Publications

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Power will be a first-class operating constraint for Exascale computing. In order to manage power consumption of systems, measurement and control methods need to be developed. While several approaches have been developed by hardware manufacturers, they are vendor-specific and in some cases implementation-specific interfaces. Integrating all of the individual device level measurement and control functionality in a single system is a difficult task that requires system specific code. Sandia National Laboratories, in collaboration with many industry and academic partners, has developed a Power API specification, consisting of a broad range of interfaces spanning from low-level hardware to platform management and accounting. In order for many of the interfaces to be useful, especially at large scale, measurement data must be collected and control directives must be distributed in a scalable manner. This paper details the challenges of providing large scale power measurement and control and the scalable collection and control distribution architecture that is being integrated into the Power API reference implementation.

Remote Direct Memory Access (RDMA) is expected to be an integral communication mechanism for future exascale systems - enabling asynchronous data transfers, so that applications may fully utilize all CPU resources while simultaneously sharing data amongst remote nodes. We examined this network-induced memory contention (NiMC), the interactions between RDMA and the memory subsystem when applications and out-of-band services compete for memory resources, and NiMC's resulting impact on application-level performance. For a range of hardware technologies and HPC workloads, we quantified NiMC and show that NiMC's impact grows with scale resulting in up to 3X performance degradation at scales as small as 8K processes even in applications that previously have been shown to be performance resilient in the presence of noise. We also evaluated three potential techniques to reduce NiMC's performance impact, namely hardware offloading, core reservation and software-based network throttling. While all three of these solutions show promise, we provide guidelines that help select the best solution for a given environment.

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Measuring and controlling the power and energy consumption of high performance computing systems by various components in the software stack is an active research area [13, 3, 5, 10, 4, 21, 19, 16, 7, 17, 20, 18, 11, 1, 6, 14, 12]. Implementations in lower level software layers are beginning to emerge in some production systems, which is very welcome. To be most effective, a portable interface to measurement and control features would significantly facilitate participation by all levels of the software stack. We present a proposal for a standard power Application Programming Interface (API) that endeavors to cover the entire software space, from generic hardware interfaces to the input from the computer facility manager.

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Future exascale systems are under increased pressure to find power savings. The network, while it consumes a considerable amount of power is often left out of the picture when discussing total system power. Even when network power is being considered, the references are frequently a decade or older and rely on models that lack validation on modern inter- connects. In this work we explore how dynamic mechanisms of an Infiniband network save power and at what granularity we can engage these features. We explore this within the context of the host controller adapter (HCA) on the node and for the fabric, i.e. switches, using three different mechanisms of dynamic link width, frequency and disabling of links for QLogic and Mellanox systems. Our results show that while there is some potential for modest power savings, real world systems need to improved responsiveness to adjustments in order to fully leverage these savings. This page intentionally left blank.

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This report provides in-depth information and analysis to help create a technical road map for developing next-generation programming models and runtime systems that support Advanced Simulation and Computing (ASC) work- load requirements. The focus herein is on asynchronous many-task (AMT) model and runtime systems, which are of great interest in the context of "Oriascale7 computing, as they hold the promise to address key issues associated with future extreme-scale computer architectures. This report includes a thorough qualitative and quantitative examination of three best-of-class AIM] runtime systems – Charm-++, Legion, and Uintah, all of which are in use as part of the Centers. The studies focus on each of the runtimes' programmability, performance, and mutability. Through the experiments and analysis presented, several overarching Predictive Science Academic Alliance Program II (PSAAP-II) Asc findings emerge. From a performance perspective, AIV runtimes show tremendous potential for addressing extreme- scale challenges. Empirical studies show an AM runtime can mitigate performance heterogeneity inherent to the machine itself and that Message Passing Interface (MP1) and AM11runtimes perform comparably under balanced conditions. From a programmability and mutability perspective however, none of the runtimes in this study are currently ready for use in developing production-ready Sandia ASC applications. The report concludes by recommending a co- design path forward, wherein application, programming model, and runtime system developers work together to define requirements and solutions. Such a requirements-driven co-design approach benefits the community as a whole, with widespread community engagement mitigating risk for both application developers developers. and high-performance computing runtime systein

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