The big data revolution has ushered an era with ever increasing volumes and complexity of data requiring ever faster computational analysis. During this very same era, CPU performance growth has been stagnating, pushing the industry to either scale their computation horizontally using multiple nodes in datacenters, or to scale vertically using heterogeneous components to reduce compute time. However, networking and storage continue to provide both higher throughput and lower latency, which allows for leveraging heterogeneous components, deployed in data centers around the world. Still, the integration of big data analytics frameworks with heterogeneous hardware components such as GPGPUs and FPGAs is challenging, because there is an increasing gap in the level of abstraction between analytics solutions developed with big data analytics frameworks, and accelerated kernels developed with heterogeneous components. In this article, we focus on FPGA accelerators that have seen wide-scale deployment in large cloud infrastructures. FPGAs allow the implementation of highly optimized hardware architectures, tailored exactly to an application, and unburdened by the overhead associated with traditional general-purpose computer architectures. FPGAs implementing dataflow-oriented architectures with high levels of (pipeline) parallelism can provide high application throughput, often providing high energy efficiency. Latency-sensitive applications can leverage FPGA accelerators by directly connecting to the physical layer of a network, and perform data transformations without going through the software stacks of the host system. While these advantages of FPGA accelerators hold promise, difficulties associated with programming and integration limit their use. This article explores the existing practices in big data analytics frameworks, discusses the aforementioned gap in development abstractions, and provides some perspectives on how to address these challenges in the future.

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大数据分析的FPGA加速：挑战与机遇

大数据革命迎来了一个时代，数据量越来越大，数据越来越复杂，需要更快的计算分析能力。在同一时代，CPU性能的增长一直停滞不前，这迫使业界要么使用数据中心中的多个节点水平扩展计算，要么使用异构组件垂直扩展计算以减少计算时间。但是，网络和存储继续提供更高的吞吐量和更低的延迟，这允许利用部署在全球数据中心中的异构组件。尽管如此，将大数据分析框架与异构硬件组件（如GPGPU和FPGA）集成在一起仍具有挑战性，因为使用大数据分析框架开发的分析解决方案之间的抽象水平差距越来越大，以及使用异类组件开发的加速内核。在本文中，我们重点介绍已经在大型云基础架构中进行了大规模部署的FPGA加速器。FPGA允许实施高度优化的硬件体系结构，完全针对应用进行定制，并且不受与传统通用计算机体系结构相关的开销的负担。实施具有高水平（管道）并行性的面向数据流的架构的FPGA可以提供很高的应用程序吞吐量，通常可以提供很高的能源效率。对延迟敏感的应用程序可以通过直接连接到网络的物理层来利用FPGA加速器，并且无需通过主机系统的软件堆栈即可执行数据转换。尽管FPGA加速器的这些优势有望实现，与编程和集成相关的困难限制了它们的使用。本文探讨了大数据分析框架中的现有实践，讨论了开发抽象中的上述差距，并就如何应对未来的挑战提供了一些见解。