Efficiently allocating shared on-chip resources across cores is critical to optimize execution in chip multiprocessors (CMPs). Techniques proposed in the literature often rely on global, centralized mechanisms that seek to maximize system throughput. Global optimization may hurt scalability: as more cores are integrated on a die, the search space grows exponentially, making it harder to achieve optimal or even acceptable operating points at run-time without incurring significant overheads. In this paper, we propose XChange, a novel CMP resource allocation mechanism that delivers scalable high throughput and fairness. Through XChange, the CMP functions as a market, where each shared resource is assigned a price which changes over time, and each core seeks to maximize its own utility, by bidding for these shared resources. Because each core works largely independently, the resource allocation becomes a scalable, mostly distributed decision-making process. In addition, by distributing the resources proportionally to the bids, the system avoids unfairness, treating each core in an unbiased manner. Our evaluation shows that, using detailed simulations of a 64-core CMP configuration running a variety of multiprogrammed workloads, the proposed XChange mechanism improves system throughput (weighted speedup) by about 21% on average, and fairness (harmonic speedup) by about 24% on average, compared with equal-share on-chip cache and power distribution. On both metrics, that is at least about twice as much improvement over equal-share as a state-ofthe-art centralized allocation scheme. Furthermore, our results show that XChange is significantly more scalable than the state-of-the-art centralized allocation scheme we compare against.
In REF , the CMP architecture functions as a "market" where each core bids for "priced" shared resources with its "budget." By allocating resources proportionally to the bids, the system avoids unfairness and treats each core in an unbiased manner.
XChange: A market-based approach to scalable dynamic multi-resource allocation in multicore architectures
2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)
2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)
Computer Science