Gearing Hadoop towards HPC sytems

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1 Gearing Hadoop towards HPC sytems Xuanhua Shi Huazhong University of Science and Technology HPC Advisory Council China Workshop 2014, Guangzhou

2 2014/11/5 2 Internet Internet Traffic Web Social Networks 8 Billion pages Typical router: 42 bytes/second 3.5 Gigabytes/day Mobile Apps

3 2014/11/5 3 Hadoop a well- known processing platform for large data sets and widely used in many domains Hadoop Yahoo Hadoop Facebook Hadoop Twitter Hadoop Linkedin Hadoop Alibaba

4 2014/11/5 4 Data processing on HPC IDC Report, 25 Oct 2013 [1] High Performance Data Analysis Report 67% of the sites in the 2013 study said they perform Big Data analysis on their HPC systems, with 30% of the available computing cycles devoted on average to Big Data analysis work. IDC forecasts that revenue for high performance data analysis (HPDA) servers will grow robustly during the forecast period [1] New idc worldwide hpc end-user study identifies latest trends in high performance computing usage and spending. com/getdoc.jsp?containerid=prus

5 2014/11/5 5 Hadoop on HPC How HPC is Hacking Hadoop, HPC wire, 2014, by Nicole Hemsoth Utilizing HPC resource for Data-intensive Computing (e.g., UCSD Gordon) Hadoop/MapReduce in bioinformatics Data intensive app on HDFS molecular dynamics simulation utilizing Hadoop Hadoop for remote sensing analysis Genome resequencing using Hadoop Hadoop and MPI, e.g., Data-MPI Others Hadoop and HPC storage systems

6 2014/11/5 6 Take a look at the Hadoop on HPC 320 GB data set, Wordcount 17 nodes, each node is equipped with two 8-core 2.6GHz Intel(R) Xeon(R) E CPUs, 32GB memory and a 300GB 10,000RPM SAS disk

7 2014/11/5 7 Typical Hadoop cluster Hadoop [1] 20 servers /rack cpu:intel(r) Xeon(R) CPU E5645 (Dual 6-core) Disk: 12 * 2TB HDD Mem: 72 GB Network: 2 * 1Gb Ethernet Standard 2U server [1]

8 2014/11/5 8 Typical Hadoop cluster Hadoop [1] Over 10,000 nodes cpu:intel(r) Xeon(R) CPU E5645 * 4 Disk numbers: 12 Mem: 48GB [1]

9 2014/11/5 9 Typical Hadoop cluster Hadoop [1] 4400 nodes cpu:over 100,000 cores Memory: 275 TB Disk: 100PB (52800 disks) [1]

10 2014/11/5 10 Typical HPC cluster Name Number of cores Mem(GB) Mem/core(GB/core) Tianhe-2 3,120,000 1,024, K computer 705,024 1,410,048 2 Stampede 462, , SuperMUC 155, , TSUBAME 74,358 74,358 1

11 2014/11/5 11 The Hadoop execution engine 1.I/O operations 3.I/O operations 2.I/O operations I/O wait 5.I/O operations 6.I/O operations 7.I/O operations 4.I/O operations

12 2014/11/5 12 Mammoth: gearing Hadoop on HPC [1] a multi-threaded execution engine. The engine is based on Hadoop, but it runs in a single JVM on each node. global memory management a novel rule-based heuristic to prioritize memory allocation and revocation among execution units (mapper, shuffler, reducer etc), to maximize the holistic benefits of the Map/Reduce job when scheduling each memory unit disk accesses serialization, multi-cache, shuffling from memory solved the problem of full Garbage Collection (GC) in the JVM [1]X. Shi, et al. Mammoth: Gearing Hadoop towards Memory-intensive applications, TPDS, 2014

13 2014/11/5 13 Architecture of Mammoth

14 2014/11/5 14 Mammoth: Map phase Reader input split HDFS get Cache Pool Mapper MSorter Merger get Element Pool Element Queue return cahce file cahce file final cache file Sort Buffer Send Buffer I/O Scheduler

15 2014/11/5 15 Shuffle phase Push Model return Cache Pool get final cache file Send Buffer Sender spill network spill I/O Scheduler Spiller Reader Reciever MergeSort read Receive Buffer Disk

16 2014/11/5 16 Reduce phase RSorter Reducer Pusher Receive Buffer read return I/O Scheduler Writer Reader get Cache Pool HDFS Disk Pipeline

17 2014/11/5 17 Memory usage types Map Phase Element Pool Map Phase and Reduce Phase Sort Buffer Memory usage Cache Pool Send Buffer Pr Multi-buffer Receive Buffer Map Phase Shuffle Phase

18 2014/11/5 18 Memory management

19 2014/11/5 19 Memory management Map Shuffle Reduce t 1 t 2 t 3 t 4 t 5 time Sort Buffer>Send Buffer>Receive Buffer

20 2014/11/5 20 I/O scheduling Multi-buffer

21 2014/11/5 21 Evaluations -Intermediate Data Size Time(1000s) Mammoth for WCC Hadoop for WCC Mammoth for Sort Hadoop for Sort Mammoth for WC Hadoop for WC Total Map Shuffle Reduce Job execution time in different phases

22 /11/5 22 Evaluations available memory completion time(1000s) memory(gb) mammoth hadoop

23 2014/11/5 23 Evaluations Real applications CloudBurst in bioinformatics implementation of the RMAP algorithm for short-read gene alignment Graph computing Pegaus: Peta Graph Mining library computing the diameter of the graph Concmpt: an application for connected component detecting Radius: an application for graph radius computing DegDist: an application for vertex degree counting PageRank:widely used by the search engines to rank web pages

24 2014/11/5 24 Evaluations Real applications

25 vinced, 2014/11/5 we optimize the Spark s software configuration parameters 25 Fig. 1. Performance comparison with WordCount uments ( We set the r to be org.apache.spark.serializer.kryoserializer Does Figure spark 1 illustrates run the well performances on HPC? of the three and systems set the in the f nsolidatefiles see that to Mammoth be true. and Spark obtain almost the same performance, wh speedup compared with Hadoop. The three systems will aggregate the same way that the <key, value> pairs with the same key will be summed pair. For example, one <word, 1> and one <word, 1> will be transformed way, the quantity of the intermediate data will be reduced heavily, and th sufficient. In Mammoth, most of the intermediate data is processed spilling the Map tasks results to disks for fault tolerance. Spark processes and uses the hash table other than the sort to implement data aggregat 0 the memory better than Hadoop, which is the reason for their performan Hadoop Mammoth Spark. 1. Performance comparison with WordCount 8000 execution time(s) 800 execution time(s) 6000 performances of the three systems in the first experiment. We can rk obtain almost the same performance, 4000which is more or less a 1.7x doop. The three systems will aggregate the intermediate data in the 2000 lue> pairs with the same key will be summed up to one <key, value> rd, 1> and one <word, 1> will be transformed 0 to one <word, 2>. In this Hadoop Mammoth Spark ermediate data will be reduced heavily, and the memory will be rather

26 2014/11/5 26 If you are interested Open-source: Project: Source code: The patch at Apache Software Foundation: Publications and technical report are available on the project website

27 2014/11/5 27 Thanks!

Original citation: Shi, Xuanhua, Chen, Ming, He, Ligang, Xie, Xu, Lu, Lu, Jin, Hai, Chen, Yong and Wu, Song. (214) Mammoth : gearing hadoop towards memory-intensive MapReduce applications. IEEE Transactions

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