Both commercial and scientific workloads benefit from concurrency and exhibit data sharing across threads/processes. The resulting sharing patterns are often fine-grain, with the modified cache lines still residing in the writer's primary cache when accessed. Chip multiprocessors present an opportunity to optimize for fine-grain sharing using direct access to remote processor components through low-latency on-chip interconnects. In this paper, we present Adaptive Replication, Migration, and producer-Consumer Optimization (ARMCO), a coherence protocol that, to the best of our knowledge, is the first to exploit direct access to the L1 caches of remote processors (rather than via coherence mechanisms) in order to support fine-grain sharing.   Our goal is to provide support for tightly coupled sharing by recognizing and adapting to common sharing patterns such as migratory, producer-consumer, multiple-reader, and multiple read-write. The protocol places data close to where it is most needed and leverages direct access when following conventional coherence actions proves wasteful. Via targeted optimizations for each of these access patterns, our proposed protocol is able to reduce the average access latency and increase the effective cache capacity at the L1 level with on-chip storage overhead as low as 0.38%. Full-system simulations of 16-processor CMPs show an average (geometric mean) speedup of 1.13 (ranging from 1.04 to 2.26) for 12 commercial, scientific, and mining workloads, with an average of 1.18 if we include 2 microbenchmarks. ARMCO also reduces the on-chip bandwidth requirements and dynamic energy (power) consumption by an average of 33.3% and 31.2% (20.2%) respectively. By evaluating optimizations at both the L1 and the L2 level, we demonstrate that when considering performance, optimization at the L1 level is more effective at supporting fine-grain sharing than that at the L2 level.
['Hemayet Hossain', 'Sandhya Dwarkadas', 'Michael C. Huang']
Improving support for locality and fine-grain sharing in chip multiprocessors