Henry N. Schuh, Weihao Liang, Ming G. Liu, J. Nelson, A. Krishnamurthy
{"title":"Xenic: SmartNIC-Accelerated Distributed Transactions","authors":"Henry N. Schuh, Weihao Liang, Ming G. Liu, J. Nelson, A. Krishnamurthy","doi":"10.1145/3477132.3483555","DOIUrl":null,"url":null,"abstract":"High-performance distributed transactions require efficient remote operations on database memory and protocol metadata. The high communication cost of this workload calls for hardware acceleration. Recent research has applied RDMA to this end, leveraging the network controller to manipulate host memory without consuming CPU cycles on the target server. However, the basic read/write RDMA primitives demand trade-offs in data structure and protocol design, limiting their benefits. SmartNICs are a flexible alternative for fast distributed transactions, adding programmable compute cores and on-board memory to the network interface. Applying measured performance characteristics, we design Xenic, a SmartNIC-optimized transaction processing system. Xenic applies an asynchronous, aggregated execution model to maximize network and core efficiency. Xenic's co-designed data store achieves low-overhead remote object accesses. Additionally, Xenic uses flexible, point-to-point communication patterns between SmartNICs to minimize transaction commit latency. We compare Xenic against prior RDMA- and RPC-based transaction systems with the TPC-C, Retwis, and Smallbank benchmarks. Our results for the three benchmarks show 2.42x, 2.07x, and 2.21x throughput improvement, 59%, 42%, and 22% latency reduction, while saving 2.3, 8.1, and 10.1 threads per server.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"39 2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Operating Systems Review (ACM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3477132.3483555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 31
Abstract
High-performance distributed transactions require efficient remote operations on database memory and protocol metadata. The high communication cost of this workload calls for hardware acceleration. Recent research has applied RDMA to this end, leveraging the network controller to manipulate host memory without consuming CPU cycles on the target server. However, the basic read/write RDMA primitives demand trade-offs in data structure and protocol design, limiting their benefits. SmartNICs are a flexible alternative for fast distributed transactions, adding programmable compute cores and on-board memory to the network interface. Applying measured performance characteristics, we design Xenic, a SmartNIC-optimized transaction processing system. Xenic applies an asynchronous, aggregated execution model to maximize network and core efficiency. Xenic's co-designed data store achieves low-overhead remote object accesses. Additionally, Xenic uses flexible, point-to-point communication patterns between SmartNICs to minimize transaction commit latency. We compare Xenic against prior RDMA- and RPC-based transaction systems with the TPC-C, Retwis, and Smallbank benchmarks. Our results for the three benchmarks show 2.42x, 2.07x, and 2.21x throughput improvement, 59%, 42%, and 22% latency reduction, while saving 2.3, 8.1, and 10.1 threads per server.
期刊介绍:
Operating Systems Review (OSR) is a publication of the ACM Special Interest Group on Operating Systems (SIGOPS), whose scope of interest includes: computer operating systems and architecture for multiprogramming, multiprocessing, and time sharing; resource management; evaluation and simulation; reliability, integrity, and security of data; communications among computing processors; and computer system modeling and analysis.