EAGER: Software-Hardware Co-Design Approaches for Multi-Level Memories

大数据问题的有效解决方案要求计算机具有非常大的内存容量，并且能够每秒执行非常多的操作。由于提供具有足够带宽的所需数量的主存储器以实现期望的吞吐量的成本令人望而却步，供应商已求助于多级存储器(MLM)架构，其中主存储器包括两级或更多级，其中每一级具有具有不同带宽和成本特性的存储器。Intel Knights Landing就是一个例子；它有两个级别的主内存-16 GB高成本、高吞吐量内存，以及高达384 GB的相对低成本、低吞吐量内存。骑士登陆还有72个计算核心，能够执行高达6万亿次的单精度运算或3万亿次的双精度运算。该项目旨在展示多核传销架构在解决大数据问题方面的有效性。该项目将为具有不同工作流特征的代表性应用程序开发高效的多核传销软件：数据并行、分层和任务并行。正在考虑的具体应用包括可压缩多相湍流的模拟、稀疏矩阵因式分解和合成孔径雷达数据的图像重建。该软件将在骑士登陆以及使用传销模拟软件进行评估。将确定开发这类软件的技术和基于工作量特征的最佳传销配置。

The effective solution of big data problems requires computers that have very large memory capacity and that are able to perform very many operations per second. Since providing the required amount of main memory with sufficient bandwidth to achieve the desired throughput is cost prohibitive, vendors have resorted to multi-level memory (MLM) architectures in which main memory comprises two or more levels, with each level having memory with different bandwidth and cost characteristics. The Intel Knights Landing is an example; it has two levels of main memory - 16Gigabytes of high-cost, high-throughput memory, and up to 384Gigabytes of relatively low-cost, low-throughput memory. The Knights Landing also has 72 compute cores capable of performing up to 6 teraflops of single precision or 3 teraflops of double precision operations. This project seeks to demonstrate the effectiveness of multicore MLM architectures in solving big data problems.This project will develop efficient multicore MLM software for representative applications with different workflow characteristics: data parallel, hierarchical, and task parallel. The specific applications being considered are simulation of compressible multiphase turbulence, sparse matrix factorization, and image reconstruction from synthetic aperture radar data. The software will be evaluated on the Knights Landing as well as using MLM simulation software. Techniques for the development of such software and optimal MLM configurations based on workload characterization will be identified.