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DTSTART;TZID=America/New_York:20221208T110000
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DTSTAMP:20260618T031003
CREATED:20221201T023045Z
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UID:6008-1670497200-1670500800@ece.northeastern.edu
SUMMARY:Danlin Jia's PhD Dissertation Defense
DESCRIPTION:“Towards Performance and Cost-efficiency for Data-intensive Applications in Distributed Data Processing Systems” \nAbstract: \nData-intensive science (DIS) has experienced a significant boom in the past decade. The emerging technologies of data-intensive services and infrastructures contribute to DIS’s development and raise challenges. An ecosystem has been constructed considering performance\, scalability\, sustainability\, and reliability to provide a high-quality service to DIS applications. The ecosystem consists of services exposed to users for application deployment and infrastructures to support data storage\, transfer\, and management from the system’s perspective. DIS applications share typical features\, such as memory and I/O intensity. Thus\, addressing the bottlenecks triggered by memory-intensive or I/O-intensive workloads in services and infrastructures is essential to improve the performance and cost-efficiency of the whole ecosystem. In this dissertation\, we investigate the characteristics of various DIS applications and design new resource allocation and scheduling schemes for the services and infrastructures in the DIS ecosystem. \nWe first investigate memory optimization in DIS ecosystems. In-memory data analytic frameworks are proposed to cache critical intermediate data in memory instead of in storage drives. Apache Spark is a commonly adopted in-memory data analytic framework with two memory managers\, Static and Unified. However\, the static memory manager lacks flexibility. In contrast\, the unified memory manager puts heavy pressure on the garbage collection of the Java Virtual Machine on which Spark resides. To address these issues\, we propose a new learning-based bidirectional usage-bounded memory allocation scheme to support dynamic memory allocation considering both memory demands and latency introduced by garbage collection. Distributed data-processing workloads in container-based virtualization take advantage of resource sharing\, fast delivery\, and excellent portability of containerization but also suffer from resource competition and performance interference. This inevitably induces performance degradation and significantly long latency\, even worse when over-provisioning. Motivated by this problem\, we design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors applications’ memory usage and cache characteristics and dynamically re-allocates memory resources. \nWe also propose I/O optimizations for DIS applications and infrastructures. Distributed Deep Learning (DDL) accelerates DNN training by distributing training workloads across multiple computation accelerators\, e.g.\, GPUs. Although a surge of research has been devoted to optimizing DDL training\, the impact of data loading on GPU usage and training performance has been relatively under-explored. When multiple DDL applications are deployed\, the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. In this dissertation\, we thus investigate the impact of data-loading on the global training throughput and design a resource allocator that uses the data-loading rate as a knob to reduce the GPU idleness. Finally\, designs and optimizations on disaggregated storage systems supported by cutting-edge storage and network techniques emerge dramatically. Disaggregated storage systems can scale resources independently and provide high-quality services for hyper-scale architectures. The traditional congestion control mechanism relieves congestion by limiting the data-sending rate of senders. However\, such a design scarifies the storage drive’s performance as data are generated but stalled on storage host nodes if network congestion happens. To solve this issue\, we design a storage-side rate control mechanism to mitigate network congestion while avoiding sacrificing I/O performance. \nCommittee: \nProf. Ningfang Mi (Advisor) \nProf. Xue Lin \nProf. David Kaeli
URL:https://ece.northeastern.edu/event/danlin-jias-phd-dissertation-defense/
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DTSTART;TZID=America/New_York:20221208T140000
DTEND;TZID=America/New_York:20221208T160000
DTSTAMP:20260618T031003
CREATED:20221202T201226Z
LAST-MODIFIED:20221202T201226Z
UID:6014-1670508000-1670515200@ece.northeastern.edu
SUMMARY:Chuangtang Wang's PhD Proposal Review
DESCRIPTION:“All-optical Control of Magnetization in Nanostructures” \nCommittee: \nProf. Yongmin Liu (Advisor) \nProf. Don Heiman \nProf. Nian X. Sun \nAbstract:\nThe switching of magnetization by a femtosecond laser within several picoseconds has recently gained substantial attention\, because it promises next-generation\, energy-efficient\, and high-rate data storage technology. One of the most intriguing demonstrations is the helicity-dependent switching (HD-AOS) of a ferromagnet\, in which the magnetization states can be deterministically written and erased using left- and right-circularly polarized light. However\, the challenge is to realize a single-pulse HD-AOS. Controlling the spin angular momentum transfer from light to magnetic materials in nanostructures is the key to advance this field.\nIn my thesis research work\, I will study the all-optical control of magnetization in different nanostructures\, aiming to better understand the underlying mechanisms of HD-AOD and accelerate the technology development. Firstly\, helicity-driven magnetization dynamics in heavy metal/ferromagnet Au(Pt)/Co bilayer by the optical spin transfer torque (OSTT) is experimentally explored. The wavelength-dependent measurement of OSTT reveals that the quantum efficiency of OSTT strongly depends on the interface electronic structure and pump energy. The Inverse Faraday effect (IFE)\, which is believed to be the driving mechanism of HD-AOS\, is subsequently investigated in an Au thin film. The dependence of IFE on photon energy implies that the orbital angular momentum contribution to IFE is dominated by the excitation of laser pulses. To the best of our knowledge\, it is the first demonstration of this phenomenon. Lastly\, I will discuss our recent results on plasmonics-enhanced all-optical control of magnetization. Light can be tightly confined in plasmonic structures\, which can potentially enable low-energy and high-density magnetic data storage.
URL:https://ece.northeastern.edu/event/chuangtang-wangs-phd-proposal-review/
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