CSR: Medium: Energy-Efficient Architectures for Emerging Big-Data Workloads

在现代服务器体系结构中，处理器插座和内存系统是作为独立的模块实现的。这些模块之间的数据交换代价昂贵——速度慢，消耗大量的能量，并且对于狭窄的数据链路有很长的等待时间。新兴的大数据工作负载将需要在处理器和内存之间进行大量的数据移动。为了降低大数据工作负载的数据移动成本，该项目试图设计能够利用3d堆叠技术的新服务器架构。所提出的方法被称为近数据计算（NDC），它减少了计算单元子集和内存子集之间的距离，并且可以为表现局部性的工作负载产生高效率。该项目还将开发新的大数据算法和运行时系统，以利用新架构的特性。该项目将带来能够提高性能并减少大数据工作负载能源需求的技术。一些报告指出了这些工作负载对国家、工业和科学计算基础设施的重要性。该项目的成果将被整合到犹他大学的课程中，并将在数据中心设计和运营的新学位课程中发挥重要作用。这些pi将通过公开分发他们的部分软件基础设施和参与涉及少数民族和K-12学生的外展项目来扩大他们的影响。

In modern server architectures, the processor socket and the memory systemare implemented as separate modules. Data exchange between these modulesis expensive -- it is slow, it consumes a large amount of energy, and thereare long wait times for narrow data links. Emerging big-data workloads willrequire especially large amounts of data movement between the processorand memory. To reduce the cost of data movement for big-data workloads,the project attempts to design new server architectures that can leverage3D stacking technology. The proposed approach, referred to as Near DataComputing (NDC), reduces the distance between a subset of computationalunits and a subset of memory, and can yield high efficiency for workloadsthat exhibit locality. The project will also develop new big-data algorithms and runtime systems that can exploit the properties of thenew architectures.The project will lead to technologies that can boost performance andreduce the energy demands of big-data workloads. Several reports havecited the importance of these workloads to national, industrial, andscientific computing infrastructures. The project outcomes will beintegrated into University of Utah curricula and will play a significantrole in a new degree program on datacenter design and operation. ThePIs will broaden their impact by publicly distributing parts of theirsoftware infrastructure and by engaging in outreach programs thatinvolve minorities and K-12 students.