Publications

Abstract not provided.

The Vanguard program informally began in January 2017 with the submission of a white pa- per entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia Na- tional Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads. This document describes the high-level Vanguard program goals, the Vanguard-Astra project acquisition plan and procurement up to contract placement, the initial software stack environment planned for the Vanguard-Astra platform (Astra), a description of how the communities of users will utilize the platform during the transition from the open network to the classified network, and initial performance results.

Abstract not provided.

The Vanguard program informally began in January 2017 with the submission of a white pa- per entitled "Sandia's Vision for a 2019 Arm Testbed" to NNSA headquarters. The program proceeded in earnest in May 2017 with an announcement by Doug Wade (Director, Office of Advanced Simulation and Computing and Institutional R&D at NNSA) that Sandia Na- tional Laboratories (Sandia) would host the first Advanced Architecture Prototype platform based on the Arm architecture. In August 2017, Sandia formed a Tri-lab team chartered to develop a robust HPC software stack for Astra to support the Vanguard program goal of demonstrating the viability of Arm in supporting ASC production computing workloads. This document describes the high-level Vanguard program goals, the Vanguard-Astra project acquisition plan and procurement up to contract placement, the initial software stack environment planned for the Vanguard-Astra platform (Astra), a description of how the communities of users will utilize the platform during the transition from the open network to the classified network, and initial performance results.

High-performance computing (HPC) systems are critically important to the objectives of universities, national laboratories, and commercial companies. Because of the cost of deploying and maintaining these systems ensuring their efficient use is imperative. Job scheduling and resource management are critically important to the efficient use of HPC systems. As a result, significant research has been conducted on how to effectively schedule user jobs on HPC systems. Developing and evaluating job scheduling algorithms, however, requires a detailed understanding of how users request resources on HPC systems. In this paper, we examine a corpus of job data that was collected on Trinity, a leadership-class supercomputer. During the stabilization period of its Intel Xeon Phi (Knights Landing) partition, it was made available to users outside of a classified environment for the Trinity Open Science Phase 2 campaign. We collected information from the resource manager about each user job that was run during this Open Science period. In this paper, we examine the jobs contained in this dataset. Our analysis reveals several important characteristics of the jobs submitted during the Open Science period and provides critical insight into the use of one of the most powerful supercomputers in existence. Specifically, these data provide important guidance for the design, development, and evaluation of job scheduling and resource management algorithms.

Large-scale HPC systems increasingly incorporate sophisticated power management control mechanisms. While these mechanisms are potentially useful for performing energy and/or power-aware job scheduling and resource management (EPA JSRM), greater understanding of their operation and performance impact on real-world applications is required before they can be applied effectively in practice. In this paper, we compare static p-state control to static node-level power cap control on a Cray XC system. Empirical experiments are performed to evaluate node-to-node performance and power usage variability for the two mechanisms. We find that static p-state control produces more predictable and higher performance characteristics than static node-level power cap control at a given power level. However, this performance benefit is at the cost of less predictable power usage. Static node-level power cap control produces predictable power usage but with more variable performance characteristics. Our results are not intended to show that one mechanism is better than the other. Rather, our results demonstrate that the mechanisms are complementary to one another and highlight their potential for combined use in achieving effective EPA JSRM solutions.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Advanced power measurement capabilities are becoming available on large scale High Performance Computing (HPC) deployments. There exist several approaches to providing power measurements today, primarily through in-band (e.g. RAPL) and out-of-band measurements (e.g. power meters). Both types of measurement can be augmented with application-level profiling, however it can be difficult to assess the type and detail of measurement needed to obtain insight from the application power profile. This paper presents a taxonomy for classifying power profiling techniques on modern HPC platforms. Three HPC mini-applications are analyzed across three production HPC systems to examine the level of detail, scope, and complexity of these power profiles. We demonstrate that a combination of out-of-band measurement with in-band application region profiling can provide an accurate, detailed view of power usage without introducing overhead. This work also provides a set of recommendations for how to best profile HPC workloads.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

With the increased scale expected on future leadership-class systems, detailed information about the resource usage and performance of MPI message matching provides important insights into how to maintain application performance on next-generation systems. However, obtaining MPI message matching performance data is often not possible without significant effort. A common approach is to instrument an MPI implementation to collect relevant statistics. While this approach can provide important data, collecting matching data at runtime perturbs the application’s execution, including its matching performance, and is highly dependent on the MPI library’s matchlist implementation. In this paper, we introduce a trace-based simulation approach to obtain detailed MPI message matching performance data for MPI applications without perturbing their execution. Using a number of key parallel workloads, we demonstrate that this simulator approach can rapidly and accurately characterize matching behavior. Specifically, we use our simulator to collect several important statistics about the operation of the MPI posted and unexpected queues. For example, we present data about search lengths and the duration that messages spend in the queues waiting to be matched. Data gathered using this simulation-based approach have significant potential to aid hardware designers in determining resource allocation for MPI matching functions and provide application and middleware developers with insight into the scalability issues associated with MPI message matching.