<filename>publication/pdp-17/pdp-17.bib
@inproceedings{pdp17,
 abstract = {Power-aware computing is gaining an increasing attention both in academic and industrial settings. The problem of guaranteeing a given
QoS requirement (either in terms of performance or power consumption) can be faced by selecting and dynamically adapting the amount of physical
and logical resources used by the application. In this study, we considered standard multicore platforms by taking as a reference approaches for power-aware
computing two well-known dynamic reconfiguration techniques: Concurrency Throttling and Thread Packing. Furthermore, we also studied the impact of using simultaneous
multithreading (e.g., Intel’s HyperThreading) in both techniques. In this work, leveraging on the applications of the PARSEC benchmark suite, we evaluate these
techniques by considering performance-power trade-offs, resource efficiency, predictability and required programming effort. The results show that, according to the
comparison criteria, these techniques complement each other.},
 author = {<NAME> <NAME>, <NAME> <NAME>, <NAME> <NAME>},
 booktitle = {Proceedings of the 25th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2017},
 doi = {10.1109/PDP.2017.39},
 location = {St. Petersburg, Russia},
 pages = {219-223},
 title = {Evaluating Concurrency Throttling and Thread Packing on SMT Multicores},
 year = {2017}
}

