A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Dynamic Thermal and Power Management: From Computers to Buildings

Thermal and power management have become increasingly important for both computing and physical systems. Computing systems from real-time embedded systems to data centers require effective thermal and power management to prevent overheating and save energy. In the mean time, as a major consumer of energy buildings face challenges to reduce the energy consumption for air conditioning while maintaining comfort of occupants. In this dissertation we investigate dynamic thermal and power management for computer systems and buildings. (1) We present thermal control under utilization bound (TCUB), a novel control-theoretic thermal management algorithm designed for single core real-time embedded systems. A salient feature of TCUB is to maintain both desired processor temperature and real-time performance. (2) To address unique challenges posed by multicore processors, we develop the real-time multicore thermal control (RT-MTC) algorithm. RT-MTC employs a feedback control loop to enforce the desired temperature and CPU utilization of the multicore platform via dynamic frequency and voltage scaling. (3) We research dynamic thermal management for real-time services running on server clusters. We develop the control-theoretic thermal balancing (CTB) to dynamically balance temperature of servers via distributing clients\u27 service requests to servers. Next, (4) we propose CloudPowerCap, a power cap management system for virtualized cloud computing infrastructure. The novelty of CloudPowerCap lies in an integrated approach to coordinate power budget management and resource management in a cloud computing environment. Finally we expand our research to physical environment by exploring several fundamental problems of thermal and power management on buildings. We analyze spatial and temporal data acquired from an real-world auditorium instrumented by a multi-modal sensor network. We propose a data mining technique to determine the appropriate number and location of temperature sensors for estimating the spatiotemporal temperature distribution of the auditorium. Furthermore, we explore the potential energy savings that can be achieved through occupancy-based HVAC scheduling based on real occupancy data of the auditorium

Thermal aware task assignment for multicore processors using genetic algorithm

Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors

Power-aware scheduling with effective task migration for real-time multicore embedded systems

A major design issue in embedded systems is reducing the power consumption because batteries have a limited energy budget. For this purpose, several techniques such as dynamic voltage and frequency scaling (DVFS) or task migration are being used. DVFS allows reducing power by selecting the optimal voltage supply, whereas task migration achieves this effect by balancing the workload among cores. This paper focuses on power-aware scheduling allowing task migration to reduce energy consumption in multicore embedded systems implementing DVFS capabilities. To address energy savings, the devised schedulers follow two main rules: migrations are allowed at specific points of time and only one task is allowed to migrate each time. Two algorithms have been proposed working under real-time constraints. The simpler algorithm, namely, single option migration (SOM) only checks just one target core before performing a migration. In contrast, the multiple option migration (MOM) searches the optimal target core. In general, the MOM algorithm achieves better energy savings than the SOM algorithm, although differences are wider for a reduced number of cores and frequency/voltage levels. Moreover, the MOM algorithm reduces energy consumption as much as 40% over the worst fit algorithm.This work was supported by the Spanish MICINN, Consolider Programme and Plan E funds, as well as European Commission FEDER funds, under Grants CSD2006-00046 and TIN2009-14475-C04-01.March Cabrelles, JL.; Sahuquillo Borrás, J.; Petit Martí, SV.; Hassan Mohamed, H.; Duato Marín, JF. (2013). Power-aware scheduling with effective task migration for real-time multicore embedded systems. Concurrency and Computation: Practice and Experience. 25(14):1987-2001. doi:10.1002/cpe.2899S198720012514Euiseong Seo, Jinkyu Jeong, Seonyeong Park, & Joonwon Lee. (2008). Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors. IEEE Transactions on Parallel and Distributed Systems, 19(11), 1540-1552. doi:10.1109/tpds.2008.104March, J. L., Sahuquillo, J., Hassan, H., Petit, S., & Duato, J. (2011). A New Energy-Aware Dynamic Task Set Partitioning Algorithm for Soft and Hard Embedded Real-Time Systems. The Computer Journal, 54(8), 1282-1294. doi:10.1093/comjnl/bxr008AlEnawy, T. A., & Aydin, H. (s. f.). Energy-Aware Task Allocation for Rate Monotonic Scheduling. 11th IEEE Real Time and Embedded Technology and Applications Symposium. doi:10.1109/rtas.2005.20Intel atom processor microarchitecture www.intel.com/Marvell ARMADA TM 628 Marvell Semiconductor, Inc. Santa Clara, CA, USA http://www.marvell.com/company/press_kit/assets/Marvell_ARMADA_628_Release_FINAL3.pdfMcNairy, C., & Bhatia, R. (2005). Montecito: A Dual-Core, Dual-Thread Itanium Processor. IEEE Micro, 25(2), 10-20. doi:10.1109/mm.2005.34Kalla, R., Sinharoy, B., & Tendler, J. M. (2004). IBM power5 chip: a dual-core multithreaded processor. IEEE Micro, 24(2), 40-47. doi:10.1109/mm.2004.1289290Shah A Arm plans to add multithreading to chip design 2010 http://www.itworld.com/hardware/122383/arm-plans-add-multithreading-chip-designSchranzhofer, A., Chen, J.-J., & Thiele, L. (2010). Dynamic Power-Aware Mapping of Applications onto Heterogeneous MPSoC Platforms. IEEE Transactions on Industrial Informatics, 6(4), 692-707. doi:10.1109/tii.2010.2062192Cazorla, F. J., Knijnenburg, P. M. W., Sakellariou, R., Fernandez, E., Ramirez, A., & Valero, M. (2006). Predictable performance in SMT processors: synergy between the OS and SMTs. IEEE Transactions on Computers, 55(7), 785-799. doi:10.1109/tc.2006.108Fisher, N., & Baruah, S. (2008). The feasibility of general task systems with precedence constraints on multiprocessor platforms. Real-Time Systems, 41(1), 1-26. doi:10.1007/s11241-008-9054-5Buttazzo, G., Bini, E., & Yifan Wu. (2011). Partitioning Real-Time Applications Over Multicore Reservations. IEEE Transactions on Industrial Informatics, 7(2), 302-315. doi:10.1109/tii.2011.2123902Intel Pentium M processor datasheet INTEL Corp. Santa Clara, CA, USA 2004 http://download.intel.com/support/processors/mobile/pm/sb/25261203.pdfChaparro, P., Gonzáles, J., Magklis, G., Cai, Q., & González, A. (2007). Understanding the Thermal Implications of Multi-Core Architectures. IEEE Transactions on Parallel and Distributed Systems, 18(8), 1055-1065. doi:10.1109/tpds.2007.1092WCET analysis project. WCET benchmark programs 2006 http://www.mrtc.mdh.se/projects/wcet

A dynamic power-aware partitioner with task migration for multicore embedded systems

Nowadays, a key design issue in embedded systems is how to reduce the power consumption, since batteries have a limited energy budget. For this purpose, several techniques such as Dynamic Voltage Scaling (DVS) or task migration can be used. DVS allows reducing power by selecting the optimal voltage supply, while task migration achieves this effect by balancing the workload among cores. This paper first analyzes the impact on energy due to task migration in multicore embedded systems with DVS capability and using the well-known Worst Fit (WF) partitioning heuristic. To reduce overhead, migrations are only performed at the time that a task arrives to and/or leaves the system and, in such a case, only one migration is allowed. The huge potential on energy saving due to task migration, leads us to propose a new dynamic partitioner, namely DP, that migrates tasks in a more efficient way than typical partitioners. Unlike WF, the proposed algorithm examines which is the optimal target core before allowing a migration. Experimental results show that DP can improve energy consumption in a factor up to 2.74 over the typical WF algorithm. © 2011 Springer-Verlag.This work was supported by Spanish CICYT under Grant TIN2009-14475-C04-01, and by Consolider-Ingenio under Grant CSD2006-00046.March Cabrelles, JL.; Sahuquillo Borrás, J.; Petit Martí, SV.; Hassan Mohamed, H.; Duato Marín, JF. (2011). A dynamic power-aware partitioner with task migration for multicore embedded systems. En Euro-Par 2011 Parallel Processing. Springer Verlag (Germany). 2011(6852):218-229. https://doi.org/10.1007/978-3-642-23400-2_21S21822920116852AlEnawy, T.A., Aydin, H.: Energy-Aware Task Allocation for Rate Monotonic Scheduling. In: Proceedings of the 11th Real Time on Embedded Technology and Applications Symposium, March 7-10, pp. 213–223. 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IEEE Computer Society, Atlanta (2007)Cazorla, F., Knijnenburg, P., Sakellariou, R., Fernández, E., Ramirez, A., Valero, M.: Predictable Performance in SMT Processors: Synergy between the OS and SMTs. IEEE Transactions on Computers 55(7), 785–799 (2006)Donald, J., Martonosi, M.: Techniques for Multicore Thermal Management: Classification and New Exploration. In: Proceedings of the 33rd Annual International Symposium on Computer Architecture, June 17-21, pp. 78–88. IEEE Computer Society, Boston (2006)El-Haj-Mahmoud, A., AL-Zawawi, A., Anantaraman, A., Rotenberg, E.: Virtual Multiprocessor: An Analyzable, High-Performance Architecture for Real-Time Computing. In: Proceedings of the International Conference on Compilers, Architectures and Synthesis for Embedded Systems, September 24-27, pp. 213–224. ACM Press, San Francisco (2005)Hung, C., Chen, J., Kuo, T.: Energy-Efficient Real-Time Task Scheduling for a DVS System with a Non-DVS Processing Element. In: Proceedings of the 27th Real-Time Systems Symposium, December 5-8, pp. 303–312. IEEE Computer Society, Rio de Janeiro (2006)Kalla, R., Sinharoy, B., Tendler, J.M.: IBM Power5 Chip: A Dual-Core Multithreaded Processor. IEEE Micro 24(2), 40–47 (2004)Kato, S., Yamasaki, N.: Global EDF-based Scheduling with Efficient Priority Promotion. In: Proceedings of the 14th International Conference on Embedded and Real-Time Computing Systems and Applications, August 25-27, pp. 197–206. IEEE Computer Society, Kaohisung (2008)Malardalen Real-Time Research Center, Vasteras, Sweden: WCET Analysis Project. WCET Benchmark Programs (2006), [Online], http://www.mrtc.mdh.se/projects/wcet/March, J., Sahuquillo, J., Hassan, H., Petit, S., Duato, J.: A New Energy-Aware Dynamic Task Set Partitioning Algorithm for Soft and Hard Embedded Real-Time Systems. To be published on The Computer Journal (2011)McNairy, C., Bhatia, R.: Montecito: A Dual-Core, Dual-Thread Itanium Processor. 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Energy-efficient thermal-aware multiprocessor scheduling for real-time tasks using TCPNs

We present an energy-effcient thermal-aware real-time global scheduler for a set of hard real-time (HRT) tasks running on a multiprocessor system. This global scheduler fulfills the thermal and temporal constraints by handling two independent variables, the task allocation time and the selection of clock frequency. To achieve its goal, the proposed scheduler is split into two stages. An off-line stage, based on a deadline partitioning scheme, computes the cycles that the HRT tasks must run per deadline interval at the minimum clock frequency to save energy while honoring the temporal and thermal constraints, and computes the maximum frequency at which the system can run below the maximum temperature. Then, an on-line, event-driven stage performs global task allocation applying a Fixed-Priority Zero-Laxity policy, reducing the overhead of quantum-based or interval-based global schedulers. The on-line stage embodies an adaptive scheduler that accepts or rejects soft RT aperiodic tasks throttling CPU frequency to the upper lowest available one to minimize power consumption while meeting time and thermal constraints. This approach leverages the best of two worlds: the off-line stage computes an ideal discrete HRT multiprocessor schedule, while the on-line stage manage soft real-time aperiodic tasks with minimum power consumption and maximum CPU utilization

Task Activity Vectors: A Novel Metric for Temperature-Aware and Energy-Efficient Scheduling

This thesis introduces the abstraction of the task activity vector to characterize applications by the processor resources they utilize. Based on activity vectors, the thesis introduces scheduling policies for improving the temperature distribution on the processor chip and for increasing energy efficiency by reducing the contention for shared resources of multicore and multithreaded processors