Benchmarks for Blaze, A high-performance C++ math library

I remember this library being discussed on the Eigen list in 2012. Here&#x27;s the thread:<p><a href="http:&#x2F;&#x2F;thread.gmane.org&#x2F;gmane.comp.lib.eigen&#x2F;3423" rel="nofollow">http:&#x2F;&#x2F;thread.gmane.org&#x2F;gmane.comp.lib.eigen&#x2F;3423</a><p>The main critique at the time was that Blaze always assumes perfectly aligned data, including inside(!) the matrix, and pads the data with zeros if that is not the case. Of course, this makes it impossible to map external data, which is a huge downside. I&#x27;m not sure if that is still the case, but from skimming through the docs it doesn&#x27;t look to me like this has changed.

better link: <a href="https:&#x2F;&#x2F;bitbucket.org&#x2F;blaze-lib&#x2F;blaze" rel="nofollow">https:&#x2F;&#x2F;bitbucket.org&#x2F;blaze-lib&#x2F;blaze</a>

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&#x27;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; Blaze has hundred of thousands of lines of code.

Does Blaze implement its own BLAS subroutines? Or does it wrap around an existing BLAS library?

It would be interesting to see benchmarks of sparse operations, as well.

I&#x27;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).

In the first plot, why do all libraries slow down at the n=1000 mark? something to do with cache?