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

在过去的十年中，芯片制造商已经通过在每个芯片上包括多个处理器内核来增强计算性能。 然而，考虑到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。 为了帮助处理这些和其他问题，工业界已经开发了各种新的存储器技术，例如相变存储器、自旋扭矩转移磁性RAM和基于忆阻器的电阻式RAM。 这些技术提供了比标准DRAM存储器技术显著更低的能量和更高的密度的承诺。 然而，其中一个关键问题是，基于这些技术的写入存储器比从存储器中阅读的成本要高得多，具有更高的延迟、更低的每芯片带宽和更高的能源成本。本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，这些算法在执行的写入次数方面是有效的，并因此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，写操作的成本被认为不会比读操作更高。 如果成功，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，他们将免费公开提供。 该项目包括一个教育推广部分，作为数据库和应用算法课程的一部分，PI将向学生讲授新的内存技术和可以利用它们的算法。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）证明下限，（4）开发有助于表达这种算法的编程抽象，（5）开发工作应用（例如，在图分析和数据库）的基础上开发的算法，和（6）实验验证的模型和抽象的效用，指导开发的有效算法。 在这种背景下，智力挑战将是开发这样的模型，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.