Compiler-directed data partitioning for muiticluster processors

Multicluster architectures overcome the scaling problem of centralized resources by distributing the datapath, register file, and memory subsystem across multiple clusters connected by a communication network. Traditional compiler partitioning algorithms focus solely on distributing operations across the clusters to maximize instruction-level parallelism. The distribution of data objects is generally ignored. In this work, we examine explicit partitioning of data objects and its affects on operation partitioning. The partitioning of data objects must consider several factors: object size, access frequency/pattern, and dependence patterns between operations that manipulate the objects. This work proposes a compiler-directed approach to synergistically partition both data objects and computation across multiple clusters. First, a global view of the application determines the interaction between data memory objects and their associated computation. Next, data objects are partitioned across multiple clusters with knowledge of the associated computation required by the application. Finally, the resulting distribution of the data objects is relayed to a region-level computation partitioner, which carefully places computation operations in a performance-centric manner.