{"action":"opened","issue":{"url":"https://api.github.com/repos/numba/numba/issues/1288","labels_url":"https://api.github.com/repos/numba/numba/issues/1288/labels{/name}","comments_url":"https://api.github.com/repos/numba/numba/issues/1288/comments","events_url":"https://api.github.com/repos/numba/numba/issues/1288/events","html_url":"https://github.com/numba/numba/issues/1288","id":92414160,"number":1288,"title":"Segfaults with recent libc","user":{"login":"ml31415","id":2386612,"avatar_url":"https://avatars.githubusercontent.com/u/2386612?v=3","gravatar_id":"","url":"https://api.github.com/users/ml31415","html_url":"https://github.com/ml31415","followers_url":"https://api.github.com/users/ml31415/followers","following_url":"https://api.github.com/users/ml31415/following{/other_user}","gists_url":"https://api.github.com/users/ml31415/gists{/gist_id}","starred_url":"https://api.github.com/users/ml31415/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ml31415/subscriptions","organizations_url":"https://api.github.com/users/ml31415/orgs","repos_url":"https://api.github.com/users/ml31415/repos","events_url":"https://api.github.com/users/ml31415/events{/privacy}","received_events_url":"https://api.github.com/users/ml31415/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-07-01T15:32:08Z","updated_at":"2015-07-01T15:32:08Z","closed_at":null,"body":"I recently updated my Debian machine from the old stable to the new one, and since then it looks like anything I try to do with numba immediately segfaults. I have an Ubuntu box with a similar setup, everything works great there. I get the segfaults with any numba version I tried from 1.8 to 1.9.2. Different python versions from 2.7.6 to 2.7.10 also seem to make no difference. The only relevant difference that I could find so far are different libc versions, `2.19-18` on Debian, not working and `2.19-10ubuntu2`, working. Are there any known incompatibilities with different versions?\r\n\r\nThis is how to reproduce the error on the Debian machine, but basically any `numba.test()` will do. Just the backtrace is more readable:\r\n```python\r\nimport numba as nb\r\n@nb.jit\r\ndef testadd(x):\r\n    return x + 4\r\nprint testadd(4)\r\n```\r\n\r\n`gdb --args python test.py` now results in the following segfault:\r\n```\r\nProgram received signal SIGSEGV, Segmentation fault.\r\n0x00007ffff7ff2010 in wrapper..main..testadd$1.int64 ()\r\n(gdb) bt\r\n#0  0x00007ffff7ff2010 in wrapper..main..testadd$1.int64 ()\r\n#1  0x00007fffe7d67933 in call_cfunc (kws=0x0, args=(4,), cfunc=<built-in method testadd of _dynfunc._Closure object at remote 0x7ffff7fb3750>) at numba/_dispatcher.c:499\r\n#2  compile_and_invoke (self=self@entry=0x7ffff7e92850, args=args@entry=(4,), kws=kws@entry=0x0) at numba/_dispatcher.c:522\r\n#3  0x00007fffe7d68350 in Dispatcher_call (self=0x7ffff7e92850, args=(4,), kws=<optimized out>) at numba/_dispatcher.c:663\r\n#4  0x00000000004caaa1 in PyObject_Call (kw=<optimized out>, arg=<optimized out>, func=<optimized out>) at ../Objects/abstract.c:2529\r\n#5  do_call (nk=<optimized out>, na=<optimized out>, pp_stack=<optimized out>, func=<optimized out>) at ../Python/ceval.c:4251\r\n#6  call_function (oparg=<optimized out>, pp_stack=<optimized out>) at ../Python/ceval.c:4056\r\n#7  PyEval_EvalFrameEx () at ../Python/ceval.c:2679\r\n#8  0x00000000004c87a1 in PyEval_EvalCodeEx () at ../Python/ceval.c:3265\r\n#9  0x00000000005030ef in PyEval_EvalCode (\r\n    locals={'__builtins__': <module at remote 0x7ffff7fa5b08>, '__file__': 'nbtest.py', '__doc__': None, 'testadd': <CPUOverloaded(__module__='__main__', _compile_lock=<NonReentrantLock(_owner=None, _lock=<thread.lock at remote 0x7ffff7f7ac50>) at remote 0x7fffe7a3a490>, overloads={(<Integer(is_parametric=False, _code=13, name='int64', signed=True, bitwidth=64) at remote 0x7fffe8e98750>,): <built-in method testadd of _dynfunc._Closure object at remote 0x7ffff7fb3750>}, _npsigs=[<Signature at remote 0x7fffe7784b00>], _pysig=<Signature at remote 0x7fffe7c87c10>, targetctx=<CPUContext(address_size=64, data_model_manager=<DataModelManager(_handlers={<_TypeMetaclass(__module__='numba.types', cast_python_value=<function at remote 0x7fffe8e7f578>, __le__=<function at remote 0x7fffe8e7f668>, __ge__=<function at remote 0x7fffe8e7f6e0>, __gt__=<function at remote 0x7fffe8e7f488>, __lt__=<function at remote 0x7fffe8e7f5f0>, __doc__=None, __init__=<function at remote 0x7fffe8e7f500>) at remote 0xad8830>: <type at remote 0xf3ed00>, ...(truncated), \r\n    globals={'__builtins__': <module at remote 0x7ffff7fa5b08>, '__file__': 'nbtest.py', '__doc__': None, 'testadd': <CPUOverloaded(__module__='__main__', _compile_lock=<NonReentrantLock(_owner=None, _lock=<thread.lock at remote 0x7ffff7f7ac50>) at remote 0x7fffe7a3a490>, overloads={(<Integer(is_parametric=False, _code=13, name='int64', signed=True, bitwidth=64) at remote 0x7fffe8e98750>,): <built-in method testadd of _dynfunc._Closure object at remote 0x7ffff7fb3750>}, _npsigs=[<Signature at remote 0x7fffe7784b00>], _pysig=<Signature at remote 0x7fffe7c87c10>, targetctx=<CPUContext(address_size=64, data_model_manager=<DataModelManager(_handlers={<_TypeMetaclass(__module__='numba.types', cast_python_value=<function at remote 0x7fffe8e7f578>, __le__=<function at remote 0x7fffe8e7f668>, __ge__=<function at remote 0x7fffe8e7f6e0>, __gt__=<function at remote 0x7fffe8e7f488>, __lt__=<function at remote 0x7fffe8e7f5f0>, __doc__=None, __init__=<function at remote 0x7fffe8e7f500>) at remote 0xad8830>: <type at remote 0xf3ed00>, ...(truncated), \r\n    co=0x7ffff7e859b0) at ../Python/ceval.c:667\r\n#10 run_mod.lto_priv () at ../Python/pythonrun.c:1371\r\n#11 0x00000000004f8c72 in PyRun_FileExFlags () at ../Python/pythonrun.c:1357\r\n#12 0x00000000004f7d77 in PyRun_SimpleFileExFlags () at ../Python/pythonrun.c:949\r\n#13 0x00000000004982f2 in Py_Main () at ../Modules/main.c:640\r\n#14 0x00007ffff6f14b45 in __libc_start_main (main=0x497d80 <main>, argc=2, argv=0x7fffffffe028, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffffffe018)\r\n    at libc-start.c:287\r\n#15 0x0000000000497ca0 in _start ()\r\n```\r\n\r\nOr is there anything, that I completely overlooked, besides the different libc and python versions?\r\n"}}