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Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory

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Pubblicato in:Journal of Computer Science and Technology vol. 34, no. 1 (Jan 2019), p. 94
Autore principale: Hong, Yang
Altri autori: Zheng, Yang, Yang, Fan, Zang, Bin-Yu, Guan, Hai-Bing, Chen, Hai-Bo
Pubblicazione:
Springer Nature B.V.
Soggetti:
Synchronism
Big Data
Clusters
Multiprocessing
Distributed memory
Redesign
Memory management
Nodes
Design
Software
Information sharing
Efficiency
Commodities
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Abstract:The multicore evolution has stimulated renewed interests in scaling up applications on shared-memory multiprocessors, significantly improving the scalability of many applications. But the scalability is limited within a single node; therefore programmers still have to redesign applications to scale out over multiple nodes. This paper revisits the design and implementation of distributed shared memory (DSM) as a way to scale out applications optimized for non-uniform memory access (NUMA) architecture over a well-connected cluster. This paper presents MAGI, an efficient DSM system that provides a transparent shared address space with scalable performance on a cluster with fast network interfaces. MAGI is unique in that it presents a NUMA abstraction to fully harness the multicore resources in each node through hierarchical synchronization and memory management. MAGI also exploits the memory access patterns of big-data applications and leverages a set of optimizations for remote direct memory access (RDMA) to reduce the number of page faults and the cost of the coherence protocol. MAGI has been implemented as a user-space library with pthread-compatible interfaces and can run existing multithreaded applications with minimized modifications. We deployed MAGI over an 8-node RDMAenabled cluster. Experimental evaluation shows that MAGI achieves up to 9.25x speedup compared with an unoptimized implementation, leading to a scalable performance for large-scale data-intensive applications.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-019-1901-4
Fonte:ABI/INFORM Global