Cloud, Edge, High-Performance Computing
Explore our Research Topics
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Xiaoning Ding |
Research Areas: Cloud/edge computing infrastructures, system designs for AI/DL, system software designs, database and data storage systems In the post Moore’s law era, computing resources are undergoing fundamental changes in many aspects (e.g., types, architectures, and features). In clouds and edges, computing resources are increasingly heterogeneous (e.g., many varieties of processors and accelerators), disaggregated (e.g., local and remote memory pooled together and made available through fast network) and dynamic (e.g., resource availability changing over time). These changes enable new computing paradigms and optimization opportunities, yet raise new challenges in resource management. System Software for Scalable Computation in the Cloud As computational resources continue to increase, we need ways to scale the performance of these computers by taking advantage of the extra resources. The objective is to guarantee that applications in the cloud can achieve higher performance when presented with more resources.
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Alex Gerbessiotis
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Research Areas: Architecture-independent parallel algorithm design and implementation Multi-Core and Many-Core Algorithm Design, Analysis and Implementation We study models of computation that abstract and capture parallelism in the presence of multiple memory hierarchies and cores. New approaches are needed to make multi-core architectures accessible to software designers in domains such as machine learning and big data. Abstracting the programming requirements of such architectures in a useful and usable manner is necessary to increase processing speed and improve memory performance. Parallel Computing Techniques in Sequential Serial Computing The norm in computing is to port sequential algorithms that work on one processor into multi-core or parallel algorithms intended for multiple cores and processors. Amdahl’s Law highlights the limitations of using multiple cores in programs with an inherently sequential component that is not amenable to parallelization. We address this by exploring the utilization of parallel computing techniques to speed up a sequential program by exploiting the multiple memory hierarchies present in contemporary microprocessors, even if its multi-core capabilities are left unexploited.
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Jing Li |
Research Areas: Real-time systems, parallel computing, cyber-physical systems and reinforcement learning for system design and optimization. Parallel Real-Time Systems Real-time systems need to provide timing guarantees for latency-critical applications in cyber- physical systems that interact with humans or the physical environment. Examples span autonomous vehicles, drones, avionic systems and robotics to structural health monitoring systems and hybrid simulation systems in earthquake engineering. However, as parallel machines become ubiquitous, we face challenges in designing real-time systems that can fully utilize the efficiencies of parallel and heterogeneous computing platforms. We are developing parallel real-time systems by exploiting the untapped efficiencies in the parallel platforms, drastically improving the system performance of a cyber-physical system. Scheduling for Interactive Cloud Services Delivering consistent interactive latencies, such as response delays, is the key performance metric of interactive cloud services that significantly impacts user experience. The need to guarantee low-service latency, while supporting increasing computational demands due to complex functions of the services, requires parallel scheduling infrastructure to effectively harness parallelism in the computation and efficiently utilize system resources. Our research designs, analyzes and implements scheduling strategies that are measurably good and practically efficient to provide various quality-of-service guarantees on cloud service latency.
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Andrew Sohn
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Research Areas: Adaptive computing infrastructure, autonomous migration of Linux virtual machines, maximizing cluster utilization Enabling High-Performance Cloud Computing The persistent uploading, downloading and processing of images, videos and files to and from the cloud can lead to inefficiency and delayed response times due to irregular computing demands. Our research focuses on live migration of virtual machines, as well as containers that will help alleviate the problem and improve cloud servers such as Amazon’s Elastic Compute Cloud and Microsoft Azure. It will also help meet the power and computing requirements of mobile and enterprise cloud applications. Scalable Parallel Graph Partitioning for Enabling Real-Time Analytics We are working on high performance computing for large-scale data, in particular largescale graph partitioning projects called HARP and S-HARP (scalable HARP) designed and implemented with collaborators at the NASA Ames Research Center and the Lawrence Berkeley National Laboratory. Large-scale graph partitioning is critical in real-time social network analytics and is particularly challenging when dealing with dynamic graphs that change over time, as there needs to be balance of partition quality and execution time. We established a framework for partitioning dynamic graphs for NASA applications and continue to improve the technology for real-time social network analytics on a cluster of personal computers.
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