Hi there,

I have been looking at the existing benchmarks, to see how to extend them to cover more functions as well as alternative implementations. The existing benchmarks have a few shortcomings that I would like to address:

* a single benchmark executable will measure a range of operations, and write output to stdout. It's impossible to benchmark individual operations

* operations are measured with a single set of inputs. It would be very helpful to be able to run operations on a range of inputs, to see how they perform over a variety of problem sizes.

* the generated output should be easily machine-readable, so it can be post-processed into benchmark reports (including performance charts).

(The above will be particularly useful as we are preparing PRs to include support for OpenCL backends (work that has been done by Fady Essam as a GSoC project).

I have attempted to prototype a few new benchmarks (matrix-matrix products, as well as matrix-vector products, for a variety of value-types), together with a simple script to produce graphs. For example, the attached plot was produced running:

```

 .../mm_prod -t float > mm_prod_float.txt

.../mm_prod -t double > mm_prod_double.txt

.../mm_prod -t fcomplex > mm_prod_fcomplex.txt

.../mm_prod -t dcomplex > mm_prod_dcomplex.txt

plot.py mm_prod_*.txt

```

I'd appreciate any feedback, both on the general concepts, as well as the code, which is here: https://github.com/boostorg/ublas/pull/57

Thanks,

-- 

      ...ich hab' noch einen Koffer in Berlin...
    

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