11–20 of 34 posts
In the first plot, why do all libraries slow down at the n=1000 mark? something to do with cache?
better link: https://bitbucket.org/blaze-lib/blaze
In the first plot, why do all libraries slow down at the n=1000 mark? something to do with cache?
I've had great success with Blaze, despite the fact that it has received little publicity compared to alternatives like Eigen, Armadillo, etc. Blaze is consistently the leader of the pack in benchmarks, and even outperforms Intel MKL on the Xeon E5-2660 (the CPU for which the benchmark results are shown).
For what problems? General statements like this are hard to back up, especially in the wild world of numerical linear algebra. From my experience, there are currently no good distributed-memory open source sparse-direct solvers. No good distributed-memory ILU implementation, either. Scalability is almost non-existant beyond 100 cores.
better link: https://bitbucket.org/blaze-lib/blaze
Thanks, I tried finding the link to the Bitbucket repository but I had a hard time finding the link via Google.
I've had great success with Blaze, despite the fact that it has received little publicity compared to alternatives like Eigen, Armadillo, etc. Blaze is consistently the leader of the pack in benchmarks, and even outperforms Intel MKL on the Xeon E5-2660 (the CPU for which the benchmark results are shown).
For what problems? General statements like this are hard to back up, especially in the wild world of numerical linear algebra. From my experience, there are currently no good distributed-memory open source sparse-direct solvers. No good distributed-memory ILU implementation, either. Scalability is almost non-existant beyond 100 cores.
I am considering converting a C++03 math library to C++14 as a side project to learn C++14 and I examined Eigen and Blaze. Eigen's code size seems to be a fraction of Blaze, even though their functionalities are similar. Eigen also has some design documents while Blaze has papers but not much more. It seems I will try my hands on Eigen library for now. It is amazing that a couple of people could do in a few years; Bl…
A common example is matrix multiply. For smaller matrices it's faster to use naive O(n^3) multiply because of the large constant factor with Strassen's algorithm. At some point n^3 will dominate the constant factor, and Strassen's becomes better. To get the best performance in all cases, both algorithms need to be implemented, which increases the code size.
I am considering converting a C++03 math library to C++14 as a side project to learn C++14 and I examined Eigen and Blaze. Eigen's code size seems to be a fraction of Blaze, even though their functionalities are similar. Eigen also has some design documents while Blaze has papers but not much more. It seems I will try my hands on Eigen library for now. It is amazing that a couple of people could do in a few years; Bl…
Eigen is header only and heavily templated, which certainly keeps the source down. However, SLOC is a poor metric for the quality of a codebase, especially scientific ones. I've certainly found many instances where longer line counts are more performant, for instance with hand-unrolling loops ( very rare edge case, not suggesting doing this as a rule!). Unless you intend to become involved in development of the libra…
.. Unless it's written in a completely uncomprehensible way of course (such as some meta c++ stuff), but in languages with good metaprogramming.
Earlier quoted context omitted.
For what problems? General statements like this are hard to back up, especially in the wild world of numerical linear algebra. From my experience, there are currently no good distributed-memory open source sparse-direct solvers. No good distributed-memory ILU implementation, either. Scalability is almost non-existant beyond 100 cores.
That's because direct solvers can't scale. If you want to solve a large (distributed over hundreds of nodes) sparse linear algebra problem as fast as possible, decades of research have been poured into efficient techniques (Krylov methods, Multigrid, preconditioners) for solving them iteratively.