Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory
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| Udgivet i: | Journal of Computer Science and Technology vol. 34, no. 1 (Jan 2019), p. 94 |
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| Andre forfattere: | , , , , |
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Springer Nature B.V.
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| Online adgang: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1007/s11390-019-1901-4 |2 doi | |
| 035 | |a 2918609996 | ||
| 045 | 2 | |b d20190101 |b d20190131 | |
| 084 | |a 137755 |2 nlm | ||
| 100 | 1 | |a Hong, Yang |u Shanghai Jiao Tong University, Shanghai Key Laboratory for Scalable Computing Systems, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) | |
| 245 | 1 | |a Scaling out NUMA-Aware Applications with RDMA-Based Distributed Shared Memory | |
| 260 | |b Springer Nature B.V. |c Jan 2019 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Synchronism | ||
| 653 | |a Big Data | ||
| 653 | |a Clusters | ||
| 653 | |a Multiprocessing | ||
| 653 | |a Distributed memory | ||
| 653 | |a Redesign | ||
| 653 | |a Memory management | ||
| 653 | |a Nodes | ||
| 653 | |a Design | ||
| 653 | |a Software | ||
| 653 | |a Information sharing | ||
| 653 | |a Efficiency | ||
| 653 | |a Commodities | ||
| 700 | 1 | |a Zheng, Yang |u Shanghai Jiao Tong University, Shanghai Key Laboratory for Scalable Computing Systems, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) | |
| 700 | 1 | |a Yang, Fan |u Shanghai Jiao Tong University, Shanghai Key Laboratory for Scalable Computing Systems, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) | |
| 700 | 1 | |a Zang, Bin-Yu |u Shanghai Jiao Tong University, Shanghai Key Laboratory for Scalable Computing Systems, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) | |
| 700 | 1 | |a Guan, Hai-Bing |u Shanghai Jiao Tong University, Shanghai Key Laboratory for Scalable Computing Systems, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) | |
| 700 | 1 | |a Chen, Hai-Bo |u Shanghai Jiao Tong University, Shanghai Key Laboratory for Scalable Computing Systems, Shanghai, China (GRID:grid.16821.3c) (ISNI:0000 0004 0368 8293) | |
| 773 | 0 | |t Journal of Computer Science and Technology |g vol. 34, no. 1 (Jan 2019), p. 94 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2918609996/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2918609996/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |