CAJ | 학술논문

随着高性能计算机逐步应用在大规模数据处理领域，存储系统将成为制约数据处理效率的主要瓶颈。在分析了影响数据密集型计算 I/O 性能若干关键因素的基础上，提出使用计算结点本地存储构建协作式非易失缓存、以分布式存储架构加速集中式存储架构的方法。该方法基于应用层协同使用分布化的本地存储资源，使用非易失存储介质构成大缓存空间，存放大规模数据分析的中间过程结果，以此实现高缓存命中率，并利用并发度约束控制等手段避免 I/O 竞争，充分利用本地存储的特定性能优势保证缓存加速效果，从而有效地提高了大规模数据处理过程的 I/O 效率。基于多平台多种 I/O 模式的测试结果证实了该方法的有效性，聚合 I/O 带宽具有高扩展性，典型数据密集应用的整体性能最大可提升6倍。
수착고성능계산궤축보응용재대규모수거처리영역，존저계통장성위제약수거처리효솔적주요병경。재분석료영향수거밀집형계산 I/O 성능약간관건인소적기출상，제출사용계산결점본지존저구건협작식비역실완존、이분포식존저가구가속집중식존저가구적방법。해방법기우응용층협동사용분포화적본지존저자원，사용비역실존저개질구성대완존공간，존방대규모수거분석적중간과정결과，이차실현고완존명중솔，병이용병발도약속공제등수단피면 I/O 경쟁，충분이용본지존저적특정성능우세보증완존가속효과，종이유효지제고료대규모수거처리과정적 I/O 효솔。기우다평태다충 I/O 모식적측시결과증실료해방법적유효성，취합 I/O 대관구유고확전성，전형수거밀집응용적정체성능최대가제승6배。
With HPC systems widely used in today’s modern science computing ,more data‐intensive applications are generating and analyzing the increasing scale of datasets ,which makes HPC storage system facing new challenges .By comparing the different storage architectures with the corresponding approaches of file system ,a novel cache approach , named DDCache ,is proposed to improve the efficiency of data‐intensive computing . DDCache leverages the distributed storage architecture as performance booster for centralized storage architecture by fully exploiting the potential benefits of node‐local storage distributed across the system .In order to supply much larger cache volume than volatile memory cache ,DDCache aggregates the node‐local disks as huge non‐volatile cooperative cache .Then high cache hit ratio is achieved through keeping intermediate data in the DDCache as long as possible during overall process of applications .To make the node‐local storage efficient enough to act as data cache ,locality aware data layout is used to make cached data close to compute tasks and evenly distributed .Furthermore ,concurrency control is introduced to throttle I/O requests flowing into or out of DDCache and regain the special advantage of node‐local storage .Evaluations on the typical HPC platforms verify the effectiveness of DDCache .Scalable I/O bandwidth is achieved on the well‐known HPC scenario of checkpoint/restart and the overall performance of typical data‐intensive application is improved up to 6 times .