Collaborative Research: PPoSS: Planning: Scaling Secure Serverless Computing on Heterogeneous Datacenters

协作研究：PPoSS：规划：在异构数据中心上扩展安全无服务器计算

• 批准号: 2028850
• 负责人: Xu Liu
• 金额: $ 7.66万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028850&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Scaling

Cloud computing has been a dominant computing paradigm that enables many important computing capabilities including large-scale (big) data processing, artificial intelligence, and scientific discoveries. A recent evolution of cloud computing includes the move to serverless computing, which simplifies the deployment of computation while enabling better scaling and higher resource utilization. Meanwhile, datacenters, the backbone of cloud computing, increasingly include heterogeneous compute and memory resources. The move toward serverless computing and heterogeneous architecture of datacenters produces a gap that unless addressed, results in inefficient use of resources. The project seeks to address this gap in order to enable new applications and new functionalities to be provided in the cloud, at lower cost and higher security, providing platforms for the advancement of science, engineering, and commerce. Future datacenters will consist of heterogeneous compute and memory. Applications in the cloud are increasingly varied in their requirements, such as degree and granularity of parallelism; memory latency, capacity, and bandwidth requirements; and security and privacy requirements. This project investigates serverless computing as a promising programming model for heterogeneous platforms. Serverless platforms decouple system management from application execution: applications provide functions that manipulate data, and leave it to the platform to determine when the function should run, with what input data, and on what physical machine. Current platforms, such as AWS Lambda, Google Compute Functions or Azure Functions do not fully implement this vision, as they do not expose heterogeneous resources nor manage all resources automatically. This project explores novel abstractions for compute that extend serverless functions to better leverage unique hardware characteristics, and for memory to allow more automated leveraging of workload characteristics such as locality and compute intensity.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

云计算已经成为一种占主导地位的计算范例，其实现了许多重要的计算能力，包括大规模（大）数据处理、人工智能和科学发现。云计算的最新发展包括转向无服务器计算，这简化了计算的部署，同时实现了更好的扩展和更高的资源利用率。与此同时，云计算的骨干网络中心越来越多地包括异构计算和内存资源。向无服务器计算和异构架构的转变产生了一个差距，除非得到解决，否则会导致资源的低效使用。该项目旨在解决这一差距，以便能够以更低的成本和更高的安全性在云中提供新的应用程序和新的功能，为科学，工程和商业的发展提供平台。未来的计算中心将由异构计算和内存组成。云中的应用程序的需求越来越多样化，例如并行度和粒度;内存延迟，容量和带宽要求;以及安全和隐私要求。该项目研究无服务器计算作为异构平台的一种有前途的编程模型。无服务器平台将系统管理与应用程序执行分离：应用程序提供操作数据的函数，并将其留给平台来确定函数何时运行，使用什么输入数据以及在什么物理机器上运行。 目前的平台，如AWS Lambda、Google Compute Functions或Azure Functions，并没有完全实现这一愿景，因为它们没有公开异构资源，也没有自动管理所有资源。该项目探索了新的计算抽象，扩展了无服务器功能，以更好地利用独特的硬件特性，并为内存提供了更自动化的工作负载特性，如局部性和计算强度。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响评审标准进行评估，被认为值得支持。

项目成果

Understanding and bridging the gaps in current GNN performance optimizations

• DOI: 10.1145/3437801.3441585
• 发表时间: 2021-02
• 期刊: Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• 作者: Kezhao Huang;Jidong Zhai;Zhen Zheng;Youngmin Yi;Xipeng Shen

Recurrent Neural Networks Meet Context-Free Grammar: Two Birds with One Stone

递归神经网络遇到上下文无关语法：一石二鸟

• DOI: 10.1109/icdm51629.2021.00125
• 发表时间: 2021
• 期刊: IEEE International Conference on Data Mining
• 作者: Guan, Hui;Chaudhary, Umang;Xu, Yuanchao;Ning, Lin;Zhang, Lijun;Shen, Xipeng
• 通讯作者: Shen, Xipeng

G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression

• DOI: 10.1109/icde51399.2021.00148
• 发表时间: 2021-04
• 期刊: 2021 IEEE 37th International Conference on Data Engineering (ICDE)
• 作者: Feng Zhang;Zaifeng Pan;Yanliang Zhou;Jidong Zhai;Xipeng Shen;O. Mutlu;Xiaoyong Du

Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU

• DOI: 10.1109/ipdps53621.2022.00056
• 发表时间: 2022-01
• 期刊: 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Jou-An Chen;Hsin-Hsuan Sung;Nathan R. Tallent;K. Barker;Xipeng Shen;Ang Li

Deep NLP-Based Co-Evolvement for Synthesizing Code Analysis from Natural Language

基于深度 NLP 的协同进化，用于从自然语言综合代码分析

• DOI: 10.1145/3446804.3446852
• 发表时间: 2021
• 期刊: Compiler Construction
• 作者: Nan, Zifan;Guan, Hui;Shen, Xipeng;Liao, Chunhua
• 通讯作者: Liao, Chunhua