XPS: FULL: FP: Write-Efficient Parallel Algorithms for Emerging Memory Technologies

在过去的十年里，芯片制造商一直在通过在每个芯片上包含多个处理器核心来提高计算性能。然而，鉴于所有内核都必须访问共享内存，这种访问在能量、延迟和带宽方面越来越成为瓶颈。为了帮助解决这些和其他问题，工业界一直在开发各种新的存储器技术，如相变存储器、自旋扭矩转移磁RAM和基于忆阻器的阻性RAM。与标准DRAM存储技术相比，这些技术有望显著降低能耗和提高密度。然而，其中一个关键问题是，基于这些技术向存储器写入数据的成本明显高于从存储器读取数据，具有更高的延迟、更低的芯片带宽和更高的能源成本。该项目的目标是开发新的顺序和并行算法和算法设计技术，这些算法和算法设计技术在写入次数方面是高效的，从而通过降低能耗和提高性能来更好地利用这些新技术。这与50年来对算法的研究形成了鲜明对比，在这些算法中，写入被认为不比读取成本更高。如果成功，这项研究将对这类技术的未来用户产生广泛的影响，这可能会很多，以及对未来算法设计的模型和方法产生影响。PI还计划开发高效的算法实现，他们将免费和公开地提供这些算法。在工作范围内，PI将(1)开发用于捕获存储器中的非对称成本的适当抽象模型，(2)开发和分析模型中的算法，(3)证明下界，(4)开发有助于表达这种算法的编程抽象，(5)基于所开发的算法开发工作应用程序(例如，在图形分析和数据库中)，以及(6)实验验证了模型和抽象在指导高效算法开发方面的实用性。这种背景下的智力挑战将是开发这样的模型、算法和编程抽象，这些抽象同时简单、优雅和实用，同时获得对基本限制和权衡的洞察。

Chip manufacturers in the past ten years have been enhancing computing performance by including multiple processor cores per chip. Given that all the cores have to access a shared memory, however, this access has increasingly become a bottleneck in terms of energy, latency, and bandwidth. To help deal with these and other problems, industry has been developing a variety of new memory technologies such as phase-change memory, Spin-Torque Transfer Magnetic RAM, and Memristor-based Resistive RAM. These technologies offer the promise of significantly lower energy and higher density than standard DRAM memory technology. One of the key issues, however, is that writing to memory based on the technologies is significantly more costly than reading from memory, suffering from higher latency, lower per-chip bandwidth, and higher energy costs.The goal of this project is to develop new sequential and parallel algorithms and algorithm design techniques that are efficient in terms of the number of writes they perform, and hence make better use of these new technologies by reducing energy consumption and improving performance. This contrasts with 50 years of research on algorithms in which writes are assumed to be no more costly than reads. If successful the research will have a broad impact on future users of such technologies, which could be very many, as well as on the models and approaches for future algorithm design. The PIs also plan to develop efficient implementations of algorithms that they will make freely and openly available. The project includes an educational outreach component in which, as part of courses on databases and applied algorithms, the PIs will teach students about the new memory technologies and algorithms that can take advantage of them.Within the scope of work the PIs will (1) develop appropriate abstract models for capturing the asymmetric costs in memories, (2) develop and analyze algorithms in the models, (3) prove lower bounds, (4) develop programming abstractions that help express such algorithms, (5) develop working applications (e.g., in graph analytics and databases) based on the algorithms developed, and (6) experimentally verify the utility of the models and abstractions in guiding the development of efficient algorithms. The intellectual challenge within this context will be in developing such models, algorithms, and programming abstractions that are simultaneously simple, elegant, and practical, while at the same time gaining insights into fundamental limits and trade-offs.