September 2017 - cctbxbb - phenix-online.org

Compiling CCTBX with newer GCC on Mac
by Lance Westerhoff 04 Oct '17

04 Oct '17

Hello All- How does one introduce an alternate gcc compiler for CCTBX on Mac? I must be missing it in the documentation. I don’t want to use the old one in /usr/bin (and instead I want to you my newer one in /opt/gcc). I’ve tried all of the standard methods used by other build systems (change the $PATH, set $CXX and $CC, etc) but to no avail. These environment variables seem to be disregarded. We do use this compiler for building CCTBX on linux without too much trouble, so I think the support should be there. Specifically: mac-build64:build jenkins$ env | grep CXX CXX=/opt/gcc/current/bin/g++ mac-build64:build jenkins$ env | grep CC CC=/opt/gcc/current/bin/gcc mac-build64:build jenkins$ which gcc /opt/gcc/current/bin/gcc mac-build64:build jenkins$ g++ -v Using built-in specs. COLLECT_GCC=g++ COLLECT_LTO_WRAPPER=/opt/gcc/gcc-6.3.0-08092017/libexec/gcc/x86_64-apple-darwin11.4.2/6.3.0/lto-wrapper Target: x86_64-apple-darwin11.4.2 Configured with: ../gcc-6.3.0/configure --prefix=/opt/gcc/gcc-6.3.0-08092017 --enable-languages=c,c++,fortran,objc --with-gmp=/opt/gcc/gcc-6.3.0-08092017 --with-mpfr=/opt/gcc/gcc-6.3.0-08092017 --with-mpc=/opt/gcc/gcc-6.3.0-08092017 --enable-libgomp --disable-multilib Thread model: posix gcc version 6.3.0 (GCC) mac-build64:build jenkins$ /opt/python/current/bin/python ../modules/cctbx_project/libtbx/configure.py cctbx Python: 2.7.9 "/opt/python/current/bin/python" Compiler: default .... This script is only applicable to Linux - exiting. Done. mac-build64:build jenkins$ make ./bin/libtbx.scons -j "`./bin/libtbx.show_number_of_processors`" scons: Reading SConscript files ... On MacOS, using clang 4.2.0 Xcode 4.6.3 Build version 4H1503 ... Thanks for your help! -Lance ____________________ Lance M. Westerhoff, Ph.D. President and General Manager QuantumBio Inc. WWW: http://www.quantumbioinc.com Email: lance(a)quantumbioinc.com

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Should we enable boost threads in bootstrap?
by Graeme.Winter＠diamond.ac.uk 28 Sep '17

28 Sep '17

Afternoon all, Should we do this? Any opinions? Could be useful for threads in a semi-portable way... Thanks & cheerio Graeme -- This e-mail and any attachments may contain confidential, copyright and or privileged material, and are for the use of the intended addressee only. If you are not the intended addressee or an authorised recipient of the addressee please notify us of receipt by returning the e-mail and do not use, copy, retain, distribute or disclose the information in or attached to the e-mail. Any opinions expressed within this e-mail are those of the individual and not necessarily of Diamond Light Source Ltd. Diamond Light Source Ltd. cannot guarantee that this e-mail or any attachments are free from viruses and we cannot accept liability for any damage which you may sustain as a result of software viruses which may be transmitted in or with the message. Diamond Light Source Limited (company no. 4375679). Registered in England and Wales with its registered office at Diamond House, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, United Kingdom

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Should we enable boost threads in bootstrap?
by Tristan Croll 27 Sep '17

27 Sep '17

Sorry for not replying directly to the existing thread, but I've only just subscribed to cctbxbb. Since I started the discussion of the Global Interpreter Lock (GIL) yesterday, I thought I should give a quick run-down of why it's important and where/how it can be released. In brief, the much-maligned GIL exists because much of the Python API proper (in particular, reference counting) is not thread-safe. Its purpose is to ensure that only one thread can ever be using Python objects at any given time. In practice this means that a naive implementation of Python threads gives you the worst of all possible worlds: parallel logic, but slower-than-single-threaded performance. Python regularly tries to swap between all threads it has running, but only the one that currently holds the GIL can go forward. So if one thread runs a method that takes 10 seconds without releasing the GIL, *all* threads will hang for 10 seconds. To be clear, this *doesn't* prevent you from using C++ threads, as long as those threads are not acting on Python objects. The thing is, in most production code the heavy-duty computation is *not* done on Python objects - it's done in C++ on objects that Python simply holds a pointer to. All such functions can safely release the GIL as long as they reacquire it before returning to Python. In the context of the example above, that means all other threads are able to continue on doing their own thing while that 10-second function is running. So why use threads rather than multiprocessing? Three key reasons: - threads are trivial to set up and run in the same way on Linux, macOS and Windows - threads share memory by default, making life much easier when they need to communicate regularly - It turns out that OpenCL/CUDA do not play at all well with forked processes. A real-world example: in ISOLDE I'm currently bringing together two key packages with their own Python APIs: ChimeraX for the molecular graphics, OpenMM for MD simulation. Since this is an interactive application, speed is of the essence. Graphics performance needs to be independent of simulation performance (to allow the user to rotate/translate etc. smoothly no matter how fast the simulation runs) so parallelism is mandatory. There is constant back-and-forth communication needed (the simulation needs to update coordinates; interactions need to be sent back to the simulation), which easier/faster in shared memory. The simulation is running on a GPU. My initial implementation used Python's multiprocessing module applying os.fork() - this worked in Linux under the very specific circumstances where the GPU had not previously been used for OpenCL or CUDA by the master process, but failed on the Mac and is of course impossible on Linux. Switching from multiprocessing to threading (with no other changes to my code) gave me an implementation that works equally well on Linux and Mac with no OS-specific code, and should work just as well on Windows once I get around to doing a build. The performance is effectively equal to what I was getting from multiprocessing, since all the major libraries I'm using (ChimeraX, OpenMM, NumPy) release the GIL in all their C++ calls. At the moment, though, CCTBX functions don't - but there's no reason why they can't. See https://wiki.python.org/moin/boost.python/HowTo#Multithreading_Support_for_…. A simple toy example using Python threads to speed up a heavyish computation: from multiprocessing.pool import ThreadPool import numpy from math import ceil from time import sleep def f(a, target, start_i, end_i): arr = a[start_i:end_i] target[start_i:end_i] = numpy.exp(numpy.cos(arr)+numpy.sin(arr)) def test_threads(a, num_threads): l = len(a) ret = numpy.empty(l, a.dtype) stride = int(ceil(l/num_threads)) with ThreadPool(processes=num_threads) as p: for i in range(num_threads): start = stride*i end = stride*(i+1) if end > l: end = l p.apply_async(f, (a, ret, start, end)) p.close() p.join() return ret a = numpy.random.rand(50000000) target = numpy.empty(len(a), a.dtype) %timeit f(a, target, 0, len(a)) 2.35 s ± 102 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit test_threads(a, 1): 2.55 s ± 30.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit test_threads(a, 2): 1.5 s ± 5.69 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) %timeit test_threads(a, 3): 1.2 s ± 111 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

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rstbx test is failing without indication in test_all_parallel
by Oleg Sobolev 22 Sep '17

22 Sep '17

Dear colleagues, Accidentally I found out that this command: rstbx.simage.solver d_min=5 lattice_symmetry=P422 intensity_symmetry=P4 index_and_integrate=True multiprocessing=True finishes with traceback. It is run as part of phenix_regression/misc/tst_rstbx.csh The second problem is that test_all_parallel script fails to detect an actual failure. It would be great if somebody who is in charge of rstbx and testing could look at this. Best regards, Oleg Sobolev.

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Massive commit just now
by Graeme.Winter＠diamond.ac.uk 08 Sep '17

08 Sep '17

Folks, Earlier a branch which was fairly long lived was merged in perhaps a non-ideal way, apparently replaying every commit from around July to now. While this is confusing and has (a little) messed with history, it has not actually broken anything... However, if you have a big branch you want to merge, I think a pull request makes this a little more obvious Just some background to what happened... Cheers Graeme -- This e-mail and any attachments may contain confidential, copyright and or privileged material, and are for the use of the intended addressee only. If you are not the intended addressee or an authorised recipient of the addressee please notify us of receipt by returning the e-mail and do not use, copy, retain, distribute or disclose the information in or attached to the e-mail. Any opinions expressed within this e-mail are those of the individual and not necessarily of Diamond Light Source Ltd. Diamond Light Source Ltd. cannot guarantee that this e-mail or any attachments are free from viruses and we cannot accept liability for any damage which you may sustain as a result of software viruses which may be transmitted in or with the message. Diamond Light Source Limited (company no. 4375679). Registered in England and Wales with its registered office at Diamond House, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, United Kingdom

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