.png){kind=icon}
返回
General-purpose data stream processing on heterogeneous architectures with WindFlow
.png){kind=icon}
OA
AI
DOI：10.1016/j.jpdc.2023.104782.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Many emerging applications analyze data streams by running graphs of communicating tasks called operators. To develop and deploy such applications, Stream Processing Systems (SPSs) like Apache Storm and Flink have been made available to researchers and practitioners. They exhibit imperative or declarative programming interfaces to develop operators running arbitrary algorithms working on structured or unstructured data streams. In this context, the interest in leveraging hardware acceleration with GPUs has become more pronounced high-throughput use cases. Unfortunately, GPU acceleration has been studied for relational operators working on structured streams only, while non-relational operators have often been overlooked. This paper presents WINDFLOW, a library supporting the seamless GPU offloading of general partitioned-stateful operators, extending the range of operators that benefit from hardware acceleration. Its design provides high throughput still exposing a high-level API to users compared with the raw utilization of GPUs in Apache Flink.
Keyword:
Data stream processing
Heterogeneous architectures
System-on-chip devices
Multicores
GPUs
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
3.8K
被引数:
4.8K
机构
引用论文
STRETCH: Virtual Shared-Nothing Parallelism for Scalable and Elastic Stream ProcessingSTRETCH: 用于可伸缩和弹性流处理的虚拟无共享并行性
OAAI
OAAI分享收藏DSPBench: A Suite of Benchmark Applications for Distributed Data Stream Processing Systems.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏 没有更多内容
没有更多内容