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"certain functionality" is a bit of an understatement, here. Those are precisely the core algorithms, that allow you to do things with matrices, solve systems,
by enriquto 8y ago
"certain functionality" is a bit of an understatement, here. Those are precisely the core algorithms, that allow you to do things with matrices, solve systems, etc. As you put it, numpy is simply a wrapper for this.
- plus 8y agoI think that's an unfair characterization of numpy's functionality. Numpy introduces data structures not present in the core python library (another one of your complaints about python, though it's not clear to me why it matters that numpy is a separate library), and certain manipulations do not require BLAS/LAPACK acceleration, eg. multiplying an array by a scalar (yes, I know dscal exists in BLAS) or array slicing. Further, it is possible to use numpy without any BLAS/LAPACK libraries at all, and it continues to work (albeit more slowly).
- amelius 8y agoNote that most accelerated implementations of BLAS are not written in Fortran, but use e.g. SIMD instructions directly. See also: https://stackoverflow.com/questions/1303182/how-does-blas-get-such-extreme-performance https://stackoverflow.com/questions/1303182/how-does-blas-ge...