Embedded Software for SoC - Grupo de Mecatrônica EESC/USP

316 Chapter 23
We also measured the overhead breakdown for our approach. The overheads
are divided into three portions: the overheads due to sampling the
counters‚ the overheads due to the calculations for computing the objective
function‚ checking constraints‚ and selecting the best number of processors
(denoted Calculations in the figure)‚ and the overheads due to processor reactivation
(i.e.‚ transitioning a processor from the inactive state to the active
state). We observed that the contributions of these overheads are 4.11%‚
81.66%‚ and 14.23%‚ respectively. In other words‚ most of the overhead is
due to the (objective function + constraint evaluation) calculations performed
at runtime. The last component of our overheads (i.e.‚ locality-related one) is
difficult to isolate and is incorporated as part of the execution time/energy.
The results reported so far have been obtained by setting the number of
iterations used in training to 10% of the total number of iterations in the nest.
To study the impact of the size of the training set‚ we performed a set of
experiments where we modified the number of iterations used in the training
period. The results shown in Figure 23-4 indicate that for each benchmark
there exists a value (for the training iterations) that generates the best result.
Working with very small size can magnify the small variations between iterations
and might prevent us from detecting the best number of processors
accurately. At the other extreme‚ using a very large size makes sure that we
find the best number of processors; however‚ we also waste too much
time/energy during the training phase itself.
To investigate the influence of conservative training (Section 1.3.0) and
history based training (Section 1.3.0)‚ we focus on two applications: Atr and
Hyper (both of these applications have nine nests). In Atr‚ in some nests‚ there
are some significant variations (load imbalances) between loop iterations.
Therefore‚ it is a suitable candidate for conservative training. The first two
bars in Figure 23-5 show‚ for each nest‚ the normalized energy-delay product
due to our strategy without and with conservative training‚ respectively. We
see that the 3rd‚ 4th‚ 5th‚ and 9th nests take advantage of conservative training‚