing, but also for I/O and GUI >> interaction. > > since 2007a: > "MATLAB has multithreaded computation support for many linear algebra > and element-wise numeric operations, allowing performance improvement > on multicore and multiprocessor systems." That is due to MKL and FFTW. I have NumPy and SciPy linked with MKL. It uses all four cores on my laptop for linear algebra as well. Matlab does not have a multi-threaded interpreter. Please don't confuse this. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
