On Tue, May 26, 2015 at 9:53 AM, Julian Taylor <[email protected]> wrote: > On 05/26/2015 04:56 PM, Matthew Brett wrote: >> Hi, >> >> This morning I was wondering whether we ought to plan to devote some >> resources to collaborating with the OpenBLAS team. >> >> >> >> It is relatively easy to add tests using Python / numpy. We like >> tests. Why don't we propose a collaboration with OpenBLAS where we >> build and test numpy with every / most / some commits of OpenBLAS, and >> try to make it easy for the OpenBLAS team to add tests. Maybe we >> can use and add to the list of machines on which OpenBLAS is tested >> [1]? We Berkeley Pythonistas can certainly add the machines at our >> buildbot farm [2]. Maybe the Julia / R developers would be interested >> to help too? >> > > Technically we only need a single machine with the newest instruction > set available. All other cases could then be tested via a virtual > machine that only exposes specific instruction sets (e.g. qemu which > could technically also emulate stuff the host does not have). > > Concerning test generation there is a huge parameter space that needs > testing due with openblas, at least some of it would need to be > automated/fuzzed. We also need specific preconditioning of memory to > test failure cases openblas had in the past, E.g. filling memory around > the matrices with nans and also somehow filling openblas own temporary > buffers with some signaling values (might require special built openblas > if _MALLOC_PERTURB does not work).
A lot of this stuff is easier if we take a white-box instead of black-box approach -- adding hooks in OpenBLAS to override the CPU-based kernel-autoselection sounds a lot easier than creating unnatural machines in qemu, and similarly for initializing temporary buffers. (I would be really unsurprised if OpenBLAS re-uses temporary buffers across calls instead of doing a free/re-malloc, for example.) > Maybe it would be feasible to write a hypothesis [0] strategy for some > of the blas stuff to automate the parameter exploration. Or if this is daunting, you can get pretty far just sitting down and writing some for loops... I think this is a case where something is a lot better than nothing :-). -n -- Nathaniel J. Smith -- http://vorpus.org _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
