  OpenMP is the de facto API for parallel programming in HPC applications.
These programs are often computed in data centers, where energy consumption is
a major issue. Whereas previous work has focused almost entirely on
performance, we here analyse aspects of OpenMP from an energy consumption
perspective. This analysis is accomplished by executing novel microbenchmarks
and common benchmark suites on data center nodes and measuring the energy
consumption. Three main aspects are analysed: directive-generated loop tiling
and unrolling, parallel for loops and explicit tasking, and the policy of
handling blocked threads. For loop tiling and unrolling, we find that tiling
can yield significant energy savings for some, mostly unoptimised programs,
while directive-generated unrolling provides very minor improvement in the best
case and degenerates performance majorly in the worst case. For the second
aspect, we find that parallel for loops yield better results than explicit
tasking loops in cases where both can be used. This becomes more prominent with
more fine-grained workloads. For the third, we find that significant energy
savings can be made by not descheduling waiting threads, but instead having
them spin, at the cost of a higher power consumption. We also analyse how the
choice of compiler affects the above questions by compiling programs with each
of ICC, Clang and GCC, and find that while neither is strictly better than the
others, they can produce very different results for the same compiled programs.
As a final step, we combine the findings of all results and suggest novel
compiler directives as well as general recommendations on how to reduce energy
consumption in OpenMP programs.
