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[core] [addressof.hpp] [addressof_test2.cpp] ISO C++ says that these are ambiguous
by Sergey Sprogis 25 Dec '14

25 Dec '14

It looks like he following code fragment from boost/core/addressof.hpp (latest trunk version) violates ISO C++ overloading rules: template<class T> struct addressof_impl { static BOOST_FORCEINLINE T * f( T & v, long ) { return reinterpret_cast<T*>( &const_cast<char&>(reinterpret_cast<const volatile char &>(v))); } static BOOST_FORCEINLINE T * f( T * v, int ) { return v; } }; As a result, libs/core/test/addressof_test2.cpp fails to compile with Oracle Studio 12.4 C++ compiler while gcc-4.8.2 produces just warning here. It's easy to reproduce that behavior of both compilers with 26 lines test case listed below: gcc -c t.cc t.cc:7:33: warning: ISO C++ says that these are ambiguous, even though the worst conversion for the first is better than the worst conversion for the second: [enabled by default] return addressof_impl<T>::f(v,0); ^ CC -c t.cc "t.cc", line 7: Error: Overloading ambiguity between "static addressof_impl<convertible2>::f(convertible2&, long)" and "static addressof_impl<convertible2>::f(convertible2*, int)". t.cc ==================================== template<class T> struct addressof_impl{ static T *f(T &v,long){} static T *f(T *v,int){} }; template<class T> T *addressof(T &v){ return addressof_impl<T>::f(v,0); } template<class T> void scalar_test(T* =0){ T *px=new T(); T &x =*px; (addressof(x)==px)? 1 : 0; } class convertible{ public: convertible(int=0){} }; class convertible2{ public: convertible2(int=0){} operator convertible2 *()const{ return 0; } }; void foo(){ scalar_test<convertible>(); scalar_test<convertible2>(); } ============================== It would be nice if boost/core/addressof.hpp file will be changed somehow to fit the proper ISO C++ requirements. Sergey

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[compute] Some questions
by Andrey Semashev 23 Dec '14

23 Dec '14

Hi, I have no experience with OpenCL or GPU computing in general, so bear with me if my questions sound silly. I have a few questions regarding Boost.Compute: 1. When you define a kernel (e.g. with the BOOST_COMPUTE_FUNCTION macro), is this kernel supposed to be in C? Can it reference global (namespace scope) objects and other functions? Other kernels? 2. When is the kernel compiled and uploaded to the device? Is it possible to cache and reuse the compiled kernel? 3. Why is the library not thread-safe by default? I'd say, we're long past single-threaded systems now, and having to always define the config macro is a nuisance. 4. Is it possible to upload the data to process to the device's local memroy from a user-provided buffer, without copying it to boost::compute::vector? Same for downloading. What I'd like to do is move some of data processing to the GPU while the rest is performed on the CPU (possibly with other libraries), and avoid excessive copying. 5. Is it possible to pass buffers in the device-local memory between different processes (on the CPU) without downloading/uploading data to/from the CPU memory? 6. Is it possible to discover device capabilities? E.g. the amount of local memory (total/used/free), execution units, vendor and device name? Thanks.

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[compute] Review period extended till December 30 + sum up
by Antony Polukhin 23 Dec '14

23 Dec '14

Hi, Review period of Compute library extended till December 30. Some notes and answers to them that were pointed out during review so far: * Some of the algorithms could be tuned for a specific hardware - Library author is working on a auto-tuning solution and improves existing algos * Why OpenCL C API used instead of C++ API? - C++ API had issues, C API allows a bit more control. No C++ API for OpenCL 2.0 * API is not N4105 compatible, library is not N*** compatible - This could be easily fixed by a library that will use Compute as a backend * Some of the algorithms return futures while other work with command queues - This is because of the OpenCL design + async chains/futures would be probably added later * Not all the types of the Khronos API are supported - A few types are not documented yet, there's a patch in the works which would allow the Khronos C++ types to be passed into Compute algorithms * A few type related errors could be detected at compile time - This will be fixed, more asserts would be added to runtime only checkable places. Report issues to the tracker https://github.com/kylelutz/compute/issues * On small data sets CPU algorithm would work faster than a GPGPU. How about implicitly dispatching algo on CPU in those cases? - The call on whether to execute the algorithm should be left up to the user. While library author agrees that this would be a useful feature, he just don't think Compute is the right place for that logic. * How about providing way to do chains of async operations - This is a big task that will be solved some day. * How about providing Boost.ASIO like error handling via throw and error_code - Implementing an approach like ASIO's wouldn't be that difficult. Thanks to all the reviewers for spending their time and providing useful comments so far! -- Best regards, Antony Polukhin

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[compute] Define user functions using lambda expressions
by Asbjørn 22 Dec '14

22 Dec '14

Hi, I'm probably missing something obvious, but how would I go about making a custom function using a lambda expression? Say I want to declare some function "sqr" which returns "_1 * _1", and then use this in a transform() lambda expression, ie transform(..., 2.0 * sqr(_1) - 3.0 * _1 + 5.0, ...); to evaluate 2x^2 - 3x + 5. If I try to use BOOST_COMPUTE_FUNCTION() I can't use the resulting function in a lambda expression it seems. Cheers - Asbjørn

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[compute] some comments
by Mathias Gaunard 22 Dec '14

22 Dec '14

Hi, I do not know if I'll be doing a full review, but I have some comments from having skimmed through the code and documentation. From a quick glance, from all the algorithms that are supported, quite a few are not easy to implement with the OpenCL programming model efficiently. I think the documentation should say more about the implementation strategies of each algorithm, its complexity and how much it is parallelized.

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[icl] insert_iterator and elements
by Jeff Flinn 22 Dec '14

22 Dec '14

interval_set has overloaded insert methods for both intervals and elements. Is there a reason there insert iterator lacks this ability. The line in the code below *itr = 4; fails to compile. This could be made to compile by: - adding an implicit constructor 'interval( const element& )' for the underlying (discrete) intervals, initializing to [element, element+1), or [element, element]. - adding an overloaded operator=( const element& ) - adding an element_insert_iterator class I've encountered 2 domains now where I need to construct an interval_set<int> from a range of ints. The most natural ways to accomplish this (for me) are: boost::copy( ints, inserter(int_interval_set) ); or boost::copy_range<int_interval_set>( ints ); Of course I can transform the incoming ints, but this seems a common enough usage pattern for the library to provide. Especially since the lack of this facility is asymmetric to the insert overloads of interval_set. #include <iostream> #define BOOST_ICL_USE_STATIC_BOUNDED_INTERVALS #include <boost/icl/interval_set.hpp> #include <boost/range/algorithm/copy.hpp> int main() { typedef boost::icl::interval_set<int> IdSet; typedef IdSet::interval_type IdInterval; IdSet ids; ids.insert(3); boost::icl::insert_iterator<IdSet> itr(ids, ids.end()); *itr = IdInterval(1,2); //*itr = 4; //compile error no viable operator = boost::copy(ids, std::ostream_iterator<IdInterval>(std::cout)); std::cout << "\n"; return 0; }

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[compute] review
by Pavan Yalamanchili 22 Dec '14

22 Dec '14

*Design and Implementation* The library provides an STL-like library for OpenCL devices. Although there are other libraries offering similar behavior, Boost.Compute is the most complete and the most generalized. The directory structure is well organized and easy to dig through to understand the implementation or debug an issue. Others have pointed out the existence of the OpenCL C++ wrapper and the fact that Compute library does not use it. Here are a few reasons why I think the author was *right* in his design decisions. - The C++ wrapper begins to look similar to the original OpenCL C interface once you start using it in a generalized fashion. - cl.hpp is monolithic (single file 12k+ LOC) and is not modular enough to only include the parts that you need. - Compute and cl.hpp have two different goals. The biggest selling point of Compute is the set of algorithms it supports not its C++ wrapper around OpenCL. Having said that, I think the Compute library might be better off with an additional interface that accepts the OpenCL-C++ wrapped objects. *Documentation* The documentation is comprehensive and the API is documented well. There are a few improvements that can be done. For example, the reference is at the bottom of the TOC and is hard to see immediately. The API reference in the TOC can also be expanded out a bit more to show the general categories of algorithms that are supported. *Potential Usefulness* It is fairly easy to transition from applications using vector algorithms in STL to Compute. The wide availability of OpenCL devices makes the library useful to a large user base. *Domain Knowledge* I am a developer of the ArrayFire library[1]. I am the lead engineer of a company that specializes in this domain. I consider myself to be fairly knowledgeable in this domain. *Experience with the library* ArrayFire depends on Boost.Compute for a few algorithms in our OpenCL backend. We explicitly and implicitly (via ArrayFire) test Boost.Compute on a variety of hardware / compilers / operating systems. Compilers and Operating systems we use: - gcc 4.8, 4.9 on various Linux distributions - clang 3.4 on OSX - Visual Studio 2013 on Windows. We have found some bugs through our usage that have been mostly resolved by the author or by patches sent by us. *Conclusion* The experience overall has been fairly positive. But there are certainly *some* rough edges that need resolving. An issue that will plague any OpenCL library is the performance portability across various devices. It would be nice if the author can talk about how he plans to eventually address this issue. Overall, Boost.Compute will be a great addition to Boost. But before it is accepted the following issues need to be addressed. - The tests need to be a bit more comprehensive. - Due to the general nature of OpenCL, there needs to be a list of "officially supported" devices. - Make sure all the tests are passing on the supported devices. *Links* [1] https://github.com/arrayfire/arrayfire

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A Google Summer of Code Proposal(Boost C++)
by Mudit Sharma 22 Dec '14

22 Dec '14

Respected Sir, I have been studying the Boost C++ libraries.I see that there isn't any library for game development in Boost C++. I propose a new library for game development for Boost C++ under which I propose to use the pygame library of python.Make a Boost C++ analogue for pygame library of python.. *I would like to know if this can be taken as a GSoC'15 project?* Thanking You Mudit Sharma (Student,Pursuing Electronics And Communication Engg. At Delhi Technological University,Delhi,INDIA)

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IDE for developing Boost
by Jens Müller 20 Dec '14

20 Dec '14

Hi, I am looking for recommendations for developing Boost, especially code in the graph library. At work, most developers (not for Boost, for some commercial software), most developers are using Eclipse. This works quite fine as soon as it has properly auto-discovered include paths etc. But before fiddling around with Eclipse to get all the settings right, I'd wanted to to ask for your recommendations. And maybe there are already settings available to make it "just work", at least initially. Cheers, Jens

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[Phoenix] [Bug Fix] A fix for boost::bind compatiblity now on test.
by Fletcher, John P 20 Dec '14

20 Dec '14

For a long time there have been problems with some of the tests for Boost Phoenix compatibility with Boost Bind. These have shown up with recent compilers, gcc 4.9.0 and Clang 3.5 and 3.6. I have discovered that the problems were caused by incorrect overload resolution where some cases were being wrongly allocated. I have just uploaded a patch to develop which passes all tests with gcc 4.9.0 and I hope fixes this problem. The solution involves the use of boost::lazy_disable_if to reject the wrong functions from the overload set and allow them when they are needed. Tests affected are: bind_member_function_tests, bind_mf2_test, bind_test I have not traced a bug report which directly relates to this. I hope this helps. Please reply if there are any problems. Best wishes for the season. John Fletcher

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