Intelligent Management of Mobile Systems through Computational Self-Awareness

Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this chapter, we explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads

Addressing Manufacturing Challenges in NoC-based ULSI Designs

Hernández Luz, C. (2012). Addressing Manufacturing Challenges in NoC-based ULSI Designs [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1669

Dynamic Voltage and Frequency Scaling Techniques for Chip Multiprocessor Designs

Due to chip power density limitations as well as the recent breakdown of Dennard's Scalingover the past decade, performance growth in microprocessor design has largely been driven by core scaling. These trends have led to Chip Multi- Processor(CMP) designs, currently with tens of cores, and expected to grow to the thousands in the pursuit of exascale computing. The more complicated CMP design is more leading power consumption relatively in computer architecture. The increased power consumption generates thermal issues, and so performance degradation. Therefore, it is certain that power efficient algorithm in CMP and main memory are essential. For the power efficiency, we focus on dynamic voltage/frequency scaling (DVFS) techniques for CMP and main memory. In the first work, we focus on the "uncore", consisting of an on-chip communication fabric and shared LLC in CMP. The uncore now occupies as much as 30% of the overall die area, which is not negligible in CMP design, but has rarely researched. We find there are predictable patterns in uncore utility which point towards the potential of a proactive approach to uncore power management. In this work, we utilize artificial intelligence principles to proactively leverage uncore utility pattern prediction via an Artificial Neural Network (ANN). Even though the uncore takes non-negligible portion of CMP power consumption, processor cores still exist as major power consumers. For core DVFS, We explore a novel approach with the potential to achieve synergistic energy-savings and performance gain in chip multiprocessors (CMPs). In current designs, performance must typically be traded-off to achieve energy savings or, conversely, performance gains come with significant energy overhead. Resources shared by processor cores, such as on-chip interconnect and shared memory, play an increasingly critical role in determining the overall CMP performance. Our key observation is that per-core DVFS can be used as a client regulation mechanism for the shared resources. Based on this observation, we propose a new DVFS technique inspired by TCP Vegas, a congestion control protocol from the IP-networking domain. In addition to uncore in CMP, main memory is also critical shared resource in total system. As uncore is critical resource for CMP performance while occupying critical portion of total CMP energy consumed, main memory is also critical for total performance and accounts for large fraction of total energy consumption. Most conventional approaches focused on utilization of cores and memory only for memory power management. We found, however, the uncore plays an important role of total system performance and its utilization must be considered as well for memory power management. From the observation, we propose shared resource utilization aware power management technique for main memory. Our technique chooses low V/F level of memory for some congested case in uncore, and so derives negligible performance degradation while saving more energy by the low V/F level. We also proposed coordination policies to avoid oscillation issues among individual DVFS techniques (i.e. over energy saving or over performance increment). Full system simulations on PARSEC benchmarks show that our coordinated technique reduces total energy dissipation by over 47% across all benchmarks with less than 2.3% performance degradation

Software-based and regionally-oriented traffic management in Networks-on-Chip

Since the introduction of chip-multiprocessor systems, the number of integrated cores has been steady growing and workload applications have been adapted to exploit the increasing parallelism. This changed the importance of efficient on-chip communication significantly and the infrastructure has to keep step with these new requirements. The work at hand makes significant contributions to the state-of-the-art of the latest generation of such solutions, called Networks-on-Chip, to improve the performance, reliability, and flexible management of these on-chip infrastructures

Integrated thermodynamic and control modeling of an air - to - water heat pump for estimating energy - saving potential and flexibility in the building sector

Reversible heat pumps are increasingly adopted for meeting the demand for space heating and cooling in buildings. These technologies will play a key role not only in the decarbonization of space air conditioning but also in the development of 100% renewable energy systems. However, to assess the achievable benefits through the adoption of these technologies in novel applications, reliable models are needed, capable of simulating both their steady-state operation and dynamic response at different conditions in terms of heating loads, outdoor temperatures, and so on. The operation of heat pumps is often investigated by highly simplified models, using performance data drawn from catalogs and paying scarce attention to the critical influence of controllers. In this respect, this paper proposed an integrated thermodynamic and control modeling for a reversible air-to-water heat pump. The study considered a heat pump alternatively equipped with variable-speed compressors and constant-speed compressors with sequential control. The developed modeling was then used to investigate the operation of an air-to-water heat pump serving an office building in Italy. Results show that the model provided insights into the transient operation of variable-speed heat pumps (e.g., the settling time). Regarding constant-speed heat pumps, the model provided hints of interest to the control engineer to prevent, in the examined case study, the risk of quick compressors cycling on low-load heating days or when low-temperature heating devices are supplied. Finally, using a control strategy based on a heating curve for the variable-speed heat pump, results show the potential for a sensible increase in the average coefficient of performance, from 17% up to 50%

Adaptive Resource Management for Mobile Multiprocessors through Computational Self-Awareness

Runtime resource management for many-core systems is increasingly complex.The complexity can be due to diverse workload characteristics with conflicting demands or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner.To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization).In this thesis, I explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system will reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads