Mimir: Memory-Efficient And Scalable Mapreduce For Large Supercomputing Systems

2017 31ST IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS)(2017)

引用 35|浏览73
暂无评分
摘要
In this paper we present Mimir, a new implementation of MapReduce over MPI. Mimir inherits the core principles of existing MapReduce frameworks, such as MR-MPI, while redesigning the execution model to incorporate a number of sophisticated optimization techniques that achieve similar or better performance with significant reduction in the amount of memory used. Consequently, Mimir allows significantly larger problems to be executed in memory, achieving large performance gains. We evaluate Mimir with three benchmarks on two highend platforms to demonstrate its superiority compared with that of other frameworks.
更多
查看译文
关键词
High-performance computing, Data analytics, MapReduce, Memory efficiency, Performance and scalability
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要