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Big Data Workloads An Architect’s Perspective

BPOE 2014. Big Data Workloads An Architect’s Perspective. Lizy K. John University of Texas at Austin. BPOE 2014. The Buzz with Big Data. BPOE 2014. BIG DATA - Seeing things we could not see before. Analyze massive amounts of data Derive Insights Business Medicine World Economy.

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Big Data Workloads An Architect’s Perspective

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  1. BPOE 2014 Big Data WorkloadsAn Architect’s Perspective Lizy K. John University of Texas at Austin

  2. BPOE 2014 The Buzz with Big Data Lizy K. John

  3. BPOE 2014 BIG DATA - Seeing things we could not see before Analyze massive amounts of data Derive Insights Business Medicine World Economy Lizy K. John

  4. BPOE 2014 An Architect would like to know • What kind of cores, memory organizations and clustering support needed to support big data • Performance metrics to guide workload partitioning strategies other than use available/affordable nodes • Partitioning considering performance, power, energy • Scaling of computation and communication depending on partitions Becomes important to understand big data workloads Lizy K. John

  5. BPOE 2014 What is “Big Data”? • 1 Terabyte? • Yesterdays “Big Data” • Petabytes? Exabytes? • Today’s “Big Data” • Zettabytes? • Tomorrow’s “Big Data” • What does complex mean?? • Need a more complete definition Common Definition “Data that is too large and complex to classify using traditional relational database methods” -Wikipedia Lizy K. John

  6. BPOE 2014 Some examples • Combined Space of all hard drives in 2006 • 160 exabytes • All hard drives sold by Seagate in 2011 • 300 exabytes Exa = 2^60 Zetta = 2^70 Yotta = 2^80 • The world wide web in 2013 • 4 zettabytes • NSA Utah Data Center in Snowden leaks • 5 zettabytes (some claimed it to be 1 YB) Lizy K. John

  7. BPOE 2014 Characteristics of Big Data * Not always included in taxonomy Lizy K. John

  8. BPOE 2014 Big Data Analytics = I got this in the mail the very same week my son turned 16 Lizy K. John

  9. BPOE 2014 What’s the Problem? • Deriving insights from data NOT a new problem • Traditional relational databases that contain carefully pruned and organized data • But storage is relatively cheap these days • Possible to store more data in unstructured form • Need intelligent ways to distill large amounts of data in different formats to actionable KNOWLEDGE • Many different levels to approach this problem….. Lizy K. John

  10. BPOE 2014 Big Data Stack • Algorithms • PageRank, Genetic Algorithms, SVM, etc. • Frameworks and Implementations • Map/Reduce (Hadoop), MySQL, NoSQL (Cassandra), etc • Hardware • SMT, Accelerator Nodes (Intel Phi, GPU), etc • How does workload analysis fit in? • EVERYONE BENEFITS FROM A DEEP UNDERSTANDING OF A WORKLOAD AND ITS CHARACTERISTICS! Lizy K. John

  11. BPOE 2014 Are New Benchmarks Needed? • Already have industry standard benchmarks! • Critical Question • Do Big Data workloads have different characteristics than these “traditional” Benchmarks? • Yes they do! • TLB Behavior [Wang et al] • I-Cache Behavior [Ferdman et al, Zhen et al, Wang et al] • SMT [Ferdman et al] • Operation Intensity [Wang et al] • Data Volume [Wang et al] Lizy K. John

  12. BPOE 2014 Why New Benchmarks? • I-Cache behavior from Cloudsuite [Ferdman et al] • Much higher miss rate than traditional benchmarks • Significant OS contribution to cache behavior Lizy K. John

  13. BPOE 2014 Why New Benchmarks? • OS Activity [Zhen et al] • Shows percentage of instructions • Significant variation in kernel/application dynamic instructions Lizy K. John

  14. BPOE 2014 Why New Benchmarks? • I-TLB Behavior from BigDataBench [Wang et al] • Once again, more misses than traditional benchmarks Lizy K. John

  15. BPOE 2014 Big Data Characterization Challenges INPUT GENERATION • Input data is critical! • Couple of approaches • Synthetic data generation • Questionable Veracity • Grab data from industry • Not always possible • CAIDA-like • How much data? • Feasibility vs accuracy Lizy K. John

  16. BPOE 2014 Common Big Data Domains • Databases • Structured • Typically relational data • SQL databases • Unstructured • Example: document oriented • Generally no fixed table schema • Semi-structured Lizy K. John

  17. BPOE 2014 Common Big Data Domains • Common NoSQL Databases • Cassandra • Industry leading, ultra scalable • HBase • Database built on top of Hadoop and HDFS • MongoDB • JSON- database with dynamic schema Lizy K. John

  18. BPOE 2014 Common Big Data Domains Map/Reduce - Hadoop Key/ Value computation • Map and Reduce phase Lizy K. John

  19. BPOE 2014 Common Big Data Domains • Graph Algorithms • Important for Data Mining and Machine Learning • Graphlab – essentially Hadoop over large graphs • GraphChi – web scale graph computation • Streaming graph changes • asynchronous changes to the graph  (i.e changes written to edges are immediately visible to subsequent computation) • Partitioning Challenges Lizy K. John

  20. BPOE 2014 Hierarchical Decomposition of Workloads • By dividing into functional blocks - e.g. front end, back end, and database. • By subdividing into tasks, task groups, processes, threads, etc. • By dividing considering hardware modules at microarchitectural level – memory subsystem, CPU, disk, etc. eg: consider AMD APUs • Group together tasks in an application that use data from the same rack. Lizy K. John

  21. BPOE 2014 Entropy Guided Optimizations • Partitioning Graph Workloads • How do we assign work to nodes? • Important Factors • Data Locality • Minimize Communication • Maximize Resource Utilization • Bisection bandwidth • Entropy Guided Optimization Entropy = (memory-in, memory out, #computations, …other attributes) Lizy K. John

  22. BPOE 2014 In-Memory Map/Reduce • IBM Main Memory Map Reduce (M3R) • Eliminates intermediate disk writes for Hadoop Map/Reduce Jobs • Pros • Significantly speeds up some workloads • 45x on sparse matrix mult • Cons • Data must fit in cluster memory • No failure resilience Lizy K. John

  23. BPOE 2014 Big Data Benchmarking Challenges WORKLOAD VARIETY • Ton of software stacks required • Configuration of software platform sometimes more important than workload (see next slide) • A comprehensive benchmark should feature • Offline (Batch Style Analytics) • Online (Real Time Analytics) • Seeing positive momentum here! • TPC-* -> Cloudsuite, BigDataBench, etc Lizy K. John

  24. BPOE 2014 Hadoop Case Study – Optimal Settings • What are the optimal framework settings? • Workload Dependent? • Hardware Dependent? • Just set everything to the maximum value?? • Does it matter? • How do engineers setup clusters for new platforms? • Some “rules of thumb” available, but imprecise Lizy K. John

  25. BPOE 2014 Hadoop Case Study Standard Hadoop configuration algorithm ):- hadoop_options = Google(“Best Hadoop Configuration”) launch_cluster() if (!cluster_boots || !clients_happy) { hadoop_options = Permute(hadoop_options) launch_cluster() if(!cluster_boots || !clients_happy) { options = Lookup_Options(Buddy_at_Other_Company) launch_cluster() if(!cluster_boots || !clients_happy) { options = default_options launch_cluster() } } Lizy K. John

  26. BPOE 2014 Hadoop Case Study (Mapper-Reducer Slots) 64m4r 32m4r 8m8r 16m4r 2m2r • CPU Occupancy of TeraSort for different mapper-reducer slots • Simple app, but different very different execution profile depending on configuration Lizy K. John

  27. BPOE 2014 Hadoop Case Study (Mapper-Reducer Slots) 64m4r 32m4r 8m8r 16m4r 2m2r • Memory Utilization of TeraSort for different mapper-reducer slots • Simple app, but different very different execution profile depending on configuration Lizy K. John

  28. BPOE 2014 Hadoop Case Study (Block Size) 32MB 128MB 256MB 512MB 64MB • TeraSort – Higher block size reduces total number of maps • Simple app, but different very different execution profile depending on configuration Lizy K. John

  29. BPOE 2014 Big Data Benchmarking Frameworks • Management frameworks and harnesses essential • Example: AMD SWAT • Software platform for automating the….. • creation, deployment, provisioning, execution, and data gathering of synthetic workloads on scalable clusters • Several benchmarks available • Cloudsuite • Hadoop • Graphlab • Anything you want to plugin! Lizy K. John

  30. Lizy K. John

  31. BPOE 2014 Big Data Benchmarking Challenges Big Cluster • Lots of cores, lots of memory and disk space • Hard for non-industry researchers • Prohibitively long runtimes • Can we simulate Big Data? • Requires full system simulation • Cloudsuite on Flexus(EPFL) Lizy K. John

  32. BPOE 2014 Adaptable Scalable Futuristic Benchmark Proxies • Generate Clones by setting knobs to appropriate values • Adaptable • Scalable • Futuristic Lizy K. John

  33. BPOE 2014 Abstract Workload Model Lizy K. John

  34. BPOE 2014 Big Data Synthetics? A Possibility? • Given challenges in Big Data workloads, this would be useful • But what are the knob settings for “Big Data” • Need detailed characterization Lizy K. John

  35. BPOE 2014 Big Data Workload Clones CLONES WILL AVOID COMPLEX SOFTWARE STACKS: • Clones for Hadoop • Clones for Graph Processing • Clones for DSS • Clones for OLAP • Clones for DSS with materialized views • Need detailed characterization Lizy K. John

  36. BPOE 2014 Tricks from the Old Treasure Chest • Search and Sort – • age old computer science problems • new issues raised by scale but • Old OLTP, OLAP and DSS • Combination of HPC and Database Ideas • Old Scatter-Gather • Piece-wise modeling Lizy K. John

  37. BPOE 2014 Conclusion • Big Data is here to stay • Increasingly important • Cloud and Big Data will take the world in unprecedented ways • Appropriate hardware and software need to be developed • Workload metrics to guide partitioning • Need to act now to develop intelligent benchmarks and workload analysis methodology Lizy K. John

  38. BPOE 2014 Thank You! Questions? Laboratory for Computer Architecture (LCA) The University of Texas at Austin lca.ece.utexas.edu Lizy K. John

  39. References [1] M. Ferdman, et. al.. 2012. Clearing the clouds: a study of emerging scale-out workloads on modern hardware.SIGARCH Comput. Archit. News 40, 1 (March 2012), 37-48. [2] Zhen Jia, Lei Wang, Jianfeng Zhan Lixin Zhang, Chunjie Luo. Characterizing Data Analysis Workloads in Data Centers. In Workload Characterization (IISWC), 2013 IEEE International Symposium on. IEEE. [3] Lei Wang, Jianfeng Zhan, Chunjie Luo, Yuqing Zhu, Qiang Yang, Yongqiang He, Wanling Gao, Zhen Jia, Yingjie Shi, Shujie Zhang, Cheng Zhen, Gang Lu, Kent Zhan, Xiaona Li, and Bizhu Qiu. The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, USA. [4] Huang, Shengsheng, et al. "The HiBench benchmark suite: Characterization of the MapReduce-based data analysis." Data Engineering Workshops (ICDEW), 2010 IEEE 26th International Conference on. IEEE, 2010. [5] Cooper, Brian F., et al. "Benchmarking cloud serving systems with YCSB."Proceedings of the 1st ACM symposium on Cloud computing. ACM, 2010. [6] GridMix [Online]. Available: https://hadoop.apache.org/docs/r1.2.1/gridmix.html. (21.10.2013). [7] PigMix [Online]. Available: https://cwiki.apache.org/confluence/display/PIG/PigMix.(21.10.2013). [8] PAVLO, A., PAULSON, E., RASIN, A., ABADI, D.J., DEWITT, D.J., MADDEN, S., and STONEBRAKER, M., 2009. A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data ACM, 165-178. [9] Transaction Processing Performance Council (Online) http://www.tpc.org/default.asp (02-13-2013) [10] GHAZAL, A., RABL, T., HU, M., RAAB, F., POESS, M., CROLOTTE, A., and JACOBSEN, H.-A., 2013. BigBench: Towards an Industry Standard Benchmark for Big Data Analytics. In SIGMOD ACM, New York, New York, 2013, 197-1208. [11] SUMBALY, R., KREPS, J., and SHAH, S., 2013. Linkbench: a database benchmark based on the Facebook social graph In Proceedings of the SIGMOD (New York, New Youk, USA2013), ACM, 1185-1196. [12] Cloudsuite on Flexus[Online]. http://parsa.epfl.ch/cloudsuite/isca12-tutorial.html (02-13-2013). ISCA 2012 Tutorial [13] Graphlab [Online]. Available: http://graphlab.com/). [14] Shinnar, A., Cunningham, D., Saraswat, V., & Herta, B. (2012). M3R: increased performance for in-memory Hadoop jobs. Proceedings of the VLDB Endowment,5(12), 1736-1747. [15] Nambiar, Raghunath Othayoth, and Meikel Poess. "The making of TPC-DS."Proceedings of the 32nd international conference on Very large data bases. VLDB Endowment, 2006. [16] Breternitz, Mauricio, et al. "Cloud Workload Analysis with SWAT." Computer Architecture and High Performance Computing (SBAC-PAD), 2012 IEEE 24th International Symposium on. IEEE, 2012. Lizy K. John

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