Embedded Software for SoC - Grupo de Mecatrônica EESC/USP

Generalized Data Transformations 433
The graph in Figure 31-6 shows the percentage reduction in execution times
(again, with respect to the original codes) with different cache sizes and
associativities. Each point in this graph represents an average value across
all our benchmarks. One can observe that, using a larger cache or a cache with
a higher set-associativity reduces the effectiveness of our approach. The reason
that the percentage savings due to our strategy reduce when the cache size or
associativity is increased can be explained as follows. When the cache
capacity/associativity is increased, the original (i.e., the one without optimization)
version starts to perform better as more data are captured in the
cache (as compared to the case with the small cache size/associativity). While
our approach also performs better with the increased cache size/associativity,
the improvement rate of the original codes is much higher than that of the
optimized codes. Consequently, we observe a reduction in execution time
savings as we increase the cache capacity/associativity. Nevertheless, even
with a 32 KB, 2-way data cache, we achieve around 15% reduction in
xecution time.
6. CONCLUDING REMARKS
Most existing work based on data transformations for improving data cache
behavior of array dominated codes target a single array at a time. In this work,
we develop a new, generalized data space transformation theory and present
experimental data to demonstrate its effectiveness. Our results clearly indicate
that the proposed approach is very successful in practice.