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

在现代服务器体系结构中，处理器插槽和内存系统作为单独的模块实现。这些模块之间的数据交换是昂贵的--它很慢，它消耗大量的能量，并且狭窄的数据链路需要很长的等待时间。新兴的大数据工作负载将需要在处理器和内存之间进行特别大量的数据移动。为了降低大数据工作负载的数据移动成本，该项目尝试设计能够利用3D堆叠技术的新服务器架构。提出的方法称为近数据计算(NDC)，它缩短了计算单元子集和内存子集之间的距离，可以为具有局部性的工作负载带来高效率。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和降低大数据工作负载的能源需求的技术。有几份报告指出了这些工作量对国家、工业和科学计算基础设施的重要性。项目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。PIs将通过公开分发其部分软件基础设施以及参与涉及少数族裔和K-12学生的外联计划来扩大其影响。

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.