Dear All, As suggested I have packaged up a rough-n-ready version and stuffed it in the vault. It includes Doxygen generated documentation (HTML/PDF) and an example (example_timer.cpp) which itself has copious comments. Platform specifics (as far as I know) will be related to the chronometer. You may have to wrap your own chronometer (e.g. using something like gettimeofday() or getticks() from FFTW). I do not recommend using std::clock() for the included example as it is simply not precise enough - consequently you will grow bored waiting for it to finish! http://www.boostpro.com/vault/index.php? action=downloadfile&filename=ejg_timer.zip&directory=Tools http://tinyurl.com/nejw9o On my Mac, the example can be built and run by unzipping the file and doing the following: $ cd ejg_timer/ $ g++ -DNDEBUG -O3 -I. -o example_timer ejg/example_timer.cpp $ ./example_timer Calibrating the clock, for a slow timer this may take a while! Point estimate of clock overhead (t_c): 0.0986196 Potential jitter on t_c (sigma_c) : 0.633371 Increasing precision until I can discriminate between function a and b. The nominal confidence interval (minimum, [median], maximum) represents the region: 1-alpha=0.95 Timing at a nominal precision of 50%: We cannot distinguish between A and B: (-1.62543 [-0.339245] 1.78913)% Timing at a nominal precision of 25%: We cannot distinguish between A and B: (0 [0] 2.38656)% Timing at a nominal precision of 12.5%: We cannot distinguish between A and B: (0 [0] 0.69492)% Timing at a nominal precision of 6.25%: We cannot distinguish between A and B: (0 [1.84168] 3.71728)% Timing at a nominal precision of 3.125%: We cannot distinguish between A and B: (-0.173508 [0.757778] 0.924288)% Timing at a nominal precision of 1.5625%: A is faster than B by: (0.563103 [0.690461] 0.940433)% As you will note, there's a fair bit of extra cruft that has come along for the ride, in particular I think it is worth being aware of: striding_iterator -- Perhaps boost could do with this as a separate thing - it's pretty handy. robust_linear_fit -- A least absolute deviation robust linear fit, used to calibrate the clock overhead. This is preferable to least squares as it is insensitive to outliers. statistics::ks_test -- This implements a Kolmogorov-Smirnov two- sample test and is critical to answering the question 'have we taken enough samples'. wilcoxon_ci -- Carry out a Wilcoxon signed-rank test. Used to determine confidence intervals on paired samples for the relative timing. I will take some time to carefully review my thought processes to check that what I did is what I intended to do. There are some subtle points which stretch my statistics understanding regarding the repeated application of the ks_test - I suspect this is best approached with some Baysian prior technique - that said what I have seems to work well! Perhaps more by luck than judgement! Comments and questions are welcome. -ed ------------------------------------------------ "No more boom and bust." -- Dr. J. G. Brown, 1997