RR-Compound: RDMA-Fused gRPC for Low Latency, High Throughput, and Easy Interface

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-03-23 DOI:10.1109/TPDS.2024.3404394
Liang Geng;Hao Wang;Jingsong Meng;Dayi Fan;Sami Ben-Romdhane;Hari Kadayam Pichumani;Vinay Phegade;Xiaodong Zhang
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Abstract

Advanced data centers strive for high performance and throughput, which can be achieved through the desirable merits of Remote Procedure Call (RPC) programming model and the low latency of Remote Direct Memory Access (RDMA). However, despite the widespread availability of these software and hardware utilities, they have been utilized separately for their own applications in existing production systems for many years. Although researchers have attempted to develop RDMA-enabled RPC prototypes, they often face challenges such as API discrepancies and a lack of specific features for effective integration with major production software, rendering them incompatible. This industry R&D project aims to enhance the performance of gRPC, a widely utilized RPC framework in major companies, by integrating RDMA as an internal component. Our system solution, called RR-Compound, combines the simple user interface and other merits of gRPC with low latency for remote data accesses. RR-Compound is fully compatible with gRPC and can serve as a seamless replacement without altering existing applications. However, to achieve low latency, high throughput, and scalability for RR-Compound, several technical challenges in managing network connections and memory space utilization must be effectively addressed. To overcome the limitations of existing connection methods, we have developed a new method called BPEV that is independent of gRPC and applicable to all RDMA systems. We have also retained the asynchronous framework of gRPC, albeit with limited buffer space in RDMA memory management. In micro-benchmarks, RR-Compound outperforms mRPC - the state-of-the-art RPC framework for a large number of connections, achieving a 14.77% increase in throughput and a 42.55% reduction in latency. Subsequently, we compare RR-Compound with gRPC over IPoIB using two real-world applications: KV-Store and TensorFlow. RR-Compound achieves up to a 2.35x increase in throughput and reduces the average latency by 46.92%.
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RR-Compound:RDMA 融合 gRPC,以简易接口实现低延迟和高吞吐量
先进的数据中心追求高性能和高吞吐量,这可以通过远程过程调用(RPC)编程模型和远程直接内存访问(RDMA)的低延迟来实现。然而,尽管这些软件和硬件实用程序已广泛普及,但多年来它们一直被单独用于现有生产系统中的应用。虽然研究人员已尝试开发支持 RDMA 的 RPC 原型,但它们经常面临 API 不一致、缺乏与主要生产软件有效集成的特定功能等挑战,导致它们不兼容。本行业研发项目旨在通过将 RDMA 集成为内部组件来提高 gRPC 的性能,gRPC 是大型公司广泛使用的 RPC 框架。我们的系统解决方案名为 RR-Compound,它将简单的用户界面和 gRPC 的其他优点与远程数据访问的低延迟结合在一起。RR-Compound 与 gRPC 完全兼容,可作为无缝替代,无需更改现有应用程序。然而,要实现 RR-Compound 的低延迟、高吞吐量和可扩展性,必须有效解决管理网络连接和内存空间利用方面的若干技术难题。为了克服现有连接方法的局限性,我们开发了一种名为 BPEV 的新方法,它独立于 gRPC,适用于所有 RDMA 系统。我们还保留了 gRPC 的异步框架,尽管 RDMA 内存管理中的缓冲空间有限。在微基准测试中,RR-Compound 的表现优于 mRPC(大量连接的最先进 RPC 框架),吞吐量提高了 14.77%,延迟减少了 42.55%。随后,我们使用两个实际应用对 RR-Compound 和 IPoIB 上的 gRPC 进行了比较:KV-Store 和 TensorFlow。RR-Compound 的吞吐量提高了 2.35 倍,平均延迟降低了 46.92%。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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