[Numpy-discussion] Re: 1D ndarray to java double[]
On Sat, 31 Dec 2022 23:45:54 -0800 Bill Ross wrote: > How best to write a 1D ndarray as a block of doubles, for reading in > java as double[] or a stream of double? > > Maybe the performance of simple looping over doubles in python.write() > and java.read() is fine, but maybe there are representational diffs? > Maybe there's a better solution for the use case? Java is known to be big-endian ... but your CPU is probably little-endian. Numpy has the tools to represent an array of double BE. > Use case: I get the ndarray from keras, and it represents a 2D distance > matrix. I want to find the top-50 matches for each item, per row and > column. I'm looking at moving the top-50 task to java for its superior > parallel threading. (Java doesn't fork processes with a copy of the > array, which is ~5% of memory; rather one gets 1 process with e.g. 1475% > CPU.) What about numba or cython then ? Happy new year Jerome ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: 1D ndarray to java double[]
Thanks! > Java is known to be big-endian ... your CPU is probably little-endian. $ lscpu | grep -i endian Byte Order: Little Endian > Numpy has the tools to represent an array of double BE. Is there a lower-level ndarray method that writes an array that could be used this way? Bill -- Phobrain.com On 2023-01-01 05:13, Jerome Kieffer wrote: > On Sat, 31 Dec 2022 23:45:54 -0800 > Bill Ross wrote: > >> How best to write a 1D ndarray as a block of doubles, for reading in >> java as double[] or a stream of double? >> >> Maybe the performance of simple looping over doubles in python.write() >> and java.read() is fine, but maybe there are representational diffs? >> Maybe there's a better solution for the use case? > > Java is known to be big-endian ... but your CPU is probably little-endian. > Numpy has the tools to represent an array of double BE. > >> Use case: I get the ndarray from keras, and it represents a 2D distance >> matrix. I want to find the top-50 matches for each item, per row and >> column. I'm looking at moving the top-50 task to java for its superior >> parallel threading. (Java doesn't fork processes with a copy of the >> array, which is ~5% of memory; rather one gets 1 process with e.g. 1475% >> CPU.) > > What about numba or cython then ? > > Happy new year > > Jerome > ___ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: bross_phobr...@sonic.net___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Re: 1D ndarray to java double[]
On Sun, 01 Jan 2023 05:31:55 -0800 Bill Ross wrote: > Thanks! > > > Java is known to be big-endian ... your CPU is probably little-endian. > > $ lscpu | grep -i endian > Byte Order: Little Endian > > > Numpy has the tools to represent an array of double BE. > > Is there a lower-level ndarray method that writes an array that could be > used this way? One example: numpy.array([1,2,3], dtype=">d").tobytes() b'?\xf0\x00\x00\x00\x00\x00\x00@\x00\x00\x00\x00\x00\x00\x00@\x08\x00\x00\x00\x00\x00\x00' numpy.array([1,2,3], dtype="https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
[Numpy-discussion] Documentation team meeting - Monday 2 January at 12 PM (noon) UTC
Hi all! Our next Documentation Team meeting will happen on *Monday, January 2* at ***12PM UTC***. We now alternate the meeting times to be a bit more inclusive. This means that we'll have a meeting at 12pm UTC every 28 days, and a meeting at 4pm UTC every 28 days. All are welcome - you don't need to already be a contributor to join. If you have questions or are curious about what we're doing, we'll be happy to meet you! If you wish to join on Zoom, use this (updated) link: https://numfocus-org.zoom.us/j/85016474448?pwd=TWEvaWJ1SklyVEpwNXUrcHV1YmFJQT09 Here's the permanent hackmd document with the meeting notes (still being updated): https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg https://www.google.com/url?q=https%3A%2F%2Fhackmd.io%2FoB_boakvRqKR-_2jRV-Qj... Hope to see you around! * You can also visit https://scientific-python.org/calendars to add the NumPy community calendar as an .ics file to your preferred calendar manager. * Wishing everyone a very happy new year! - Mukulika ___ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
