可擴展的數(shù)據(jù)管理大數(shù)據(jù)的應(yīng)用程序是一項艱巨的任務(wù)。它把對持久內(nèi)存墻的問題，這使得數(shù)據(jù)訪問的高性能計算（HPC）突出的性能瓶頸更大的壓力，并改變HPC的利益HPDP（高性能數(shù)據(jù)處理）。 HPC是著名的大規(guī)模并行架構(gòu)。一種自然的方式來實現(xiàn)HPDP是增加和利用存儲并發(fā)的水平與HPC相稱。我們認為，大量的內(nèi)存并發(fā)存在于當前存儲系統(tǒng)的每一層，但它并沒有被充分利用。在這次演講中，我們重新評估存儲系統(tǒng)，引進了新型的C-AMAT模型并發(fā)數(shù)據(jù)訪問的系統(tǒng)設(shè)計分析。 C-AMAT是一個范式轉(zhuǎn)變，以支持持續(xù)的數(shù)據(jù)從一個數(shù)據(jù)中心的視圖訪問。對C-AMAT的力量在于它開辟了新的方向，以減少數(shù)據(jù)訪問延遲。在一個理想的并行存儲器系統(tǒng)，該系統(tǒng)將明確地表示，并利用并行數(shù)據(jù)訪問。這種認識主要是從當前的存儲系統(tǒng)缺失和目前的架構(gòu)和算法設(shè)計缺失。我們會檢討在現(xiàn)代內(nèi)存的系統(tǒng)中可用的并發(fā)性，提出了C-AMAT的概念，并討論了考慮和優(yōu)化的大數(shù)據(jù)應(yīng)用的并行數(shù)據(jù)訪問的可能性。我們也將提出我們的一些最新研究成果而量化和利用并行I / O以下為HPDP并行存儲的概念。

    孫賢和，美國伊利諾伊理工學院計算機科學系教授、系主任，2012年當選國際電子電氣工程師學會（IEEE）院士。他曾在美國能源部艾姆斯國家實驗室，在ICASE，美國航空航天局蘭利研究中心，在路易斯安那州立大學巴吞魯日工作，并且是ASEE海軍研究實驗室研究員。

    原文：Scalable data management for big data applications is a challenging task. It puts even more pressure on the lasting memory-wall problem, which makes data access the prominent performance bottleneck for high performance computing (HPC), and has changed the interest of HPC to HPDP (High Performance Data Processing). HPC is known for its massively parallel architectures. A natural way to achieve HPDP is to increase and utilize memory concurrency to a level commensurate with that of HPC. We argue that substantial memory concurrency exists at each layer of current memory systems, but it has not been fully utilized. In this talk we reevaluate memory systems and introduce the novel C-AMAT model for system design analysis of concurrent data accesses. C-AMAT is a paradigm shift to support sustained data accessing from a data-centric view. The power of C-AMAT is that it has opened new directions to reduce data access delay. In an ideal parallel memory system, the system will explicitly express and utilize parallel data accesses. This awareness is largely missing from current memory systems and missing from current architecture and algorithm design. We will review the concurrency available in modern memory systems, present the concept of C-AMAT, and discuss the considerations and possibility of optimizing parallel data access for big data applications. We will also present some of our recent results which quantize and utilize parallel I/O following the parallel memory concept for HPDP.