Biblio

In modern high-performance computing (HPC) systems, network congestion is an important factor that contributes to performance degradation. However, how network congestion impacts application performance is not fully understood. As Aries network, a recent HPC network architecture featuring a dragonfly topology, is equipped with network counters measuring packet transmission statistics on each router, these network metrics can potentially be utilized to understand network performance. In this work, by experiments on a large HPC system, we quantify the impact of network congestion on various applications' performance in terms of execution time, and we correlate application performance with network metrics. Our results demonstrate diverse impacts of network congestion: while applications with intensive MPI operations (such as HACC and MILC) suffer from more than 40% extension in their execution times under network congestion, applications with less intensive MPI operations (such as Graph500 and HPCG) are mostly not affected. We also demonstrate that a stall-to-flit ratio metric derived from Aries network counters is positively correlated with performance degradation and, thus, this metric can serve as an indicator of network congestion in HPC systems.

The Department of Defense (DoD) manages capabilities through the Joint Interoperability and Capability Development System (JCIDS) process. As part of this process, sponsors develop a series of DoD Architecture Framework (DoDAF) products to assist analysts understand the proposed capability and how it fits into the broader network of DoD legacy systems and systems under development. However, the Joint Staff, responsible for executing the JCIDS process, often analyzes these architectures in isolation without considering the broader network of systems. DoD leadership, the Government Accountability Organization, and others have noted the lack of the DoD's ability to manage the broader portfolio of capabilities in various reports and papers. Several efforts have proposed merging DoDAF architecture into a larger meta-architecture based on individual system architectures. This paper specifically targets the Systems View 3 (SV-3), System-to-system matrix, as an opportunity to merge multiple DoDAF architecture views into a network of system and understand the potential benefits associated with analyzing a broader perspective. The goal of merging multiple SV-3s is to better understand the interoperability of a system within the network of DoD systems as network metrics may provide insights into the relative interoperability of a DoD system. Currently, the DoD's definition of interoperability focuses on the system or capability's ability to enter and operate within the DoD Information Network (DoDIN); however, this view limits the definition of interoperability as it focuses solely on information flows and not resource flows or physical connections that should be present in a SV-3. The paper demonstrates the importance of including all forms of connections between systems in a network by comparing network metrics associated with the different types of connections. Without a complete set of DoDAF architectures for each system within the DoD and based on the potential classification of these products, the paper collates data that should be included in an SV-3 from open source, unclassified references to build the overall network of DoD systems. From these sources, a network of over 300 systems with almost 1000 connections emerges based on the documented information, resource, and physical connections between these legacy and planned DoD systems. With this network, the paper explores the quantification of individual system's interoperability through the application of nodal and network metrics from social network analysis (SNA). A SNA perspective on a network of systems provides additional insights beyond traditional network analysis because of the emphasis on the importance of nodes, systems, in the network as well as the relationship, connections, between the nodes. Finally, the paper proposes future work to explore the quantification of additional attributes of systems as well as a method for further validating the findings.