SHF: Small: Ubiquitous and Transparent Near-data Computing for General Purpose Processors

SHF：小型：通用处理器的无处不在且透明的近数据计算

• 批准号: 2200831
• 负责人: Anthony Nowatzki
• 金额: $ 60万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200831&HistoricalAwards=false
• 关键词: SHF; Small; Ubiquitous; Transparent; Near

As multicore processors scale, the overheads of data movement and communication become the primary limitations to achieving commensurate performance and energy-efficiency gains. This is especially true for “big-data” workloads that rely on enormous datasets. One increasingly popular solution is to perform computation near where the data is stored to avoid communication; this is a paradigm called near-data processing. Despite the potential, existing near-data systems have severe limitations: they often require programmer help, they are limited to support a small subset of workloads, and they do not exploit the full range of near-data processing technologies in a coherent framework. To address these challenges, this project develops new hardware/software abstractions that can enable near-data processing capabilities for general purpose architectures, and which can be constructed by a compiler or in hardware to avoid programmer burden. The potential impact of this research is to steer microprocessor design in novel ways that can help sustain expected exponential performance improvements, including efficiently scaling existing multicore processor size by an order-of-magnitude. In addition, our open source near-data compiler/simulation framework can foster research in this new direction. In terms of education, this project enhances courses with the infrastructure to give students cross-stack experience in co-designing hardware and software. Finally, the project develops an outreach program to provide mentorship and networking opportunities to prospective graduate students across multiple universities.Towards the goal of ubiquitous and transparent near-data processing, the primary technical insight of this research is that a more powerful near-data abstraction would encapsulate the program’s interaction with each individual data-structure: its address pattern, associated computation, and dependences -- this new program abstraction is called a “fiber”. Each fiber can be offloaded to a level of the memory hierarchy (e.g. last-level caches or memory) that best optimizes for data locality, and all accesses maintain sequential memory semantics. Fibers are attractive because their high-level properties can be used to determine an optimal near-data offloading strategy, and they are sufficiently coarse grain to enable efficient coherence and coordination. Towards these goals, the project addresses five fundamental intellectual questions: What set of primitive offloading strategies is required for generality, especially considering offloading to multiple memory-hierarchy levels? How is it possible to enable programmer and even ISA-transparent NDP? How to enable sequential memory semantics without being overwhelmed by coordination messages? How to perform near-data computation at high throughput and low overhead, either by reusing general cores, processing-using-memory (PUM), or reconfigurable accelerators? And finally, how to optimize data-placement for NDP, considering the importance of co-locating the operands of near-data tasks? Overall, this project explores a radical departure from the core-centric paradigm of traditional general purpose processors, and will develop new program abstractions and microarchitectures for efficient decentralized computation based on near-data principles.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.

随着多核处理器的扩展，数据移动和通信的开销成为实现相应性能和能效提升的主要限制，对于依赖大量数据集的“大数据”工作负载来说尤其如此，一种日益流行的解决方案是执行。在数据存储位置附近进行计算以避免通信；这是一种称为近数据处理的范例，尽管有潜力，但现有的近数据系统具有严重的局限性：它们通常需要程序员的帮助，并且仅限于支持一小部分工作负载。 ，他们确实为了解决这些挑战，该项目开发了新的硬件/软件抽象，可以为通用架构启用近数据处理功能，并且可以由编译器构建。这项研究的潜在影响是以新颖的方式指导微处理器设计，有助于维持预期的指数级性能改进，包括有效地将现有的多核处理器尺寸扩大一个数量级。开源近数据编译器/模拟框架可以促进这一新方向的研究，该项目通过基础设施增强了课程，为学生提供共同设计硬件和软件的跨堆栈经验。最后，该项目开发了一个外展计划来提供指导。以及为多所大学的未来研究生提供交流机会。为了实现无处不在且透明的近数据处理的目标，本研究的主要技术见解是更强大的近数据抽象将封装程序与每个单独数据结构的交互:它的地址模式、关联的计算和依赖性——这种新的程序抽象称为“纤程”，每个纤程都可以卸载到最优化数据局部性的内存层次结构级别（例如最后一级缓存或内存），所有访问都保持顺序内存语义，因为它们的高级属性可用于确定最佳的近数据卸载策略，并且它们的粒度足够粗，可以实现高效的一致性和协调。该项目解决了五个基本的智力问题： 通用性需要什么样的原始卸载策略，特别是考虑到多个内存层次结构级别的卸载？ 如何在不使用顺序内存语义的情况下启用程序员甚至 ISA 透明的 NDP？被协调消息淹没了吗？如何通过重用通用核心、使用内存处理（PUM）或可重新配置加速器来以高吞吐量和低开销执行近数据计算？考虑到共同定位近数据任务的操作数的重要性，如何优化 NDP 的数据放置？ 总体而言，该项目探索了与传统通用处理器以核心为中心的范式的彻底背离，并将开发新的程序抽象。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的有效审查标准进行评估，被认为值得支持。

项目成果

Infinity Stream: Portable and Programmer-Friendly In-/Near-Memory Fusion

• DOI: 10.1145/3582016.3582032
• 发表时间: 2023-03
• 期刊: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
• 作者: Zhengrong Wang;Christopher Liu;Aman Arora;L. John;Tony Nowatzki

Infinity Stream: Enabling Transparent and Automated In-Memory Computing

Infinity Stream：实现透明且自动化的内存计算

• DOI: 10.1109/lca.2022.3203064
• 发表时间: 2022
• 期刊: IEEE Computer Architecture Letters
• 影响因子: 2.3
• 作者: Wang, Zhengrong;Liu, Christopher;Nowatzki, Tony
• 通讯作者: Nowatzki, Tony