Re: [boost] [gsoc18][ublas] Proposal to add advanced matrix operations

Dear all, pon., 22 sty 2018 o 19:03 użytkownik SHIKHAR SRIVASTAVA via Boost < boost@lists.boost.org> napisał:

Hi all,

Thanks a lot for your suggestions. It made a lot of things clearer. My proposal was to add advanced matrix operations that were missing from ublas.

From the discussions it seems that it isn't a great idea to reimplement everything in ublas itself as there can be other options giving much better performance.

@karen - I am not specifically interested in machine learning. I want to contribute to ublas for performance and more usability in applications.

@david - The discussions lead me to think if adding Openblas/LAPACK backend for advanced matrix operation is a really good idea. It would boost performance and make some hardware acceleration possible (depending on blas implementation). It would be nice to have some interface with them. Can this be a potential gsoc anyone would like to mentor?

as others have already suggested, there's no point in manually implementing various linear algebra problems since there are multiple BLAS and LAPACK implementations which provide the most optimized and vectorized implementations for a given platform. However, I don't think that uBLAS would benefit from it unless the expression templates structure provides a way of effectively dispatching kernels to the library. I've used uBLAS briefly in 2015 and I've seen it much less performant than other C++ libraries. I experienced performance problems in nested expressions where creating a temporary is necessary, such as: "A * (a + b + c)" or "(A - B) * (C - D)". Were there major improvements to the library since 2015? Furthermore, I don't recall uBLAS being able to handle well matrix chain computations. Of course, the problem is exponential in time but I've seen decent implementations. For example, Armadillo solves this problem by broking down chains into subchains of the length shorter than four. Obviously, the algorithm is not complete but I've found it quite efficient in practice. The same applies to expressions involving matrix-vector products, I've seen both Blaze and Armadillo being able of finding the efficient parenthesization, e.g. A(Bv) instead of (AB)v. I've done a lot of linear algebra benchmarking in the last couple of months and I believe there are three high-performance C++ linear algebra libraries: Eigen, already mentioned by Hans, Armadillo, and Blaze. Blaze developers present several benchmarks on their website: https://bitbucket.org/blaze-lib/blaze/wiki/Benchmarks It might be a good starting point to benchmark uBLAS against other libraries and discuss whether it is possible to easily modify the library to offer competitive performance. I believe there's no point in targeting accelerators if the library cannot find an efficient mapping of the expression templates tree to BLAS/LAPACK kernels, unless your goal is to simply provide high-level API for BLAS/LAPACK functions. Best regards, Marcin

Regards Shikhar Srivastava

On 22-Jan-2018 10:59 PM, "Karen Shaeffer via Boost"

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wrote:

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On Mon, Jan 22, 2018 at 11:16:55AM +0100, Hans Dembinski via Boost wrote:

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Hi David, Rajaditya, Artyom, Shikhar,

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On 21. Jan 2018, at 13:00, David Bellot via Boost < boost@lists.boost.org> wrote:

- yes uBLAS is slow. Not only needs it a support for hardware acceleration but also it needs to be more modern (C++11/14/17) and its architecture could be much more improved. This would help integrating hardware acceleration, IMHO.

I am surprised that no one here mentioned Eigen, an IMHO excellent high-level header-only C++ library to do matrix-vector calculations.

http://eigen.tuxfamily.org/index.php?title=Main_Page < http://eigen.tuxfamily.org/index.php?title=Main_Page>

It uses expression templates to re-structure the expressions written by users to fast optimised code. It supports both matrices with dimensions known at compile-time and at run-time, and supports mixed versions, e.g. one dimension fixed the other dynamic.

Hello, Tensorflow uses Eigen.

If Shikhar is most interested in machine learning libraries, the TensorFlow might have some GSOC projects.

Karen.

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According to their own (admittedly, a bit outdated) benchmarks, they

are extremely competitive, either on par or even beating $$$ closed-source libraries.

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http://eigen.tuxfamily.org/index.php?title=Benchmark <

http://eigen.tuxfamily.org/index.php?title=Benchmark>

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Eigen tracks its own performance, so you don't need to worry about it

getting slower, but perhaps the others have gotten faster in the meantime.

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http://eigen.tuxfamily.org/index.php?title=Performance_monitoring <

http://eigen.tuxfamily.org/index.php?title=Performance_monitoring>

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I used the library in projects and it is very convenient. You just

write your expressions as you would on paper and let Eigen generate the fastest possible code out of that. I only dislike that Eigen does not play well with "auto" since C++11, because they use the assignment to a matrix or vector to trigger the evaluation of the expression template.

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I haven't done it myself, but Eigen seems to have a good strategy for

adding new features, too. People can start to develop their stuff in an "unsupported" subdirectory, which is not meant for production, but the code is nevertheless distributed with Eigen. This helps to exposes the experimental code to more adventurous users. Eventually, a project that passed the test of time can move from "unsupported" to the main library.

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Best regards, Hans

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