Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Dynamic Energy Management for Chip Multi-processors under Performance Constraints

We introduce a novel algorithm for dynamic energy management (DEM) under performance constraints in chip multi-processors (CMPs). Using the novel concept of delayed instructions count, performance loss estimations are calculated at the end of each control period for each core. In addition, a Kalman filtering based approach is employed to predict workload in the next control period for which voltage-frequency pairs must be selected. This selection is done with a novel dynamic voltage and frequency scaling (DVFS) algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Using our customized Sniper based CMP system simulation framework, we demonstrate the effectiveness of the proposed algorithm for a variety of benchmarks for 16 core and 64 core network-on-chip based CMP architectures. Simulation results show consistent energy savings across the board. We present our work as an investigation of the tradeoff between the achievable energy reduction via DVFS when predictions are done using the effective Kalman filter for different performance penalty thresholds

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

Performance and power optimizations in chip multiprocessors for throughput-aware computation

The so-called "power (or power density) wall" has caused core frequency (and single-thread performance) to slow down, giving rise to the era of multi-core/multi-thread processors. For example, the IBM POWER4 processor, released in 2001, incorporated two single-thread cores into the same chip. In 2010, IBM released the POWER7 processor with eight 4-thread cores in the same chip, for a total capacity of 32 execution contexts. The ever increasing number of cores and threads gives rise to new opportunities and challenges for software and hardware architects. At software level, applications can benefit from the abundant number of execution contexts to boost throughput. But this challenges programmers to create highly-parallel applications and operating systems capable of scheduling them correctly. At hardware level, the increasing core and thread count puts pressure on the memory interface, because memory bandwidth grows at a slower pace ---phenomenon known as the "bandwidth (or memory) wall". In addition to memory bandwidth issues, chip power consumption rises due to manufacturers' difficulty to lower operating voltages sufficiently every processor generation. This thesis presents innovations to improve bandwidth and power consumption in chip multiprocessors (CMPs) for throughput-aware computation: a bandwidth-optimized last-level cache (LLC), a bandwidth-optimized vector register file, and a power/performance-aware thread placement heuristic. In contrast to state-of-the-art LLC designs, our organization avoids data replication and, hence, does not require keeping data coherent. Instead, the address space is statically distributed all over the LLC (in a fine-grained interleaving fashion). The absence of data replication increases the cache effective capacity, which results in better hit rates and higher bandwidth compared to a coherent LLC. We use double buffering to hide the extra access latency due to the lack of data replication. The proposed vector register file is composed of thousands of registers and organized as an aggregation of banks. We leverage such organization to attach small special-function "local computation elements" (LCEs) to each bank. This approach ---referred to as the "processor-in-regfile" (PIR) strategy--- overcomes the limited number of register file ports. Because each LCE is a SIMD computation element and all of them can proceed concurrently, the PIR strategy constitutes a highly-parallel super-wide-SIMD device (ideal for throughput-aware computation). Finally, we present a heuristic to reduce chip power consumption by dynamically placing software (application) threads across hardware (physical) threads. The heuristic gathers chip-level power and performance information at runtime to infer characteristics of the applications being executed. For example, if an application's threads share data, the heuristic may decide to place them in fewer cores to favor inter-thread data sharing and communication. In such case, the number of active cores decreases, which is a good opportunity to switch off the unused cores to save power. It is increasingly harder to find bulletproof (micro-)architectural solutions for the bandwidth and power scalability limitations in CMPs. Consequently, we think that architects should attack those problems from different flanks simultaneously, with complementary innovations. This thesis contributes with a battery of solutions to alleviate those problems in the context of throughput-aware computation: 1) proposing a bandwidth-optimized LLC; 2) proposing a bandwidth-optimized register file organization; and 3) proposing a simple technique to improve power-performance efficiency.El excesivo consumo de potencia de los procesadores actuales ha desacelerado el incremento en la frecuencia operativa de los mismos para dar lugar a la era de los procesadores con múltiples núcleos y múltiples hilos de ejecución. Por ejemplo, el procesador POWER7 de IBM, lanzado al mercado en 2010, incorpora ocho núcleos en el mismo chip, con cuatro hilos de ejecución por núcleo. Esto da lugar a nuevas oportunidades y desafíos para los arquitectos de software y hardware. A nivel de software, las aplicaciones pueden beneficiarse del abundante número de núcleos e hilos de ejecución para aumentar el rendimiento. Pero esto obliga a los programadores a crear aplicaciones altamente paralelas y sistemas operativos capaces de planificar correctamente la ejecución de las mismas. A nivel de hardware, el creciente número de núcleos e hilos de ejecución ejerce presión sobre la interfaz de memoria, ya que el ancho de banda de memoria crece a un ritmo más lento. Además de los problemas de ancho de banda de memoria, el consumo de energía del chip se eleva debido a la dificultad de los fabricantes para reducir suficientemente los voltajes de operación entre generaciones de procesadores. Esta tesis presenta innovaciones para mejorar el ancho de banda y consumo de energía en procesadores multinúcleo en el ámbito de la computación orientada a rendimiento ("throughput-aware computation"): una memoria caché de último nivel ("last-level cache" o LLC) optimizada para ancho de banda, un banco de registros vectorial optimizado para ancho de banda, y una heurística para planificar la ejecución de aplicaciones paralelas orientada a mejorar la eficiencia del consumo de potencia y desempeño. En contraste con los diseños de LLC de última generación, nuestra organización evita la duplicación de datos y, por tanto, no requiere de técnicas de coherencia. El espacio de direcciones de memoria se distribuye estáticamente en la LLC con un entrelazado de grano fino. La ausencia de replicación de datos aumenta la capacidad efectiva de la memoria caché, lo que se traduce en mejores tasas de acierto y mayor ancho de banda en comparación con una LLC coherente. Utilizamos la técnica de "doble buffering" para ocultar la latencia adicional necesaria para acceder a datos remotos. El banco de registros vectorial propuesto se compone de miles de registros y se organiza como una agregación de bancos. Incorporamos a cada banco una pequeña unidad de cómputo de propósito especial ("local computation element" o LCE). Este enfoque ---que llamamos "computación en banco de registros"--- permite superar el número limitado de puertos en el banco de registros. Debido a que cada LCE es una unidad de cómputo con soporte SIMD ("single instruction, multiple data") y todas ellas pueden proceder de forma concurrente, la estrategia de "computación en banco de registros" constituye un dispositivo SIMD altamente paralelo. Por último, presentamos una heurística para planificar la ejecución de aplicaciones paralelas orientada a reducir el consumo de energía del chip, colocando dinámicamente los hilos de ejecución a nivel de software entre los hilos de ejecución a nivel de hardware. La heurística obtiene, en tiempo de ejecución, información de consumo de potencia y desempeño del chip para inferir las características de las aplicaciones. Por ejemplo, si los hilos de ejecución a nivel de software comparten datos significativamente, la heurística puede decidir colocarlos en un menor número de núcleos para favorecer el intercambio de datos entre ellos. En tal caso, los núcleos no utilizados se pueden apagar para ahorrar energía. Cada vez es más difícil encontrar soluciones de arquitectura "a prueba de balas" para resolver las limitaciones de escalabilidad de los procesadores actuales. En consecuencia, creemos que los arquitectos deben atacar dichos problemas desde diferentes flancos simultáneamente, con innovaciones complementarias

A survey of system level power management schemes in the dark-silicon era for many-core architectures

Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, with many-core integration set to be heading towards its thousands, power consumption and temperature increases per time, meaning the number of active nodes must be reduced drastically. Therefore, optimized techniques are demanded for continuous advancement in technology. Existing efforts try to overcome this challenge by activating nodes from different parts of the chip at the expense of communication latency. Other efforts on the other hand employ run-time power management techniques to manage the power performance of the cores trading-off performance for power. We found out that, for a significant amount of power to saved and high temperature to be avoided, focus should be on reducing the power consumption of all the on-chip components. Especially, the memory hierarchy and the interconnect. Power consumption can be minimized by, reducing the size of high leakage power dissipating elements, turning-off idle resources and integrating power saving materials

Run-time Resource Management in CMPs Handling Multiple Aging Mechanisms

Abstract—Run-time resource management is fundamental for efficient execution of workloads on Chip Multiprocessors. Application- and system-level requirements (e.g. on performance vs. power vs. lifetime reliability) are generally conflicting each other, and any decision on resource assignment, such as core allocation or frequency tuning, may positively affect some of them while penalizing some others. Resource assignment decisions can be perceived in few instants of time on performance and power consumption, but not on lifetime reliability. In fact, this latter changes very slowly based on the accumulation of effects of various decisions over a long time horizon. Moreover, aging mechanisms are various and have different causes; most of them, such as Electromigration (EM), are subject to temperature levels, while Thermal Cycling (TC) is caused mainly by temperature variations (both amplitude and frequency). Mitigating only EM may negatively affect TC and vice versa. We propose a resource orchestration strategy to balance the performance and power consumption constraints in the short-term and EM and TC aging in the long-term. Experimental results show that the proposed approach improves the average Mean Time To Failure at least by 17% and 20% w.r.t. EM and TC, respectively, while providing same performance level of the nominal counterpart and guaranteeing the power budget

Dynamic Lifetime Reliability and Energy Management for Network-on-Chip based Chip Multiprocessors

In this dissertation, we study dynamic reliability management (DRM) and dynamic energy management (DEM) techniques for network-on-chip (NoC) based chip multiprocessors (CMPs). In the first part, the proposed DRM algorithm takes both the computational and the communication components of the CMP into consideration and combines thread migration and dynamic voltage and frequency scaling (DVFS) as the two primary techniques to change the CMP operation. The goal is to increase the lifetime reliability of the overall system to the desired target with minimal performance degradation. The simulation results on a variety of benchmarks on 16 and 64 core NoC based CMP architectures demonstrate that lifetime reliability can be improved by 100% for an average performance penalty of 7.7% and 8.7% for the two CMP architectures. In the second part of this dissertation, we first propose novel algorithms that employ Kalman filtering and long short term memory (LSTM) for workload prediction. These predictions are then used as the basis on which voltage/frequency (V/F) pairs are selected for each core by an effective dynamic voltage and frequency scaling algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Secondly, we investigate the use of deep neural network (DNN) models for energy optimization under performance constraints in CMPs. The proposed algorithm is implemented in three phases. The first phase collects the training data by employing Kalman filtering for workload prediction and an efficient heuristic algorithm based on DVFS. The second phase represents the training process of the DNN model and in the last phase, the DNN model is used to directly identify V/F pairs that can achieve lower energy consumption without performance degradation beyond the acceptable threshold set by the user. Simulation results on 16 and 64 core NoC based architectures demonstrate that the proposed approach can achieve up to 55% energy reduction for 10% performance degradation constraints. Simulation experiments compare the proposed algorithm against existing approaches based on reinforcement learning and Kalman filtering and show that the proposed DNN technique provides average improvements in energy-delay-product (EDP) of 6.3% and 6% for the 16 core architecture and of 7.4% and 5.5% for the 64 core architecture

Aggressive and reliable high-performance architectures - techniques for thermal control, energy efficiency, and performance augmentation

As more and more transistors fit in a single chip, consumers of the electronics industry continue to expect decline in cost-per-function. Advancements in process technology offer steady improvements in system performance. The improvements manifest themselves as shrinking area, faster circuits and improved battery life. However, this migration toward sub-micro/nano-meter technologies presents a new set of challenges as the system becomes extremely sensitive to any voltage, temperature or process variations. One approach to immunize the system from the adverse effects of these variations is to add sufficient safety margins to the operating clock frequency of the system. Clearly, this approach is overly conservative because these worst case scenarios rarely occur. But, process technology in nanoscale era has already hit the power and frequency walls. Regardless of any of these challenges, the present processors not only need to run faster, but also cooler and use lesser energy. At a juncture where there is no further improvement in clock frequency is possible, data dependent latching through Timing Speculation (TS) provides a silver lining. Timing speculation is a widely known method for realizing better-than-worst-case systems. TS is aggressive in nature, where the mechanism is to dynamically tune the system frequency beyond the worst-case limits obtained from application characteristics to enhance the performance of system-on-chips (SoCs). However, such aggressive tuning has adverse consequences that need to be overcome. Power dissipation, on-chip temperature and reliability are key issues that cannot be ignored. A carefully designed power management technique combined with a reliable, controlled, aggressive clocking not only attempts to constrain power dissipation within a limit, but also improves performance whenever possible. In this dissertation, we present a novel power level switching mechanism by redefining the existing voltage-frequency pairs. We introduce an aggressive yet reliable framework for energy efficient thermal control. We were able to achieve up to 40% speed-up compared to a base scheme without overclocking. We compare our method against different schemes. We observe that up to 75% Energy-Delay squared product (ED2) savings relative to base architecture is possible. We showcase the loss of efficiency in present chip multiprocessor systems due to excess power supplied, and propose Utilization-aware Task Scheduling (UTS) - a power management scheme that increases energy efficiency of chip multiprocessors. Our experiments demonstrate that UTS along with aggressive timing speculation squeezes out maximum performance from the system without loss of efficiency, and breaching power & thermal constraints. From our evaluation we infer that UTS improves performance by up to 12% due to aggressive power level switching and over 50% in ED2 savings compared to traditional power management techniques. Aggressive clocking systems having TS as their central theme operate at a clock frequency range beyond specified safe limits, exploiting the data dependence on circuit critical paths. However, the margin for performance enhancement is restricted due to extreme difference between short paths and critical paths. In this thesis, we show that increasing the lengths of short paths of the circuit increases the margin of TS, leading to performance improvement in aggressively designed systems. We develop Min-arc algorithm to efficiently add delay buffers to selected short paths while keeping down the area penalty. We show that by using our algorithm, it is possible to increase the circuit contamination delay by up to 30% without affecting the propagation delay, with moderate area overhead. We also explore the possibility of increasing short path delays further by relaxing the constraint on propagation delay, and achieve even higher performance. Overall, we bring out the inter-relationship between power, temperature and reliability of aggressively clocked systems. Our main objective is to achieve maximal performance benefits and improved energy efficiency within thermal constraints by effectively combining dynamic frequency scaling, dynamic voltage scaling and reliable overclocking. We provide solutions to improve the existing power management in chip multiprocessors to dynamically maximize system utilization and satisfy the power constraints within safe thermal limits