Sean J. Treichler

Isometry: A Path-Based Distributed Data Transfer System

Data transfers in parallel systems have a significant impact on the performance of applications. Most existing systems generally support only data transfers between memories with a direct hardware connection and have limited facilities for handling transformations to the datas layout in memory. As a result, to move data between memories that are not directly connected, higher levels of the software stack must explicitly divide a multi-hop transfer into a sequence of single-hop transfers and decide how and where to perform data layout conversions if needed. This approach results in inefficiencies, as the higher levels lack enough information to plan transfers as a whole, while the lower level that does the transfer sees only the individual single-hop requests. We present Isometry, a path-based distributed data transfer system. The Isometry path planner selects an efficient path for a transfer and submits it to the Isometry runtime, which is optimized for managing and coordinating the direct data transfers. The Isometry runtime automatically pipelines sequential direct transfers within a path and can incorporate flexible scheduling policies, such as prioritizing one transfer over another. Our evaluation shows that Isometry can speed up data transfers by up to 2.2x and reduce the completion time of high priority transfers by up to 95% compared to the baseline Realm data transfer system. We evaluate Isometry on three benchmarks and show that Isometry reduces transfer time by up to 80% and overall completion time by up to 60%.

A Novel Shard-Based Approach for Asynchronous Many-Task Models for In Situ Analysis

We present the current status of our work towards a scalable, asynchronous many-task, in situ statistical analysis engine using the Legion runtime system, expanding upon earlier work, that was limited to a prototype implementation with a proxy mini-application as a surrogate for a full-scale scientific simulation code. In contrast, we have more recently integrated our in situ analysis engines with S3D, a full-size scientific application, and conducted numerical tests therewith on the largest computational platform currently available for DOE science applications. The goal of this article is thus to describe the SPMD-Legion methodology we devised in this context, and compare the data aggregation technique deployed herein to the approach taken within our previous work.