research-article

Public Access

Gerenuk: thin computation over big native data using speculative program transformation

Authors:

Christian Navasca,

Guoqing Harry XuAuthors Info & Claims

SOSP '19: Proceedings of the 27th ACM Symposium on Operating Systems Principles

Pages 538 - 553

https://doi.org/10.1145/3341301.3359643

Published: 27 October 2019 Publication History

Abstract

Big Data systems are typically implemented in object-oriented languages such as Java and Scala due to the quick development cycle they provide. These systems are executed on top of a managed runtime such as the Java Virtual Machine (JVM), which requires each data item to be represented as an object before it can be processed. This representation is the direct cause of many kinds of severe inefficiencies.

We developed Gerenuk, a compiler and runtime that aims to enable a JVM-based data-parallel system to achieve near-native efficiency by transforming a set of statements in the system for direct execution over inlined native bytes. The key insight leading to Gerenuk's success is two-fold: (1) analytics workloads often use immutable and confined data types. If we speculatively optimize the system and user code with this assumption, the transformation can be made tractable. (2) The flow of data starts at a deserialization point where objects are created from a sequence of native bytes and ends at a serialization point where they are turned back into a byte sequence to be sent to the disk or network. This flow naturally defines a speculative execution region (SER) to be transformed. Gerenuk compiles a SER speculatively into a version that can operate directly over native bytes that come from the disk or network. The Gerenuk runtime aborts the SER execution upon violations of the immutability and confinement assumption and switches to the slow path by deserializing the bytes and re-executing the original SER. Our evaluation on Spark and Hadoop demonstrates promising results.

References

[1]

2006. Soot Framework. http://www.sable.mcgill.ca/soot.

[2]

2012. Join operation with MapReduce. https://stackoverflow.com/questions/4053857.

[3]

2015. Active/Inactive Users. https://stackoverflow.com/questions/49442420.

[4]

2015. Count Number of Posts. https://stackoverflow.com/questions/39030644.

[5]

2015. Error in Computing Frequencies. http://stackoverflow.com/questions/23042829.

[6]

2015. Join to Filer Spams. https://stackoverflow.com/questions/29622750.

[7]

2015. The Performance Comparison between In-Mapper Combiner and Regular Combiner. http://stackoverflow.com/questions/10925840.

[8]

2015. User Activity. https://stackoverflow.com/questions/33411920.

[9]

Foto N. Afrati and Jeffrey D. Ullman. 2010. Optimizing joins in a map-reduce environment. In EDBT. 99--110.

[10]

Parag Agrawal, Daniel Kifer, and Christopher Olston. 2008. Scheduling shared scans of large data files. Proceedings of VLDB Endow. 1, 1 (2008), 958--969.

Digital Library

[11]

Faraz Ahmad, Srimat T. Chakradhar, Anand Raghunathan, and T. N. Vijaykumar. 2014. ShuffleWatcher: Shuffle-aware Scheduling in Multi-tenant MapReduce Clusters. In USENIX ATC. USENIX Association, 1--13.

[12]

Apache 2017. Hadoop: Open-source implementation of MapReduce. http://hadoop.apache.org.

[13]

Apache 2017. The Hive Project. http://hive.apache.org.

[14]

Apache Flink 2017. Apache Flink. http://flink.apache.org/.

[15]

Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. 2006. Group Formation in Large Social Networks: Membership, Growth, and Evolution. In KDD. 44--54.

[16]

B. Blanchet. 1999. Escape Analysis for Object-Oriented Languages. Applications to Java. In OOPSLA. 20--34.

[17]

Paolo Boldi and Sebastiano Vigna. 2004. The WebGraph Framework I: Compression Techniques. In WWW. 595--601.

[18]

Vinayak R. Borkar, Michael J. Carey, Raman Grover, Nicola Onose, and Rares Vernica. 2011. Hyracks: A flexible and extensible foundation for data-intensive computing. In ICDE. 1151--1162.

[19]

Yingyi Bu, Vinayak Borkar, Guoqing Xu, and Michael J. Carey. 2013. A Bloat-Aware Design for Big Data Applications. In ISMM. 119--130.

[20]

Ronnie Chaiken, Bob Jenkins, Per-Ake Larson, Bill Ramsey, Darren Shakib, Simon Weaver, and Jingren Zhou. 2008. SCOPE: easy and efficient parallel processing of massive data sets. PVLDB 1, 2 (2008), 1265--1276.

Digital Library

[21]

Ronnie Chaiken, Bob Jenkins, Per-Ake Larson, Bill Ramsey, Darren Shakib, Simon Weaver, and Jingren Zhou. 2008. SCOPE: easy and efficient parallel processing of massive data sets. Proc. VLDB Endow. 1, 2 (2008), 1265--1276.

Digital Library

[22]

Craig Chambers, Ashish Raniwala, Frances Perry, Stephen Adams, Robert R. Henry, Robert Bradshaw, and Nathan Weizenbaum. 2010. FlumeJava: easy, efficient data-parallel pipelines. In PLDI. 363--375.

[23]

J. Choi, M. Gupta, M. Serrano, V. Sreedhar, and S. Midkiff. 1999. Escape Analysis for Java. In OOPSLA. 1--19.

[24]

Tyson Condie, Neil Conway, Peter Alvaro, Joseph M. Hellerstein, Khaled Elmeleegy, and Russell Sears. 2010. MapReduce online. In NSDI. 21--21.

[25]

Henggang Cui, James Cipar, Qirong Ho, Jin Kyu Kim, Seunghak Lee, Abhimanu Kumar, Jinliang Wei, Wei Dai, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, and Eric P. Xing. 2014. Exploiting Bounded Staleness to Speed Up Big Data Analytics. 37--48.

[26]

Jeffrey Dean and Sanjay Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Commun. ACM 51, 1 (2008), 107--113.

Digital Library

[27]

Jens Dittrich, Jorge-Arnulfo Quiané-Ruiz, Alekh Jindal, Yagiz Kargin, Vinay Setty, and Jörg Schad. 2010. Hadoop++: making a yellow elephant run like a cheetah (without it even noticing). Proceedings of VLDB Endow. 3 (2010), 515--529.

Digital Library

[28]

Julian Dolby and Andrew Chien. 2000. An automatic object inlining optimization and its evaluation. In PLDI. 345--357.

[29]

Gregory Essertel, Ruby Tahboub, James Decker, Kevin Brown, Kunle Olukotun, and Tiark Rompf. 2018. Flare: Optimizing Apache Spark with Native Compilation for Scale-Up Architectures and Medium-Size Data. In OSDI. 799--815.

[30]

Lu Fang, Khanh Nguyen, Guoqing Xu, Brian Demsky, and Shan Lu. 2015. Interruptible Tasks: Treating Memory Pressure As Interrupts for Highly Scalable Data-Parallel Programs. In SOSP. 394--409.

Digital Library

[31]

Raul Castro Fernandez, Matteo Migliavacca, Evangelia Kalyvianaki, and Peter Pietzuch. 2014. Making State Explicit for Imperative Big Data Processing. 49--60.

[32]

D. Gay and B. Steensgaard. 2000. Fast Escape Analysis and Stack Allocation for Object-Based Programs. In CC. 82--93.

[33]

Ovidiu Gheorghioiu, Alexandru Salcianu, and Martin Rinard. 2003. Interprocedural compatibility analysis for static object preallocation. In POPL. 273--284.

[34]

Ionel Gog, Jana Giceva, Malte Schwarzkopf, Kapil Vaswani, Dimitrios Vytiniotis, Ganesan Ramalingam, Manuel Costa, Derek G. Murray, Steven Hand, and Michael Isard. 2015. Broom: Sweeping Out Garbage Collection from Big Data Systems. In HotOS.

[35]

Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, and Ion Stoica. 2014. GraphX: Graph Processing in a Distributed Dataflow Framework. In OSDI. 599--613.

[36]

Google. 2017. Orkut social network. http://snap.stanford.edu/data/com-Orkut.html.

[37]

Zhenyu Guo, Xuepeng Fan, Rishan Chen, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Chang Liu, Wei Lin, Jingren Zhou, and Lidong Zhou. 2012. Spotting code optimizations in data-parallel pipelines through PeriSCOPE. In OSDI. 121--133.

[38]

Samuel Z. Guyer, Kathryn S. McKinley, and Daniel Frampton. 2006. Free-Me: a static analysis for automatic individual object reclamation. In PLDI. 364--375.

[39]

UC Irvine. 2014. Hyracks: A data parallel platform. http://code.google.com/p/hyracks/.

[40]

Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. 2007. Dryad: distributed data-parallel programs from sequential building blocks. In EuroSys. 59--72.

[41]

Kryo 2017. The Kryo serializer. https://github.com/EsotericSoftware/kryo.

[42]

Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a Social Network or a News Media?. In WWW. 591--600.

[43]

Aapo Kyrola, Guy Blelloch, and Carlos Guestrin. 2012. GraphChi: Large-Scale Graph Computation on Just a PC. In OSDI. 31--46.

[44]

Chris Lattner. 2005. Macroscopic Data Structure Analysis and Optimization. Ph.D. Dissertation. University of Illinois at Urbana-Champaign.

[45]

Chris Lattner and Vikram Adve. 2005. Automatic pool allocation: improving performance by controlling data structure layout in the heap. In PLDI. 129--142.

[46]

Chris Lattner, Andrew Lenharth, and Vikram Adve. 2007. Making Context-Sensitive Points-to Analysis with Heap Cloning Practical For The Real World. In PLDI. 278--289.

[47]

Rubao Lee, Tian Luo, Yin Huai, Fusheng Wang, Yongqiang He, and Xiaodong Zhang. 2011. YSmart: Yet Another SQL-to-MapReduce Translator. In ICDCS. 25--36.

[48]

Ondřej Lhoták and Laurie Hendren. 2003. Scaling Java Points-to Analysis Using SPARK. In CC. 153--169.

[49]

Ondrej Lhotak and Laurie Hendren. 2005. Run-time evaluation of opportunities for object inlining in Java. Concurrency and Computation: Practice and Experience 17, 5-6 (2005), 515--537.

[50]

Lu Lu, Xuanhua Shi, Yongluan Zhou, Xiong Zhang, Hai Jin, Cheng Pei, Ligang He, and Yuanzhen Geng. 2016. Lifetime-based Memory Management for Distributed Data Processing Systems. Proc. VLDB Endow. 9, 12 (2016), 936--947.

Digital Library

[51]

Martin Maas, Tim Harris, Krste Asanović, and John Kubiatowicz. 2015. Trash Day: Coordinating Garbage Collection in Distributed Systems. In HotOS.

Digital Library

[52]

Martin Maas, Tim Harris, Krste Asanović, and John Kubiatowicz. 2016. Taurus: A Holistic Language Runtime System for Coordinating Distributed Managed-Language Applications. In ASPLOS. 457--471.

Digital Library

[53]

Derek Gordon Murray, Michael Isard, and Yuan Yu. 2011. Steno: automatic optimization of declarative queries. In PLDI. 121--131.

[54]

Derek G. Murray, Frank McSherry, Rebecca Isaacs, Michael Isard, Paul Barham, and Martin Abadi. 2013. Naiad: A Timely Dataflow System. In SOSP. 439--455.

Digital Library

[55]

Jacob Nelson, Brandon Holt, Brandon Myers, Preston Briggs, Luis Ceze, Simon Kahan, and Mark Oskin. 2015. Latency-tolerant Software Distributed Shared Memory. In USENIX ATC. 291--305.

[56]

Khanh Nguyen, Lu Fang, Christian Navasca, Guoqing Xu, Brian Demsky, and Shan Lu. 2018. Skyway: Connecting Managed Heaps in Distributed Big Data Systems. In ASPLOS. 56--69.

Digital Library

[57]

Khanh Nguyen, Lu Fang, Guoqing Xu, Brian Demsky, Shan Lu, Sanazsadat Alamian, and Onur Mutlu. 2016. Yak: A High-Performance Big-Data-Friendly Garbage Collector. In OSDI. 349--365.

[58]

Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing Xu. 2015. Facade: A compiler and runtime for (almost) object-bounded big data applications. In ASPLOS. 675--690.

Digital Library

[59]

Tomasz Nykiel, Michalis Potamias, Chaitanya Mishra, George Kollios, and Nick Koudas. 2010. MRShare: sharing across multiple queries in MapReduce. Proceedings of VLDB Endow. 3, 1-2 (2010), 494--505.

Digital Library

[60]

Nigini Oliveira, Michael Muller, Nazareno Andrade, and Katharina Reinecke. 2018. The Exchange in StackExchange: Divergences Between StackOverflow and Its Culturally Diverse Participants. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 130 (Nov. 2018), 22 pages.

Digital Library

[61]

Christopher Olston, Benjamin Reed, Adam Silberstein, and Utkarsh Srivastava. 2008. Automatic optimization of parallel dataflow programs. In USENIX ATC. 267--273.

[62]

Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, and Andrew Tomkins. 2008. Pig latin: a not-so-foreign language for data processing. In SIGMOD. 1099--1110.

[63]

Rob Pike, Sean Dorward, Robert Griesemer, and Sean Quinlan. 2005. Interpreting the data: Parallel analysis with Sawzall. Sci. Program. 13, 4 (2005), 277--298.

Digital Library

[64]

Douglas Santry and Kaladhar Voruganti. 2014. Violet: A Storage Stack for IOPS/Capacity Bifurcated Storage Environments. 13--24.

[65]

Yefim Shuf, Manish Gupta, Rajesh Bordawekar, and Jaswinder Pal Singh. 2002. Exploiting Prolific Types for Memory Management and Optimizations. In POPL. 295--306.

[66]

Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Suresh Anthony, Hao Liu, Pete Wyckof, and Raghotham Murthy. 2009. Hive: a warehousing solution over a map-reduce framework. Proc. VLDB Endow. 2, 2 (2009), 1626--1629.

Digital Library

[67]

Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu, and Raghotham Murthy. 2010. Hive - a petabyte scale data warehouse using Hadoop. In ICDE. 996--1005.

[68]

J. Whaley and M. Rinard. 1999. Compositional Pointer and Escape Analysis for Java Programs. In OOPSLA. 187--206.

[69]

Guoqing Xu, Dacong Yan, and Atanas Rountev. 2012. Static Detection of Loop-Invariant Data Structures. In ECOOP. 738--763.

[70]

Guoqing Harry Xu, Margus Veanes, Michael Barnett, Madan Musuvathi, Todd Mytkowicz, Ben Zorn, Huan He, and Haibo Lin. 2019. Niijima: Sound and Automated Computation Consolidation for Efficient Multilingual Data-Parallel Pipelines. In SOSP.

Digital Library

[71]

Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, and D. Stott Parker. 2007. Map-reduce-merge: simplified relational data processing on large clusters. In SIGMOD. 1029--1040.

[72]

Yuan Yu, Michael Isard, Dennis Fetterly, Mihai Budiu, Úlfar Erlingsson, Pradeep Kumar Gunda, and Jon Currey. 2008. DryadLINQ: a system for general-purpose distributed data-parallel computing using a high-level language. In OSDI. 1--14.

[73]

Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster computing with working sets. In HotCloud.

Digital Library

[74]

Jingren Zhou, Per-Ake Larson, and Ronnie Chaiken. 2010. Incorporating partitioning and parallel plans into the SCOPE optimizer. In ICDE. 1060--1071.

Cited By

Ji RKong CXiong YHu Z(2023)Improving Oracle-Guided Inductive Synthesis by Efficient Question SelectionProceedings of the ACM on Programming Languages10.1145/35860557:OOPSLA1(819-847)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586055
Xu ZTian YZhang MZhao GJiang YSun C(2023)Pushing the Limit of 1-Minimality of Language-Agnostic Program ReductionProceedings of the ACM on Programming Languages10.1145/35860497:OOPSLA1(636-664)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586049
Zhang GMariano BShen XDillig I(2023)Automated Translation of Functional Big Data Queries to SQLProceedings of the ACM on Programming Languages10.1145/35860477:OOPSLA1(580-608)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586047
Show More Cited By

Index Terms

Gerenuk: thin computation over big native data using speculative program transformation
1. Information systems
  1. Data management systems
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

Recommendations

Programming with exceptions in JCilk
Special issue: Synchronization and concurrency in object-oriented languages

JCilk extends the serial subset of the Java language by importing the fork-join primitives spawn and sync from the Cilk multithreaded language, thereby providing call-return semantics for multithreaded subcomputations. In addition, JCilk transparently ...
JET: exception checking in the Java native interface
OOPSLA '11

Java's type system enforces exception-checking rules that stipulate a checked exception thrown by a method must be declared in the throws clause of the method. Software written in Java often invokes native methods through the use of the Java Native ...
Finding bugs in exceptional situations of JNI programs
CCS '09: Proceedings of the 16th ACM conference on Computer and communications security

Software flaws in native methods may defeat Java's guarantees of safety and security. One common kind of flaws in native methods results from the discrepancy on how exceptions are handled in Java and in native methods. Unlike exceptions in Java, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SOSP '19: Proceedings of the 27th ACM Symposium on Operating Systems Principles

October 2019

615 pages

ISBN:9781450368735

DOI:10.1145/3341301

General Chairs:
Tim Brecht
University of Waterloo
,
Carey Williamson
University of Calgary

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGOPS: ACM Special Interest Group on Operating Systems

In-Cooperation

USENIX Assoc: USENIX Assoc

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

SOSP '19

Sponsor:

SIGOPS

SOSP '19: ACM SIGOPS 27th Symposium on Operating Systems Principles

October 27 - 30, 2019

Ontario, Huntsville, Canada

Acceptance Rates

Overall Acceptance Rate 131 of 716 submissions, 18%

Upcoming Conference

SOSP '24

Sponsor:
sigops

ACM SIGOPS 30th Symposium on Operating Systems Principles

November 5 - 8, 2024

Austin , TX , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
1,079
Total Downloads

Downloads (Last 12 months)102
Downloads (Last 6 weeks)15

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ji RKong CXiong YHu Z(2023)Improving Oracle-Guided Inductive Synthesis by Efficient Question SelectionProceedings of the ACM on Programming Languages10.1145/35860557:OOPSLA1(819-847)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586055
Xu ZTian YZhang MZhao GJiang YSun C(2023)Pushing the Limit of 1-Minimality of Language-Agnostic Program ReductionProceedings of the ACM on Programming Languages10.1145/35860497:OOPSLA1(636-664)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586049
Zhang GMariano BShen XDillig I(2023)Automated Translation of Functional Big Data Queries to SQLProceedings of the ACM on Programming Languages10.1145/35860477:OOPSLA1(580-608)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586047
Shapira YAvneri EDrachsler-Cohen D(2023)Deep Learning Robustness Verification for Few-Pixel AttacksProceedings of the ACM on Programming Languages10.1145/35860427:OOPSLA1(434-461)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586042
Menz JHirsch ALi PGarg D(2023)Compositional Security Definitions for Higher-Order Where DeclassificationProceedings of the ACM on Programming Languages10.1145/35860417:OOPSLA1(406-433)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586041
Fox AStockwell GXiong SBecker HMulligan DPetri GChong N(2023)A Verification Methodology for the Arm® Confidential Computing Architecture: From a Secure Specification to Safe ImplementationsProceedings of the ACM on Programming Languages10.1145/35860407:OOPSLA1(376-405)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586040
Wagner CJaber NSamanta R(2023)Enabling Bounded Verification of Doubly-Unbounded Distributed Agreement-Based Systems via Bounded RegionsProceedings of the ACM on Programming Languages10.1145/35860337:OOPSLA1(172-200)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586033
Lin ZChen XTrinh MWang JRoşu G(2023)Generating Proof Certificates for a Language-Agnostic Deductive Program VerifierProceedings of the ACM on Programming Languages10.1145/35860297:OOPSLA1(56-84)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586029
Wang CLin F(2023)Solving Conditional Linear Recurrences for Program Verification: The Periodic CaseProceedings of the ACM on Programming Languages10.1145/35860287:OOPSLA1(28-55)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586028
Li SSu Z(2023)Accelerating Fuzzing through Prefix-Guided ExecutionProceedings of the ACM on Programming Languages10.1145/35860277:OOPSLA1(1-27)Online publication date: 6-Apr-2023
https://dl.acm.org/doi/10.1145/3586027
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents