PNNL

Abstract

We introduce Scioto, Shared Collections of Task Objects, a framework for supporting task-parallelism in one-sided and global-view parallel programming models. Scioto provides lightweight, locality aware dynamic load balancing and interoperates with existing parallel models including MPI, SHMEM, CAF, and Global Arrays. Through task parallelism, the Scioto framework provides a solution for overcoming load imbalance and heterogeneity as well as dynamic mapping of computation onto emerging multicore 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.