Scioto: A Framework for Global-View Task Parallelism

Abstract

We introduce Scioto, shared collections of task objects, a lightweight framework for providing task management on distributed memory machines under one-sided and global-view parallel programming models. Scioto provides locality aware dynamic load balancing and interoperates with MPI, ARMCI, and global arrays. Additionally, Scioto's task model and programming interface are compatible with many other existing parallel models including UPC, SHMEM, and CAF. Through task parallelism, the Scioto framework provides a solution for overcoming irregularity, load imbalance, and heterogeneity as well as dynamic mapping of computation onto emerging architectures. In this paper, we present the design and implementation of the Scioto framework and demonstrate its effectiveness on the unbalanced tree search (UTS) benchmark and two quantum chemistry codes: the closed shell self-consistent field (SCF) method and a sparse tensor contraction kernel extracted from a coupled cluster computation. We explore the efficiency and scalability of Scioto through these sample applications and demonstrate that is offers low overhead, achieves good performance on heterogeneous and multicore clusters, and scales to hundreds of processors.