research-article

Datasize-Aware High Dimensional Configurations Auto-Tuning of In-Memory Cluster Computing

Authors:

Xuehai QianAuthors Info & Claims

ASPLOS '18: Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems

Pages 564 - 577

https://doi.org/10.1145/3173162.3173187

Published: 19 March 2018 Publication History

Abstract

In-Memory cluster Computing (IMC) frameworks (e.g., Spark) have become increasingly important because they typically achieve more than 10× speedups over the traditional On-Disk cluster Computing (ODC) frameworks for iterative and interactive applications. Like ODC, IMC frameworks typically run the same given programs repeatedly on a given cluster with similar input dataset size each time. It is challenging to build performance model for IMC program because: 1) the performance of IMC programs is more sensitive to the size of input dataset, which is known to be difficult to be incorporated into a performance model due to its complex effects on performance; 2) the number of performance-critical configuration parameters in IMC is much larger than ODC (more than 40 vs. around 10), the high dimensionality requires more sophisticated models to achieve high accuracy. To address this challenge, we propose DAC, a datasize-aware auto-tuning approach to efficiently identify the high dimensional configuration for a given IMC program to achieve optimal performance on a given cluster. DAC is a significant advance over the state-of-the-art because it can take the size of input dataset and 41 configuration parameters as the parameters of the performance model for a given IMC program, --- unprecedented in previous work. It is made possible by two key techniques: 1) Hierarchical Modeling (HM), which combines a number of individual sub-models in a hierarchical manner; 2) Genetic Algorithm (GA) is employed to search the optimal configuration. To evaluate DAC, we use six typical Spark programs, each with five different input dataset sizes. The evaluation results show that DAC improves the performance of six typical Spark programs, each with five different input dataset sizes compared to default configurations by a factor of 30.4x on average and up to 89x. We also report that the geometric mean speedups of DAC over configurations by default, expert, and RFHOC are 15.4x, 2.3x, and 1.5x, respectively.

References

[1]

Faraz Ahmad, Srimat T Chakradhar, Anand Raghunathan, and TN Vijaykumar. 2014. ShuffleWatcher: Shuffle-aware Scheduling in Multitenant MapReduce Clusters. In Proceedings of USENIX Annual Technical Conference (ATC) (ATC'14). USENIX Association, Philadelphia, PA, 1-12.

Digital Library

[2]

Jason Ansel, Shoaib Kamil, Kalyan Veeramachaneni, Jonathan Ragan-Kelley, Jeffrey Bosboom, Una-May O'Reilly, and Saman Amarasinghe. 2014. OpenTuner: An Extensible Framework for Program Autotuning. In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT) (PACT'14). ACM Press, Edmonton, Canada, 303-316.

Digital Library

[3]

Michael Armbrust, Reynold S. Xin, Cheng Lian, Yin Huai, Davies Liu, Joseph K. Bradley, Xiangrui Meng, Tomer Kaftan, Michael J. Franklin, Ali Ghodsi, and Matei Zaharia. 2015. Spark SQL: Relational Data Processing in Spark. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 1383-1394.

Digital Library

[4]

Zhendong Bei, Zhibin Yu, Huiling Zhang, Wen Xiong, Chengzhong Xu, Lieven Eeckhout, and Shengzhong Feng. 2016. RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop's Configuration. IEEE Transactions on Parallel and Distributed Systems 27, 5 (June 2016), 1470-1483.

Digital Library

[5]

Dazhao Cheng, Jia Rao, Yanfei Guo, and Xiaobo Zhou. 2014. Improving MapReduce Performance in Heterogeneous Environments with Adaptive Task Tuning. In Proceedings of the 15th International Middleware Conference (Middleware) (Middleware'14). USENIX Association, Bordeaux, France, 97-108.

Digital Library

[6]

Tatsuhiro Chiba and Tamiya Onodera. 2015. Workload Characterization and Optimization of TPC-H Queries on Apache Spark. Technical Report. IBM Research - Tokyo, IBM Japan, Ltd.

[7]

Jeffrey Dean and Sanjay Ghemawat. 2004. MapReduce: Simplified Data Processing on Large Clusters. In Proceedings of the International Conference on Operating Systems Design and Implementation (OSDI) (OSDI'12). USENIX Association, San Francisco, CA, 137-150.

Digital Library

[8]

Christina Delimitrou and Christos Kozyrakis. 2013. Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters. In Proceedings of the 18th International Conference on Architecture Support for Programming Languages and Operating Systems (ASPLOS) (ASPLOS'13). ACM Press, Houston, TX, 77-88.

Digital Library

[9]

Christina Delimitrou and Christos Kozyrakis. 2014. Quasar: Resource-Efficient and QoS-Aware Cluster Management. In Proceedings of the 19th International Conference on Architecture Support for Programming Languages and Operating Systems (ASPLOS) (ASPLOS'14). ACM Press, Salt Lake City, UT, 1-12.

Digital Library

[10]

Adem Efe Gencer, David Bindel, Emin Gun Sirer, and Robbert van Renesse. 2015. Configuring Distributed Computations Using Response Surfaces. In Proceedings of the annual ACM/IFIP/USENIX Middleware conference (Middleware) (Middleware'15). USENIX Association, Vancouver, Canada, 235-246

Digital Library

[11]

Robert Gentleman and Ross Ihaka. 2016. The R Project for Statistical Computing. (Sept. 2016). Retrieved Januray 20, 2018 from https://www.r-project.org/

[12]

Herodotos Herodotou. 2011. Hadoop Performance Models. Technical Report CS-2011-05. Duke University, Durham, NC.

[13]

Herodotos Herodotou and Shivnath Babu. 2011. Profiling, What-If Analysis, and Cost-Based Optimization of MapReduce programs. Journal of VLDB Endowment 4, 11 (Jan. 2011), 1111-1122.

Digital Library

[14]

Herodotos Herodotou, Harold Lim, Gang Luo, Nedyalko Borisov, Liang Dong, Fatma Bilgen Cetin, and Shivnath Babu. 2011. Starfish: A Self-tuning System for Big Data Analytics. In Proceedings of the Biennial International Conference on Innovative Data Systems Research (CIDR'11). CIDRDB, 261-272.

[15]

Peng Huang, William J. Bolosky, Abhishek Singh, and Yuanyuan Zhou. 2015. Conf Valley: A Systematic Configuration Validation Framework for Cloud Services. In Proceedings of the ACM SIGOPS/EuroSys European Conference on Computer Systems (EuroSys) (EuroSys'15). USENIX Association, Bordeaux, France, 1-16.

Digital Library

[16]

Cloudera Inc. 2016. Tuning Spark Applications. (June 2016). Retrieved Januray 20, 2018 from https://www.cloudera.com/documentation/enterprise/5-4-x/topics/admin_spark_tuning.html

[17]

Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. 2007. Dryad: Distributed Data-Parallel Programs form Sequential Building Blocks. In Proceedings of the ACM SIGOPS/EuroSys European Conference on Computer Systems (EuroSys) (EuroSys'07). USENIX Association, Lisbon, Portugal, 59-72.

Digital Library

[18]

Manoj Kumar, Mohammad Husian, Naveen Upreti, and Deepti Gupta. 2010. Genetic algorithm: Review and Application. International Journal of Information Technology and Knowledge Management 2, 2 (Jan. 2010), 451-454.

[19]

Palden Lama and Xiaobo Zhou. 2012. AROMA: Automated Resource Allocation and Configuration of MapReduce Environment in the Cloud. In Proceedings of the 9th ACM International Conference on Autonomic Computing (ICAC) (ICAC'12). ACM Press, San Jose, CA, 63-72.

Digital Library

[20]

Jacek Laskowski. 2016. Mastering Apache Spark. (Jan. 2016). Retrieved Januray 20, 2018 from https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-dagscheduler-stages.html

[21]

Benjamin C. Lee and David Brooks. 2010. Applied Inference: Case Studies in Micro-architectural Design. ACM Transactions on Architecture and Code Optimization (TACO) 7, 2 (Sept. 2010), 8:1-8:35.

Digital Library

[22]

Roger J Lewis. 2000. An introduction to classification and regression tree (CART) analysis. In Proceedings of Annual Meeting of the Society for Academic Emergency Medicine. San Francisco, CA, 1-14.

[23]

Haoyuan Li, Ali Ghodsi, Matei Zaharia, Scott Shenker, and Ion Stoica. 2014. Tachyon: Reliable, memory speed storage for cluster computing frameworks. In Proceedings of the ACM Symposium on Cloud Computing (SoCC) (SoCC'14). ACM Press, Seattle, WA, 1-15.

Digital Library

[24]

Shen Li, Shaohan Hu, Shiguang Wang, Lu Su, Tarek Abdelzaher, Indranil Gupta, and Richard Pace. 2014. Woha: Deadline-aware map-reduce workflow scheduling framework over hadoop clusters. In Proceedings of the 2014 IEEE 34th International Conference on Distributed Computing Systems (ICDCS) (ICDCS'14). IEEE, Madrid, Spain, 93-103.

Digital Library

[25]

Guangdeng Liao, Kushal Datta, and Theodore L Willke. 2013. Gunther: Search-Based Auto-Tuning of MapReduce. In Proceedings of Euro-Par 2013 Parallel Processing (EuroPar'13). Springer, Berlin, Heidelberg, 406-419.

Digital Library

[26]

Luo Lie. 2010. Heuristic Artificial Intelligent Algorithm for Genetic Algorithm. Key Engineering Materials 439 (May 2010), 516-521.

[27]

Weiqing Liu, Jiannong Cao, Lei Yang, Lin Xu, Xuanjia Qiu, and Jing Li. 2017. AppBooster: Boosting the Performance of Interactive Mobile Applications with Computation Offloading and Parameter Tuning. IEEE Transactions on Parallel and Distributed Systems 28, 6 (June 2017), 1593-1606.

Digital Library

[28]

Zhaolei Liu and TS Eugene Ng. 2017. Leaky Buffer: A Novel Abstraction for Relieving Memory Pressure from Cluster Data Processing Frameworks. IEEE Transactions on Parallel and Distributed Systems 28, 1 (March 2017), 128-140.

Digital Library

[29]

Martin Maas, Tim Harris, Krste Asanovic, and John Kubiatowicz. 2015. Trash Day: Coordinating Garbage Collection in Distributed Systems. In Proceedings of the 15th USENIX Workshop on Hot Topics in Operating Systems (HotOS) (HotOS XV). USENIX Association, Kartause Ittingen, Switzerland, 1-6.

Digital Library

[30]

Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, and Ameet Talwalkar. 2016. MLlib: Machine Learning in Apach Spark. The Journal of Machine Learning Research 17, 1 (Jan. 2016), 1-7.

Digital Library

[31]

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 Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI) (OSDI'16). USENIX Association, Savannah, GA, 349-365.

Digital Library

[32]

Andrew Or and Josh Rosen. 2016. Unified Memor Management in Spark 1.6. (Jan. 2016). Retrieved Januray 20, 2018 from https://issues.apache.org/jira/secure/attachment/12765646/unified-memory-management-spark-10000.pdf

[33]

Kay Ousterhout, Ryan Rasti, Sylvia Ratnasamy, Scott Shenker, and Byung-Gon Chun. 2015. Making Sense of Performance in Data Analytics Frameworks. In Proceedings of the 12nd USENIX Symposium on Networked Systems Design and Implementation (NSDI) (NSDI'15). USENIX Association, Oakland, CA, 293-307.

Digital Library

[34]

Pankaj. 2017. Java (JVM) Memory Model - Memory Management in Java. (March 2017). Retrieved Januray 20, 2018 from http://www.journaldev.com/2856/java-jvm-memory-model-memory-management-in-java

[35]

Simone Pellegrini, Radu Prodan, and Thomas Fahringer. 2012. Tuning MPI Runtime Parameter Setting for High Performance Computing. In Proceedings of IEEE International Conference on Cluster Computing Workshops. IEEE Computer Society, Washington, DC, 213-221.

Digital Library

[36]

Zujie Ren, Xianghua Xu, Jian Wan, Weisong Shi, and Min Zhou. 2012. Workload Characterization on a Production Hadoop Cluster: A Case Study on Taobao. In Proceedings of IEEE International Symposium on Workload Characterization (IISWC) (IISWC'12). IEEE Computer Society, San Diego, CA, 1-11.

Digital Library

[37]

Anooshiravan Saboori, Guofei Jiang, and Haifeng Chen. 2008. Autotuning Configurations in Distributed Systems for Performance Improvements using Evolutionary Strategies. In Proceedings of the 28th International Conference on Distributed Computing Systems (ICDCS) (ICDCS'08). IEEE Computer Society, Beijing, China, 769-776.

Digital Library

[38]

Juwei Shi, Yunjie Qiu, Umar Farooq Minhas, Limei Jiao, Chen Wang, Berthold Reinwald, and Fatma Ozcan. 2015. Clash of the Titans: MapReduce vs. Spark for Large Scale Data Analytics. In Proceedings of the 42nd International Conference on Very Large Data Bases (VLDB Endowment), Vol.8, No.13 (VLDB'15), Vol. 8. Hawai'i, USA, 2110-2121.

Digital Library

[39]

Xueyuan Su, Garret Swart, Brian Goetz, Brian Oliver, and Paul Sandoz. 2014. Changing engines in midstream: A java stream computational model for big data processing. Proceedings of the VLDB Endowment 7, 13 (Sept. 2014), 1343-1354.

Digital Library

[40]

Apache HBase Team. 2016. Apache HBase. (June 2016). Retrieved Januray 20, 2018 from http://hadoop.apache.org/hbase/

[41]

Aparch Spark Team. 2016. Aparch Spark. (March 2016). Retrieved Januray 20, 2018 from http://spark.apache.org/

[42]

Aparch Spark Team. 2016. Spark Configuration. (May 2016). Retrieved Januray 20, 2018 from http://spark.apache.org/docs/latest/configuration.html

[43]

Aparch Spark Team. 2016. Tuning Spark. (June 2016). Retrieved Januray 20, 2018 from http://spark.apache.org/docs/latest/tuning.html

[44]

Spark Streaming Team. 2016. Spark Streaming. (March 2016). Retrieved Januray 20, 2018 from http://spark.apache.org/streaming/

[45]

White Tom. 2012. Hadoop: The definitive guide. O'Reilly Media, Inc.

Digital Library

[46]

Virginia Torczon and Michael W Trosset. {n. d.}. From Evolutionary Operation to Parallel Direct Search: Pattern Search Algorithms for Numerical Optimization. Computing Science and Statistics 29 ({n. d.}).

[47]

Inc. TypeSafe. 2015. Apache Spark Survey from Typesafe. (Jan. 2015). Retrieved Januray 20, 2018 from https://dzone.com/articles/apache-spark-survey-typesafe-0

[48]

Md. Wasi ur Rahman, Nusrat Sharmin Islam, Xiaoyi Lu, Dipti Shankar, and Dhabaleswar K. Panda. 2016. MR-Advisor: A Comprehensive Tuning Tool for Advising HPC Users to Accelerate MapReduce Applications on Supercomputers. In Proceedings of 2016 IEEE 28th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'16). IEEE Computer Society, Los Angeles, CA, 198-205.

[49]

Guolu Wang, Jungang Xu, and Ben He. 2016. A Novel Method for Tuning Configuration parameters of Spark Based on Machine Learning. In Proceedings of the 2016 IEEE 18th International Conference on High Performance Computing and Communications (HPCC) (HPCC'16). IEEE Computer Society, Sydney, Australia, 586-593.

[50]

Jingjing Wang and Magdalena Balazinska. 2016. Toward elastic memory management for cloud data analytics. In Proceedings of the 3rd ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond. ACM Press, San Francisco, CA, 1-7.

Digital Library

[51]

Reynold S. Xin, Joseph E. Gonzalez, Michael J. Franklin, and Ion Stoica. 2013. GraphX: A Resilient Distributed Graph System on Spark. In Proceedings of the First International Workshop on Graph Data Management Experimence and System. 1-5.

Digital Library

[52]

Wen Xiong, Zhibin Yu, Lieven Eeckhout, Zhengdong Bei, Fan Zhang, and Chengzhong Xu. 2015. SZTS: A Novel Big Data Transportation System Benchmark Suite. In Proceedings of the 44th International Conference on Parallel Processing (ICPP) (ICPP'15). IEEE, Beijin, China, 819-828.

Digital Library

[53]

Tianyin Xu, Long Jin, Xuepeng Fan, Yuanyuan Zhou, Shankar Pasupathy, and Rukma Talwadker. 2015. Hey, You Have Given Me Too Many Knobs. In Proceedings of the 10th Joint Meeting on Foundations of Software Engineering. ACM Press, Bergamo, Italy, 307-319.

Digital Library

[54]

Tianyin Xu, Jiaqi Zhang, Peng Huang, Jing Zheng, Tianwei Sheng, Ding Yuan, Yuanyuan Zhou, and Shankar Pasupathy. 2013. Do Not Blame Users for Misconfigurations. In Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) (SOSP'13). USENIX Association, Farmington, Pennsylvania, 244-259.

Digital Library

[55]

Tianyin Xu and Yuanyuan Zhou. 2015. Systems Approaches to Tackling Configuration Errors: A Survey. Comput. Surveys 47, 4 (July 2015), 1-41.

Digital Library

[56]

Tao Ye and Shivkumar Kalyanaraman. {n. d.}. A Recursive Random Search Algorithm for Large-Scale Network Parameter Configuration. ACM SIGMETRICS Performance Evaluation Review 31, 1 ({n. d.}).

Digital Library

[57]

Nezih Yigitbasi, Theodore L. Willke, Guangdeng Liao, and Dick H. J. Epema. 2013. Towards Machine Learning-Based Auto-tuning of MapReduce. In Proceedings of the 21st International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS) (MASCOTS'13). IEEE Computer Society, San Francisco, CA, 11-20.

Digital Library

[58]

Zuoning Yin, Xiao Ma, Jing Zheng, Yuanyuan Zhou, Lakshmi N. Bairavasundaram, and Shankar Pasupathy. 2011. An Empirical Study on Configuration Errors in Commercial and Open Source Systems. In Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) (SOSP'11). USENIX Association, Cascais, Portugal, 159-172.

Digital Library

[59]

Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2010. Spark: Cluster Computing with Working Sets. In Proceedings of the 2nd USENIX Workshop on Hot Topics in Cloud Computing (HotCloud) (HotCloud'10). USENIX Association, Boston, MA, 1-8.

Digital Library

[60]

Jiaqi Zhang, Lakshminarayanan Renganarayana, Xiaolan Zhang, Niyu Ge, Vasanth Bala, Tianyin Xu, and Yuanyuan Zhou. 2014. EnCore: Exploiting System Environment and Correlation Information for Misconfiguration Detection. In Proceedings of the 19th International Conference on Architecture Support for Programming Languages and Operating Systems (ASPLOS) (ASPLOS'14). ACM Press, Salt Lake City, UT, 687-700

Digital Library

[61]

Yao Zhao, Fei Hu, and Haopeng Chen. 2016. An Adaptive Tuning Strategy on Spark Based on In-memory Computation Characteristics. In Proceedings of the 18th International Conference on Advanced Communication Technology (ICACT) (ICACT'16). PyeongChang, Korea (South), 484-488.

Cited By

Chen CXin JYu Z(2024)TIE: Fast Experiment-Driven ML-Based Configuration Tuning for In-Memory Data AnalyticsIEEE Transactions on Computers10.1109/TC.2024.336593773:5(1233-1247)Online publication date: May-2024
https://doi.org/10.1109/TC.2024.3365937
Zou QRong XYu J(2024)Semantic Feature-Driven Automatic Parameter Optimization of Apache Spark2024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10498516(316-319)Online publication date: 19-Jan-2024
https://doi.org/10.1109/NNICE61279.2024.10498516
Shan YYang YQian XYu Z(2024)Guser: A GPGPU Power Stressmark Generator2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00087(1111-1124)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00087
Show More Cited By

Index Terms

Datasize-Aware High Dimensional Configurations Auto-Tuning of In-Memory Cluster Computing
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
      1. Embedded hardware
      2. Embedded software

Recommendations

LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications
SIGMOD '22: Proceedings of the 2022 International Conference on Management of Data

Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem, but suffer from two drawbacks. First, it takes a long time (high overhead) to collect ...
Datasize-Aware High Dimensional Configurations Auto-Tuning of In-Memory Cluster Computing
ASPLOS '18

In-Memory cluster Computing (IMC) frameworks (e.g., Spark) have become increasingly important because they typically achieve more than 10× speedups over the traditional On-Disk cluster Computing (ODC) frameworks for iterative and interactive ...
Auto-tuning of fast fourier transform on graphics processors
PPoPP '11

We present an auto-tuning framework for FFTs on graphics processors (GPUs). Due to complex design of the memory and compute subsystems on GPUs, the performance of FFT kernels over the range of possible input parameters can vary widely. We generate ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

ASPLOS '18: Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems

March 2018

827 pages

ISBN:9781450349116

DOI:10.1145/3173162

General Chairs:
Xipeng Shen
North Carolina State University, USA
,
James Tuck
North Carolina State University, USA
,
Program Chairs:
Ricardo Bianchini
Microsoft Research, USA
,
Vivek Sarkar
Georgia Institute of Technology, USA

ACM SIGPLAN Notices Volume 53, Issue 2
ASPLOS '18
February 2018
809 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/3296957
Editor:
Matthew Fluet
Rodchester Institude of Technology
Issue’s Table of Contents

Copyright © 2018 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 ACM 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

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 March 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Key Technique Research on Haiyun Data System of NICT, CAS
Outstanding Technicial Talent Program of CAS
Shenzhen Technology Research Project
The National Key R&D Program of China
NSFC
The Major Scientific and Technological Project of Guangdong Province

Conference

ASPLOS '18

Sponsor:

ASPLOS '18: Architectural Support for Programming Languages and Operating Systems

March 24 - 28, 2018

VA, Williamsburg, USA

Acceptance Rates

ASPLOS '18 Paper Acceptance Rate 56 of 319 submissions, 18%;

Overall Acceptance Rate 535 of 2,713 submissions, 20%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

64
Total Citations
View Citations
1,042
Total Downloads

Downloads (Last 12 months)85
Downloads (Last 6 weeks)4

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chen CXin JYu Z(2024)TIE: Fast Experiment-Driven ML-Based Configuration Tuning for In-Memory Data AnalyticsIEEE Transactions on Computers10.1109/TC.2024.336593773:5(1233-1247)Online publication date: May-2024
https://doi.org/10.1109/TC.2024.3365937
Zou QRong XYu J(2024)Semantic Feature-Driven Automatic Parameter Optimization of Apache Spark2024 4th International Conference on Neural Networks, Information and Communication (NNICE)10.1109/NNICE61279.2024.10498516(316-319)Online publication date: 19-Jan-2024
https://doi.org/10.1109/NNICE61279.2024.10498516
Shan YYang YQian XYu Z(2024)Guser: A GPGPU Power Stressmark Generator2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00087(1111-1124)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00087
Ren JXu DYang SZhao JLi ZNavasca CWang CXu HLi D(2024)Enabling Large Dynamic Neural Network Training with Learning-based Memory Management2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA57654.2024.00066(788-802)Online publication date: 2-Mar-2024
https://doi.org/10.1109/HPCA57654.2024.00066
Li YJiang HShen YFang YYang XHuang DZhang XZhang WZhang CChen PCui B(2023)Towards General and Efficient Online Tuning for SparkProceedings of the VLDB Endowment10.14778/3611540.361154816:12(3570-3583)Online publication date: 12-Sep-2023
https://dl.acm.org/doi/10.14778/3611540.3611548
Chen ZChen PWang PYu GHe ZMai GChandra SBlincoe KTonella P(2023)DiagConfig: Configuration Diagnosis of Performance Violations in Configurable Software SystemsProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616300(566-578)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616300
Dou HWei XWang KZhang YChen PHuang Y(2023)EFTuner: A Bi-Objective Configuration Parameter Auto-Tuning Method Towards Energy-Efficient Big Data ProcessingProceedings of the 14th Asia-Pacific Symposium on Internetware10.1145/3609437.3609443(292-301)Online publication date: 4-Aug-2023
https://dl.acm.org/doi/10.1145/3609437.3609443
Chen WMo ZXu HYe KXu CMohror KArnold DBadia R(2023)Interference-aware Multiplexing for Deep Learning in GPU Clusters: A Middleware ApproachProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607060(1-15)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3581784.3607060
Shen YRen XLu YJiang HXu HPeng DLi YZhang WCui BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Rover: An Online Spark SQL Tuning Service via Generalized Transfer LearningProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599953(4800-4812)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599953
Zhang ZGlova ASherwood TBalkind JAamodt TJerger NSwift M(2023)A Prediction System ServiceProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575714(48-60)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3575693.3575714
Show More Cited By

View Options

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

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents