arigato changed the topic of #pypy to: PyPy, the flexible snake (IRC logs: https://quodlibet.duckdns.org/irc/pypy/latest.log.html#irc-end ) | use cffi for calling C | mac OS and Fedora are not Windows
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<mattip> anyone have an idea what is up with bnecher4 writing to /tmp in tests?
<mattip> the error is EACCES: [Permission denied]: symlink('8149', '/tmp/ffi-203/.lock')
<mattip> cleaning out /tmp didn't help
<mattip> ahh, I cleaned out the /tmp/usession* and /tmp/tmp* but not /tmp/ffi*
<mattip> now removing those too, let's see tomorrow if it worked
<kenaan> mattip default c3622918429e /rpython/rlib/rvmprof/: fix vmprof for 32 bit linux
<mattip> while this fixes compiling on linux32, it needs to sync to upstream vmprof. PR issued.
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<kenaan> arigo default 6d85d83cc17b /lib-python/2.7/ctypes/test/test_loading.py: Remove @xfail on these two Windows tests. One of them passes right now (and so it ends up as a failure). I don't ...
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<kenaan> fijal arm64 98896de049b1 /rpython/jit/backend/aarch64/: start working on cond_call, we need float support for that
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<kenaan> fijal arm64 fee018704c07 /rpython/jit/backend/aarch64/: enough float support to start running tests
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<kenaan> fijal arm64 254778fcde03 /rpython/jit/backend/aarch64/: enough for basic float operations
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<kenaan> fijal arm64 aba1d6e3b06a /rpython/jit/backend/aarch64/: more float ops
<kenaan> fijal arm64 9453a6ab3fe2 /rpython/jit/backend/aarch64/: casts
<kenaan> fijal arm64 8e29d4e40226 /rpython/jit/backend/aarch64/: more float ops
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<kenaan> fijal arm64 1f31b1f9873c /rpython/jit/backend/aarch64/: more floats
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<kenaan> fijal arm64 0abc0d2b282f /rpython/jit/backend/aarch64/: minor progress
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<niso> Hi, ive try to use Pympler to measure the "real" size of an object and got exception. how that can be done on pypy?
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<kenaan> rlamy py3.6 92c90687b29c /pypy/objspace/std/intobject.py: Optimise str(<int>): don't rescan a string that is known to always be ASCII
<fijal> niso: the same question on pypy makes even less sense than on cpython
<niso> why is that? im trying to figure out how to investigate a high memory usage issue
<fijal> so one thing to try is to use gc module
<fijal> there is gc.get_stats(), what does it tell you?
<niso> ill check that out
<niso> As i see i cant get out from this call the types of the "most used objects"? that should be used to trace the problematic code?
<fijal> this is the highest level overview
<fijal> do you mind sharing it?
<fijal> there is also dump_rpy_heap, which writes down heap that you can later analyze
<fijal> there is no equivalent to show heap stats unfortunately :/
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