Collaborative Research: SHF: Medium: Spatial Multi-Tenant Neural Acceleration for Next Generation Datacenters

合作研究：SHF：中：下一代数据中心的空间多租户神经加速

• 批准号: 2107244
• 负责人: Manya Ghobadi
• 金额: $ 80万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107244&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Spatial

Recent advances in Artificial Intelligence are transforming many aspects of human life such as e-commerce, medicine, transportation, and beyond. Datacenter networks are the foundation of modern online services. As the world is recovering from COVID-19, society is witnessing an increased reliance on online services and machine learning. This explosive growth has created an enormous demand for computation resources in datacenters. However, today's approaches are extremely costly and energy-inefficient. In fact, if the current systems continue to grow, datacenters will account for 14% of the total worldwide carbon emissions by 2040. This project aims to address this challenge using advanced resource-sharing techniques tailored for machine learning workloads. In particular, this award enables the network operators to maximize the utilization of network resources while achieving high quality of service experience for the users.This work sets out to explore the timely requirement of multi-tenancy for machine-learning acceleration through a new paradigm called dynamic architecture fission. There is a significant degree of underutilization when it comes to machine-learning accelerators that stem from the rigidity of architectures and their single-tenant nature. As such, there is an imminent need to rethink custom accelerator design and adoption in datacenters where cost-effective resource utilization replaces unnecessary resource cloning. Similar to the case of microprocessors, multi-tenant acceleration can open up a pathway that remedies resource replication and underutilization. Nonetheless, multi-tenancy has not been a primary factor in the design of machine-learning accelerators because of the race for higher speed, the recency of accelerator adoption in datacenters, and challenges associated with accelerator multi-tenancy. To that end, this project aims to explore spatial multi-tenancy as a new dimension in accelerator design to tackle resource underutilization in datacenters and bring forth cost-effective deployment of machine learning accelerators. This new dimension will significantly help reduce the slope of over-provisioning in datacenters to pave the way towards greener cloud computing. The proposed spatial multi-tenant acceleration of deep learning at scale can substantially improve the cost-effectiveness of next-generation datacenters. Given the increasing demand for deep-learning services and the carbon footprint of training and inference, this proposal will have a significant socioeconomic and environmental impact. The researchers are also strongly committed to broadening participation in computing and have comprehensive plans to engage the underrepresented groups.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.

人工智能的最新进展正在改变人类生活的许多方面，例如电子商务，医学，运输等。数据中心网络是现代在线服务的基础。随着世界从Covid-19恢复，社会正在见证对在线服务和机器学习的依赖。这种爆炸性的增长为数据中心中的计算资源带来了巨大的需求。但是，当今的方法非常昂贵且能源造成能力。实际上，如果当前系统继续增长，到2040年，数据中心将占全球碳排放量的14％。该项目旨在使用针对机器学习工作负载量身定制的高级资源共享技术来应对这一挑战。特别是，该奖项使网络运营商能够最大程度地利用网络资源，同时为用户实现高质量的服务体验。这项工作旨在通过一种称为动态架构的新范式来探索及时的机器学习加速度多租户的要求。当涉及机器学习的加速器时，源于建筑及其单租户的刚性，因此没有充分利用。因此，即将重新考虑在数据中心中重新思考自定义加速器的设计和采用，在该数据中心，成本效益的资源利用率取代了不必要的资源克隆。与微处理器类似，多租户加速度可以打开一种补救资源复制和未充分利用的途径。尽管如此，由于竞争更高的速度，数据中心中的加速器采用以及与加速器多租赁相关的挑战，多租期并不是机器学习加速器设计的主要因素。为此，该项目旨在探索空间多租赁作为加速器设计中的新维度，以解决数据中心中未充分利用的资源，并带来机器学习加速器的成本效益部署。这个新的维度将大大有助于减少数据中心中过度提供的斜率，从而为绿色云计算铺平道路。拟议的大规模深度学习的空间多租户加速可以大大提高下一代数据中心的成本效益。鉴于对深度学习服务的需求不断增长以及培训和推断的碳足迹，该提案将产生重大的社会经济和环境影响。研究人员还强烈致力于扩大计算的参与，并制定了参与代表性不足的组的全面计划。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准通过评估来获得支持的。

项目成果

GraphIt to CUDA Compiler in 2021 LOC: A Case for High-Performance DSL Implementation via Staging with BuilDSL

• DOI: 10.1109/cgo53902.2022.9741280
• 发表时间: 2022-04
• 期刊: 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
• 作者: Ajay Brahmakshatriya;S. Amarasinghe