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中加入多个处理器内核来提高计算性能。然而，考虑到所有核心都必须访问共享内存，这种访问在能量、延迟和带宽方面日益成为瓶颈。为了解决这些问题和其他问题，业界一直在开发各种新的存储技术，如相变存储器，自旋扭矩传递磁性RAM和基于忆阻器的电阻式RAM。这些技术提供了比标准DRAM存储器技术更低的能量和更高的密度。然而，其中一个关键问题是，基于这些技术向内存写入数据的成本明显高于从内存读取数据，并且存在更高的延迟、更低的单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以及算法设计技术，这些算法和算法设计技术在它们执行的写入次数方面是有效的，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，在算法研究中，写的成本并不比读的成本高。如果成功，这项研究将对这些技术的未来用户产生广泛的影响，这可能是非常多的，以及对未来算法设计的模型和方法。pi还计划开发算法的有效实现，并将其免费公开提供。该项目包括一个教育拓展部分，其中，作为数据库和应用算法课程的一部分，pi将向学生讲授可以利用它们的新存储技术和算法。在工作范围内，pi将(1)开发适当的抽象模型来捕获内存中的不对称成本，(2)开发和分析模型中的算法，(3)证明下界，(4)开发有助于表达此类算法的编程抽象，(5)开发基于所开发算法的工作应用程序（例如，在图形分析和数据库中）。(6)实验验证了模型和抽象在指导高效算法开发方面的效用。这种情况下的智力挑战将是开发这样的模型、算法和编程抽象，这些模型、算法和编程抽象同时简单、优雅和实用，同时获得对基本限制和权衡的见解。

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.