Mailman 3 April 2018 - pypy-dev

About gurobipy
by Luis José Novoa 31 May '18

31 May '18

Hi All, My name is Luis Novoa. Im working on a large scale optimization problem using python/gurobipy for my implementation. I was wondering if any of you in this list has used gurobipy with pypy, or if it is even possible at this time. Thanks in advance, Luis.

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Re: [pypy-dev] Mysterious IndexError in service running with PyPy
by William ML Leslie 21 May '18

21 May '18

Where is the code that changes the size of self.heap? How do we know that size(self.heap) is constant? My guess is that some thread changes this; but l is not recomputed. On 18 Dec 2017 6:59 PM, "hubo" <hubo(a)jiedaibao.com> wrote: > I'm reporting this issue in this mail group, though I don't know if it is > related with PyPy, because it is really strange, and is not able to > reproduce stably. But I hope someone may know the reason or have some > points. > > I'm running some SDN services written in Python with PyPy 5.3.0. The > related code is here: > > https://github.com/hubo1016/vlcp/blob/8022e3a3c67cf4305af503d507640a730ca39… > > The full code is also in the repo, but may be too complex to describe. But > this related piece is quite simple: > > def _siftdown(self, pos): > temp = self.heap[pos] > l = len(self.heap) > while pos * 2 + 1 < l: > cindex = pos * 2 + 1 > pt = self.heap[cindex] > if cindex + 1 < l and self.heap[cindex+1][0] < pt[0]: > cindex = cindex + 1 > pt = self.heap[cindex] > if pt[0] < temp[0]: > self.heap[pos] = pt > self.index[pt[1]] = pos > else: > break > pos = cindex > self.heap[pos] = temp > self.index[temp[1]] = pos > It is a simple heap operation. The service uses a heap to process timers. > When the service is not busy, it usually runs this piece of code several > times per minute. > > I have 32 servers running this service. They are quite stable in about > three months, but one day one of the services crashes on line 100 reporting > IndexError: > pt = self.heap[cindex] > > As you can see, cindex = pos * 2 + 1, which is tested by the while > pre-conditon just two lines before. And there is not any multi-threading > issues here because this piece of code always runs in the same thread. So > it is not possible in theory for this to happen. > > Only the following facts are known about this issue: > > 1. It reproduces - through quite rarely. I've met this kind of > crashes 4 times, each with different machine, so it should not be related > to hardware issuess. Since I've got 32 servers, it might take more than one > year to reproduce with a single server. > 2. It seems not to be related to pressures. All of the crashes happens > at night, when there are little requests. Only some cleanup tasks are > running in fixed interval. > > The services are running with PyPy 5.3.0. I've upgraded a few of them to > 5.9, but it will take a long time to validate whether this still happens. > And It is not validated on CPython too. I'm also trying to collect more > debugging information for this issue, but it is very hard since it rarely > reproduces. > > It is not a serious issue. It could be workarounded with a auto-restart, > but I'm searching the cause. > > > 2017-12-18 > ------------------------------ > hubo > > _______________________________________________ > pypy-dev mailing list > pypy-dev(a)python.org > https://mail.python.org/mailman/listinfo/pypy-dev > >

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PhD Positions in programming language runtimes (Python)
by David Bremner 02 May '18

02 May '18

1 Position I: Python on OMR =========================== The candidate will explore the benefits and challenges of using OMR [1] components (e.g. JIT, GC) in Python [2,3] runtimes. This may involve extending OMR in novel ways to better support Python, or vice-versa. If questions like how to balance fast startup time for scripting applications against good performance for compute-intensive workloads sound interesting, this might an opportunity for you. 2 Position II: Python and PGAS ============================== The candidate will implement support for parallel programming with Python, using the PGAS [4] (parallel global address space) model. The PGAS model can significantly simplify the implementation of parallel algorithms on distributed and shared memory computers. Challenges include enabling parallelism while dealing with Python's Global Interpreter Lock (GIL), and implementing distributed garbage collection efficiently. 3 Qualifications ================ Normally applicants to the Computer Science PhD program at UNB require a Master's degree in computer science or a closely related area. This project will require substantial hands-on and experimental work, and being comfortable with Python and C++. Familiarity with Linux and Open Source Software development would be a plus. For position I, exposure to programming language concepts like GC, JIT, and bytecode would be helpful. For position II, experience with parallel programming frameworks and/or languages such as MPI, OpenMP, X10 is an asset. 4 Environment ============= This project is supported by the IBM Centre for Advanced Studies (Atlantic) at the University of New Brunswick [5]. We have a lively group of students, postdocs, and research assistants working on related problems. Students work closely with both UNB researchers and technical experts from IBM, gaining valuable perspective and experience. 5 Support ========= These positions are "fully funded"; the exact funding package is subject to finalization but will be consistent with other fully funded students in Computer Science at UNB. You can find some information about expenses on the UNB web site [6]. 6 Contact ========= Send your resumé and unofficial transcripts by email to Stephen MacKay <smackay3(a)unb.ca> Please specify which position(s) you are interested in. [1]: [http://www.eclipse.org/omr/] [2]: [http://www.python.org] [3]: [http://www.pypy.org] [4]: [http://www.pgas.org/] [5]: [http://www.unb.ca/research/casatlantic/] [6]: [http://www.unb.ca/fredericton/studentservices/international/new_internation…]

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pypy + pip --user
by Neal Becker 30 Apr '18

30 Apr '18

Using the pypy fedora package, it seems pip install --user is confused about ~/.local/lib/python3.5/site-packages. It should be using it's own directory, ~/.local/lib/pypy3.5/site-packages, or something like that. pypy3 $(which pip) install --user matplotlib ... Traceback (most recent call last): File "/home/nbecker/.local/lib/python3.5/site-packages/numpy/core/__init__.py", line 16, in <module> from . import multiarray ImportError: cannot import name 'multiarray' Don't know if the pypy default for --user is wrong, or if it's a fedora packaging problem. I can use pypy with virtualenv, but I think this issue shouldn't happen.

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using c-api
by Neal Becker 29 Apr '18

29 Apr '18

Just installed pypy3-6.0.0-1.fc29.x86_64 on fedora 28 (to be released in a couple of days), also install pypy3-devel and pypy3-libs. Trying to build my c modules (c++ using pybind11). I don't see any python library to link against. c-python I would link with /usr/lib64/libpython3.6m.so, but pypy3 doesn't seem to include anything like that (at least not the fedora-packaged version) -- Those who don't understand recursion are doomed to repeat it

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Could you remove my Bitbucket access?
by mike c 27 Apr '18

27 Apr '18

Hi there, I contributed to pypy way way back, but for the moment I have other priorities. Bitbucket has made it impossible for me to revoke my own access from the pypy repositories. Could someone in the development team revoke my access? Thanks, and good luck! Mike

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PyPy 6.0 release
by Matti Picus 25 Apr '18

25 Apr '18

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[cfp] COP 2018
by Jens Lincke 20 Apr '18

20 Apr '18

*** COP 2018: 10th International Workshop on Context-Oriented Programming *** July 16th, 2018 Co-located with ECOOP/ISSTA 2018 in Amsterdam, Netherlands === Advanced Modularity for Run-time Composition === Contextual information is playing an ever-increasing role in our information-centric world. Current-day software systems need to adapt continuously to changing execution and usage contexts, even while they are running. Unfortunately, mainstream programming languages and development environments still do not support this level of dynamic adaptivity very well, leading developers to implement complex designs to anticipate various dimensions of variability. Context-Oriented Programming directly supports variability at the programming level, featuring dedicated programming abstractions enabling programmers to write software applications that adapt dynamically to a wide range of dynamic attributes. It enables run-time behaviour to be dispatched directly on any detected properties of the execution or user context. Since more than a decade, several researchers have been working on a variety of notions approaching that idea. Implementations ranging from first prototypes to mature platform extensions used in commercial deployments have illustrated how multidimensional dispatch can be supported effectively to achieve expressive run-time variation in behavior. Our series of International Workshops on Context-Oriented Programming (COP) at ECOOP since 2009 have shown to be well-received, each attracting around 30 participants. Also in 2018 we hope to advance this vibrant research domain at ECOOP in the beautiful and lively city of Amsterdam. === Topics === Topics of interest to the workshop include, but are not limited to: - Interesting application domains and scenarios for COP - Programming language abstractions for COP (e.g. dynamic scoping, roles, traits, prototype-based extensions) - Implementation techniques and mechanisms for COP (e.g. different kinds of dynamic, contextual, and multi-dimensional dispatch or pre-dispatch) - Implementation issues for COP such as optimization, VM support, JIT compilation etc. - Implemented use-cases, case studies, or prototypes of COP - Theoretical foundations for COP (e.g., semantics, type systems) - Configuration languages (e.g. feature description interpreters, transformational approaches) - Interaction between non-functional programming concerns and COP (e.g. security, persistence, concurrency, distribution) - Modularization approaches for COP (e.g. modules, aspects, features, layers, plugins) - Guidelines to apply COP (e.g. best practices, idioms, patterns) - Run-time support for COP (e.g. reflection, dynamic binding) - Tool support (e.g. design tools, IDEs, debuggers) - Support for COP at the modeling level - Beyond context-oriented behavior adaptation (e.g., UI adaptation, DB adaptation). === Submission guidelines === COP invites submissions of high-quality papers reporting original research, or describing innovative contributions to, or experience with context-oriented programming, its implementation, and application. Papers that depart significantly from established ideas and practices are particularly welcome. Submissions must not have been published previously and must not be under review for any other refereed event or publication. The program committee will evaluate each contributed paper based on its relevance, significance, clarity, and originality. Papers are to be submitted via EasyChair (https://easychair.org/conferences/?conf=cop2018). They must be written in English, provided as PDF documents, and follow the new ACM Master Article Template with the sigconf option (https://www.acm.org/publications/proceedings-template). They should not exceed 8 pages. Accepted papers will be published in the ACM Digital Library. === Important dates === Submission deadline: May 18th, 2018 (AoE) Notifications: June 8th, 2018 Final version: June 29th, 2018 === Program committee === Tomoyuki Aotani, Tokyo Institute of Technology, Japan Nicolás Cardozo, Universidad de los Andes, Colombia Coen De Roover, Vrije Universiteit Brussel, Belgium Elisa Gonzalez Boix, Vrije Universiteit Brussel, Belgium David H. Lorenz, Technion IIT, Israel Gorel Hedin, Lund University, Sweden Stefan Marr, University of Kent, UK Kim Mens, Université Catholique de Louvain, Belgium Stefan Ramson, Hasso Plattner Institute, Germany Guido Salvaneschi, TU Darmstadt, Germany Matthias Springer, Tokyo Institute of Technology, Japan Mario Südholt, IMT Atlantique, France Naoyasu Ubayashi, Kyushu University, Japan Didier Verna, EPITA / LRDE, France Harumi Watanabe, Tokai University, Japan Tijs van der Storm, CWI, Netherlands === Organizing committee === Robert Hirschfeld, HPI, Germany Atsushi Igarashi, Kyoto University, Japan Tetsuo Kamina, Ritsumeikan University, Japan Jens Lincke, Hasso Plattner Institute, Germany Hidehiko Masuhara, Tokyo Institute of Technology, Japan

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Returning a "default value" for a Rtyped function
by Timothy Baldridge 20 Apr '18

20 Apr '18

I have a function in RPython that returns a tuple, and based on different situations, one value or the other may "null": def decide(x): if x: return 42, None else: return 0, Object("some value") Here's the issue, this function is part of some meta-programming and is auto generated. So the issue is, when I return `0, Object("some value")` I really don't know what the first member of the tuple is, I want it to be some value, and it can be anything since the calling function is dispatching on the second value of the tuple being None (or not). Is there some sort of way in rpython to say "return any value that fits the type expected here?" Or perhaps there's some better way to do a tagged union of sorts in rpython. Thanks, Timothy

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[CfP] ICOOOLPS 2018
by Tim Felgentreff 19 Apr '18

19 Apr '18

Call for Papers: ICOOOLPS'18 ============================ 13th Workshop on Implementation, Compilation, Optimization of Object- Oriented Languages, Programs and Systems Co-located with ECOOP 2018 held Mon 16 - Sun 22 July in Amsterdam, Netherlands Twitter: @ICOOOLPS URL: https://conf.researchr.org/track/ecoop-issta-2018/ICOOOLPS-2018-papers The ICOOOLPS workshop series brings together researchers and practitioners working in the field of language implementation and optimization. The goal of the workshop is to discuss emerging problems and research directions as well as new solutions to classic performance challenges. The topics of interest for the workshop include techniques for the implementation and optimization of a wide range of languages including but not limited to object-oriented ones. Furthermore, meta-compilation techniques or language-agnostic approaches are welcome, too. ### Topics of Interest A non-exclusive list of topics of interest for this workshop is: - Implementation and optimization of fundamental languages features (from automatic memory management to zero-overhead metaprogramming) - Runtime systems technology (libraries, virtual machines) - Static, adaptive, and speculative optimizations and compiler techniques - Meta-compilation techniques and language-agnostic approaches for the efficient implementation of languages - Compilers (intermediate representations, offline and online optimizations,…) - Empirical studies on language usage, benchmark design, and benchmarking methodology - Resource-sensitive systems (real-time, low power, mobile, cloud) - Studies on design choices and tradeoffs (dynamic vs. static compilation, heuristics vs. programmer input,…) - Tooling support, debuggability and observability of languages as well as their implementations ### Workshop Format and Submissions This workshop welcomes the presentation and discussion of new ideas and emerging problems that give a chance for interaction and exchange. More mature work is welcome as part of a mini-conference format, too. We aim to interleave interactive brainstorming and demonstration sessions between the formal presentations to foster an active exchange of ideas. The workshop papers will be published in ACM DL or an open archive (to be confirmed). Papers are to be submitted using the sigplanconf LaTeX template (http://www.sigplan.org/Resources/LaTeXClassFile/). Please submit contributions via EasyChair: https://easychair.org/conferences/submission_show_all.cgi?a=17114062 ### Important Dates Submissions: 18 May 2018 Author Notification: 8 June 2018 ### Program Committee The program committee consists of the organizers and the following reviewers: Nada Amin, University of Cambridge Clément Béra, RMOD - INRIA Lille Nord Europe Shigeru Chiba, University of Tokyo Benoit Daloze, JKU Linz Görel Hedin, Lund University Eric Jul, University of Oslo Stefan Marr, University of Kent Eliot Miranda, Cadence Design Systems Sarah Mount, King's College London Tobias Pape, Hasso Plattner Institute Jennifer Sartor, Vrije Universiteit Brussel + University Ghent ### Workshop Organizers Tim Felgentreff, Oracle Labs Potsdam Olivier Zendra, INRIA / LORIA

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