Declarative Tuning for Locality in Parallel Programs

摘要

Optimized placement of data and computation for locality is critical for improving performance and reducing energy consumption on modern computing systems. However, for most programming models, modifying data and computation placements typically requires rewriting large portions of the application, thereby posing a huge performance portability challenge in today's rapidly evolving architecture landscape. In this paper we present TunedCnC, a novel, declarative and flexible CnC tuning framework for controlling the spatial and temporal placement of data and computation by specifying hierarchical affinity groups and distribution functions. TunedCnC emphasizes a separation of concerns: the domain expert specifies a parallel application by defining data and control dependences, while the tuning expert specifies how the application should be executed on a given architecture - defining when and where for data and computation placement. The application remains unchanged when tuned for a different platform or towards different performance goals. We evaluate the utility of TunedCnC on several applications, and demonstrate that varying the tuning specification can have a significant impact on an application's performance. Our evaluation is performed using an implementation of the Concurrent Collections (CnC) declarative parallel programming model, but our results should be applicable to tuning of other data-flow task-parallel programming models as well.