{"title":"fpga加速Apache Spark大数据分析","authors":"Ehsan Ghasemi, P. Chow","doi":"10.1109/FCCM.2016.33","DOIUrl":null,"url":null,"abstract":"Summary form only given. Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform-independent, high-abstraction programming paradigm for large-scale data processing by leveraging the Java frame-work. Though it provides software portability across various machines, Java also limits the performance of distributed environments, such as Spark. While it may be unrealistic to rewrite platforms like Spark in a faster language, a more viable approach to mitigate its poor performance is to accelerate the computations while still working within the Java-based framework. This work demonstrates the feasibility of incorporating FPGA acceleration into Spark, and uses a MapReduce implementation of the k-means clustering algorithm to show that acceleration is possible even when using a hardware platform that is not well-optimized for performance. An important feature of our approach is that the use of FPGAs is completely transparent to the user through the use of library functions, which is a common way by which users access functions provided by Spark. Power users can further develop other computations using high-level synthesis.","PeriodicalId":113498,"journal":{"name":"2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Accelerating Apache Spark Big Data Analysis with FPGAs\",\"authors\":\"Ehsan Ghasemi, P. Chow\",\"doi\":\"10.1109/FCCM.2016.33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform-independent, high-abstraction programming paradigm for large-scale data processing by leveraging the Java frame-work. Though it provides software portability across various machines, Java also limits the performance of distributed environments, such as Spark. While it may be unrealistic to rewrite platforms like Spark in a faster language, a more viable approach to mitigate its poor performance is to accelerate the computations while still working within the Java-based framework. This work demonstrates the feasibility of incorporating FPGA acceleration into Spark, and uses a MapReduce implementation of the k-means clustering algorithm to show that acceleration is possible even when using a hardware platform that is not well-optimized for performance. An important feature of our approach is that the use of FPGAs is completely transparent to the user through the use of library functions, which is a common way by which users access functions provided by Spark. Power users can further develop other computations using high-level synthesis.\",\"PeriodicalId\":113498,\"journal\":{\"name\":\"2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FCCM.2016.33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FCCM.2016.33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accelerating Apache Spark Big Data Analysis with FPGAs
Summary form only given. Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform-independent, high-abstraction programming paradigm for large-scale data processing by leveraging the Java frame-work. Though it provides software portability across various machines, Java also limits the performance of distributed environments, such as Spark. While it may be unrealistic to rewrite platforms like Spark in a faster language, a more viable approach to mitigate its poor performance is to accelerate the computations while still working within the Java-based framework. This work demonstrates the feasibility of incorporating FPGA acceleration into Spark, and uses a MapReduce implementation of the k-means clustering algorithm to show that acceleration is possible even when using a hardware platform that is not well-optimized for performance. An important feature of our approach is that the use of FPGAs is completely transparent to the user through the use of library functions, which is a common way by which users access functions provided by Spark. Power users can further develop other computations using high-level synthesis.