When a cache is shared by multiple cores, its space may be allocated either by sharing, partitioning, or both. We call the last case partition-sharing. This paper studies partition-sharing as a general solution, and presents a theory an technique for optimizing partition-sharing. We present a theory and a technique to optimize partition sharing. The theory shows that the problem of partition-sharing is reducible to the problem of partitioning. The technique uses dynamic programming to optimize partitioning for overall miss ratio, and for two different kinds of fairness.Finally, the paper evaluates the effect of optimal cache sharing and compares it with conventional solutions for thousands of 4-program co-run groups, with nearly 180 million different ways to share the cache by each co-run group. Optimal partition-sharing is on average 26% better than freefor-all sharing, and 98% better than equal partitioning. We also demonstrate the trade-off between optimal partitioning and fair partitioning.
Shared cache is generally optimized for overall throughput, fairness, or both. Increasingly in shared environments, e.g., compute clouds, users are unrelated to one another. In such circumstances, an overall gain in throughput does not justify an individual loss. This paper explores a new strategy for conservative sharing, which protects the cache occupancy for individual programs, but still enables full cache sharing whenever there is unused space.
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