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Summary Energy‐efficient scientific applications require insight into how high performance computing system features impact the applications' power and performance. This insight can result from the development of performance and power models. In this article, we use the modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and 10 machine learning methods to model and predict performance and power consumption and compare their prediction error rates. We use an algorithm‐based fault‐tolerant linear algebra code and a multilevel checkpointing fault‐tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experimental results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. By utilizing the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential application optimizations, and we predict theoretical outcomes of the optimizations. Based on two collected datasets, we analyze and compare the prediction accuracy in performance and power consumption using MuMMI and 10 machine learning methods.
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Workload Shaping Energy Optimizations with Predictable Performance for Mobile Sensing
Energy-efficiency is a key concern in mobile sensing applications, such as those for tracking social interactions or physical activities. An attractive approach to saving energy is to shape the workload of the system by artificially introducing delays so that the workload would require less energy to process. However, adding delays to save energy may have a detrimental impact on user experience. To address this problem, we present Gratis, a novel paradigm for incorporating workload shaping energy optimizations in mobile sensing applications in an automated manner. Gratis adopts stream programs as a high-level abstraction whose execution is coordinated using an explicit power management policy. We present an expressive coordination language that can specify a broad range of workload-shaping optimizations. A unique property of the proposed power management policies is that they have predictable performance, which can be estimated at compile time, in a computationally efficient manner, from a small number of measurements. We have developed a simulator that can predict the energy with a average error of 7% and delay with a average error of 15%, even when applications have variable workloads. The simulator is scalable: hours of real-world traces can be simulated in a few seconds. Building on the simulator's accuracy and scalability, we have developed tools for configuring power management policies automatically. We have evaluated Gratis by developing two mobile applications and optimizing their energy consumption. For example, an application that tracks social interactions using speaker-identification techniques can run for only 7 hours without energy optimizations. However, when Gratis employs batching, scheduled concurrency, and adaptive sensing, the battery lifetime can be extended to 45 hours when the end-to-end deadline is one minute. These results demonstrate the efficacy of our approach to reduce energy consumption in mobile sensing applications.
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- Award ID(s):
- 1750155
- PAR ID:
- 10094533
- Date Published:
- Journal Name:
- 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI)
- Page Range / eLocation ID:
- 177 to 188
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IoTDI.2018.00026
