Fuzzy logic based energy and throughput aware design space exploration for MPSoCs

Multicore architectures were introduced to mitigate the issue of increase in power dissipation with clock frequency. Introduction of deeper pipelines, speculative threading etc. for single core systems were not able to bring much increase in performance as compared to their associated power overhead. However for multicore architectures performance scaling with number of cores has always been a challenge. The Amdahl's law shows that the theoretical maximum speedup of a multicore architecture is not even close to the multiple of number of cores. With less amount of code in parallel having more number of cores for an application might just contribute in greater power dissipation instead of bringing some performance advantage. Therefore there is a need of an adaptive multicore architecture that can be tailored for the application in use for higher energy efficiency. In this paper a fuzzy logic based design space exploration technique is presented that is targeted to optimize a multicore architecture according to the workload requirements in order to achieve optimum balance between throughput and energy of the system

Resource management for extreme scale high performance computing systems in the presence of failures

2018 Summer.Includes bibliographical references.High performance computing (HPC) systems, such as data centers and supercomputers, coordinate the execution of large-scale computation of applications over tens or hundreds of thousands of multicore processors. Unfortunately, as the size of HPC systems continues to grow towards exascale complexities, these systems experience an exponential growth in the number of failures occurring in the system. These failures reduce performance and increase energy use, reducing the efficiency and effectiveness of emerging extreme-scale HPC systems. Applications executing in parallel on individual multicore processors also suffer from decreased performance and increased energy use as a result of applications being forced to share resources, in particular, the contention from multiple application threads sharing the last-level cache causes performance degradation. These challenges make it increasingly important to characterize and optimize the performance and behavior of applications that execute in these systems. To address these challenges, in this dissertation we propose a framework for intelligently characterizing and managing extreme-scale HPC system resources. We devise various techniques to mitigate the negative effects of failures and resource contention in HPC systems. In particular, we develop new HPC resource management techniques for intelligently utilizing system resources through the (a) optimal scheduling of applications to HPC nodes and (b) the optimal configuration of fault resilience protocols. These resource management techniques employ information obtained from historical analysis as well as theoretical and machine learning methods for predictions. We use these data to characterize system performance, energy use, and application behavior when operating under the uncertainty of performance degradation from both system failures and resource contention. We investigate how to better characterize and model the negative effects from system failures as well as application co-location on large-scale HPC computing systems. Our analysis of application and system behavior also investigates: the interrelated effects of network usage of applications and fault resilience protocols; checkpoint interval selection and its sensitivity to system parameters for various checkpoint-based fault resilience protocols; and performance comparisons of various promising strategies for fault resilience in exascale-sized systems

Resilient IEEE802.15.4MAC Protocol for Multi-Hop Mesh Wireless Sensor Network

The success of a modern power grid system is inevitably based on the integration of a smart data exchange amid several devices in power production, transportation, dispatching and loads. For large coverage data exhange, a distributed multi-hop mesh is structured from low voltage distribution boards to the substations. Thus, being cheap, less power intake, easy set-up and operating in a free licensed spectrum, ZigBee/IEEE802.15.4 makes the most suitable wireless protocol for communicating in power grid systems. Nevertheless, IEEE802.15.4MAC protocol lacks a mechanism to enable a multi-hop mesh network with efficient energy and quality of service (QoS). Hence, in this paper, a Multi-Hop Mesh IEEE802.15.4MAC protocol is designed for a large coverage data exchange. This developed model provides a resilient network with energy efficiency and QoS. Hence, the IEEE802.15.4 super_frame_standard_structure is modified by swapping the contention_free period (CFP) and contention_access_period (CAP) for time sensitive applications. For network resilience, a Reserved_Broadcast Duration_Slot (RB_DS) is introduced in the active super_frame standard_structure as beacon_offset reference time computation. Finally, for the network performance analysis, the developed Markov chain_Model with retry and saturated traffic regime without feedback is run on NS-2 simulator. Here, the hidden terminal problem is not considered since it is assumed that all nodes can "hear" each other. The simulation results are encouraging as the developed IEEE802.15.4MAC protocol is capable of improving the time delivery delay up to 35.7%