Accelerating Big Data Processing with Hadoop, Spark and Memcached

Accelerating Big Data Processing with Hadoop, Spark and Memcached Talk at HPC Advisory Council Switzerland Conference (Mar 15) by Dhabaleswar K. (DK) Panda The Ohio State University E-mail: panda@cse.ohio-state.edu http://www.cse.ohio-state.edu/~panda

Introduction to Big Data Applications and Analytics Big Data has become the one of the most important elements of business analytics Provides groundbreaking opportunities for enterprise information management and decision making The amount of data is exploding; companies are capturing and digitizing more information than ever The rate of information growth appears to be exceeding Moore s Law Commonly accepted 3V s of Big Data Volume, Velocity, Variety Michael Stonebraker: Big Data Means at Least Three Different Things, http://www.nist.gov/itl/ssd/is/upload/nist-stonebraker.pdf 5V s of Big Data 3V + Value, Veracity 2

Data Management and Processing on Modern Clusters Substantial impact on designing and utilizing modern data management and processing systems in multiple tiers Front-end data accessing and serving (Online) Memcached + DB (e.g. MySQL), HBase Back-end data analytics (Offline) HDFS, MapReduce, Spark 3

Overview of Apache Hadoop Architecture Open-source implementation of Google MapReduce, GFS, and BigTable for Big Data Analytics Hadoop Common Utilities (RPC, etc.), HDFS, MapReduce, YARN http://hadoop.apache.org Hadoop 1.x Hadoop 2.x MapReduce (Data Processing) Other Models (Data Processing) MapReduce (Cluster Resource Management & Data Processing) YARN (Cluster Resource Management & Job Scheduling) Hadoop Distributed File System (HDFS) Hadoop Distributed File System (HDFS) Hadoop Common/Core (RPC,..) Hadoop Common/Core (RPC,..) 4

Spark Architecture Overview An in-memory data-processing framework Worker Iterative machine learning jobs Interactive data analytics Scala based Implementation Zookeeper Worker Standalone, YARN, Mesos Scalable and communication intensive Wide dependencies between Resilient Distributed Datasets (RDDs) Driver SparkContext Master Worker Worker Worker HDFS MapReduce-like shuffle operations to repartition RDDs Sockets based communication http://spark.apache.org 5

Memcached Architecture Main memory SSD CPUs HDD... Main memory SSD CPUs HDD!"#$%"$#& Internet High Performance Networks High Performance Networks Main memory SSD CPUs HDD High Performance Networks Main memory SSD CPUs HDD...... Main memory CPUs (Database Servers) SSD HDD Web Frontend Servers (Memcached Clients) (Memcached Servers) Three-layer architecture of Web 2.0 Web Servers, Memcached Servers, Database Servers Distributed Caching Layer Allows to aggregate spare memory from multiple nodes General purpose Typically used to cache database queries, results of API calls Scalable model, but typical usage very network intensive 6

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for RDMA-Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 7

Drivers for Modern HPC Clusters High End Computing (HEC) is growing dramatically High Performance Computing Big Data Computing Technology Advancement Multi-core/many-core technologies and accelerators Remote Direct Memory Access (RDMA)-enabled networking (InfiniBand and RoCE) Solid State Drives (SSDs) and Non-Volatile Random- Access Memory (NVRAM) Accelerators (NVIDIA GPGPUs and Intel Xeon Phi) Tianhe 2 Titan Stampede Tianhe 1A 8

Interconnects and Protocols in the Open Fabrics Stack Application / Middleware Interface Application / Middleware Sockets Verbs Protocol Kernel Space TCP/IP TCP/IP RSockets SDP TCP/IP RDMA RDMA Ethernet Driver IPoIB Hardware Offload User Space RDMA User Space User Space User Space Adapter Ethernet Adapter InfiniBand Adapter Ethernet Adapter InfiniBand Adapter InfiniBand Adapter iwarp Adapter RoCE Adapter InfiniBand Adapter Switch Ethernet Switch InfiniBand Switch Ethernet Switch InfiniBand Switch InfiniBand Switch Ethernet Switch Ethernet Switch InfiniBand Switch 1/10/40 GigE IPoIB 10/40 GigE- TOE RSockets SDP iwarp RoCE IB Native 9

Wide Adoption of RDMA Technology Message Passing Interface (MPI) for HPC Parallel File Systems Lustre GPFS Delivering excellent performance: < 1.0 microsec latency 100 Gbps bandwidth 5-10% CPU utilization Delivering excellent scalability 10

Challenges in Designing Communication and I/O Libraries for Big Data Systems Applications Benchmarks Big Data Middleware (HDFS, MapReduce, HBase, Spark and Memcached) Upper level Changes? Programming Models (Sockets) Other RDMA Protocols? Point-to-Point Communication Communication and I/O Library Threaded Models and Synchronization Virtualization I/O and File Systems QoS Fault-Tolerance Networking Technologies (InfiniBand, 1/10/40GigE and Intelligent NICs) Commodity Computing System Architectures (Multi- and Many-core architectures and accelerators) Storage Technologies (HDD and SSD) 11

Can Big Data Processing Systems be Designed with High- Performance Networks and Protocols? Current Design Application Our Approach Application Sockets OSU Design Verbs Interface 1/10 GigE Network 10 GigE or InfiniBand Sockets not designed for high-performance Stream semantics often mismatch for upper layers Zero-copy not available for non-blocking sockets 12

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for RDMA-Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 13

Overview of the HiBD Project and Releases RDMA for Apache Hadoop 2.x (RDMA-Hadoop-2.x) RDMA for Apache Hadoop 1.x (RDMA-Hadoop) RDMA for Memcached (RDMA-Memcached) OSU HiBD-Benchmarks (OHB) http://hibd.cse.ohio-state.edu Users Base: 95 organizations from 18 countries More than 2,900 downloads RDMA for Apache HBase and Spark will be available in near future 14

RDMA for Apache Hadoop 2.x Distribution High-Performance Design of Hadoop over RDMA-enabled Interconnects High performance design with native InfiniBand and RoCE support at the verbs-level for HDFS, MapReduce, and RPC components Enhanced HDFS with in-memory and heterogeneous storage High performance design of MapReduce over Lustre Easily configurable for different running modes (HHH, HHH-M, HHH-L, and MapReduce over Lustre) and different protocols (native InfiniBand, RoCE, and IPoIB) Current release: 0.9.6 Based on Apache Hadoop 2.6.0 Compliant with Apache Hadoop 2.6.0 APIs and applications Tested with Mellanox InfiniBand adapters (DDR, QDR and FDR) RoCE support with Mellanox adapters Various multi-core platforms Different file systems with disks and SSDs and Lustre http://hibd.cse.ohio-state.edu 15

RDMA for Memcached Distribution High-Performance Design of Memcached over RDMA-enabled Interconnects High performance design with native InfiniBand and RoCE support at the verbslevel for Memcached and libmemcached components Easily configurable for native InfiniBand, RoCE and the traditional sockets-based support (Ethernet and InfiniBand with IPoIB) High performance design of SSD-Assisted Hybrid Memory Current release: 0.9.3 Based on Memcached 1.4.22 and libmemcached 1.0.18 Compliant with libmemcached APIs and applications Tested with Mellanox InfiniBand adapters (DDR, QDR and FDR) RoCE support with Mellanox adapters Various multi-core platforms SSD http://hibd.cse.ohio-state.edu 16

OSU HiBD Micro-Benchmark (OHB) Suite - Memcached Released in OHB 0.7.1 (ohb_memlat) Evaluates the performance of stand-alone Memcached Three different micro-benchmarks SET Micro-benchmark: Micro-benchmark for memcached set operations GET Micro-benchmark: Micro-benchmark for memcached get operations MIX Micro-benchmark: Micro-benchmark for a mix of memcached set/get operations (Read:Write ratio is 90:10) Calculates average latency of Memcached operations Can measure throughput in Transactions Per Second 17

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 18

Acceleration Case Studies and In-Depth Performance Evaluation RDMA-based Designs and Performance Evaluation HDFS MapReduce Spark 19

Design Overview of HDFS with RDMA Applications Design Features Others HDFS Write RDMA-based HDFS write RDMA-based HDFS replication Java Socket Interface Java Native Interface (JNI) Parallel replication support OSU Design On-demand connection setup 1/10 GigE, IPoIB Network Verbs RDMA Capable Networks (IB, 10GE/ iwarp, RoCE..) InfiniBand/RoCE support Enables high performance RDMA communication, while supporting traditional socket interface JNI Layer bridges Java based HDFS with communication library written in native code 20

Communication Time (s) Communication Times in HDFS 25 20 10GigE IPoIB (QDR) OSU-IB (QDR) 15 10 Reduced by 30% 5 0 Cluster with HDD DataNodes 2GB 4GB 6GB 8GB 10GB File Size (GB) 30% improvement in communication time over IPoIB (QDR) 56% improvement in communication time over 10GigE Similar improvements are obtained for SSD DataNodes N. S. Islam, M. W. Rahman, J. Jose, R. Rajachandrasekar, H. Wang, H. Subramoni, C. Murthy and D. K. Panda, High Performance RDMA-Based Design of HDFS over InfiniBand, Supercomputing (SC), Nov 2012 N. Islam, X. Lu, W. Rahman, and D. K. Panda, SOR-HDFS: A SEDA-based Approach to Maximize Overlapping in RDMA-Enhanced HDFS, HPDC '14, June 2014 21

Enhanced HDFS with In-memory and Heterogeneous Storage Hybrid Replication Applications Triple-H Data Placement Policies Eviction/ Promotion Heterogeneous Storage RAM Disk SSD HDD Lustre Design Features Three modes Default (HHH) In-Memory (HHH-M) Lustre-Integrated (HHH-L) Policies to efficiently utilize the heterogeneous storage devices RAM, SSD, HDD, Lustre Eviction/Promotion based on data usage pattern Hybrid Replication Lustre-Integrated mode: Lustre-based fault-tolerance N. Islam, X. Lu, M. W. Rahman, D. Shankar, and D. K. Panda, Triple-H: A Hybrid Approach to Accelerate HDFS on HPC Clusters with Heterogeneous Storage Architecture, CCGrid 15, May 2015 22

Enhanced HDFS with In-memory and Heterogeneous Storage Three Modes HHH (default): Heterogeneous storage devices with hybrid replication schemes I/O operations over RAM disk, SSD, and HDD Hybrid replication (in-memory and persistent storage) Better fault-tolerance as well as performance HHH-M: High-performance in-memory I/O operations Memory replication (in-memory only with lazy persistence) As much performance benefit as possible HHH-L: Lustre integrated Take advantage of the Lustre available in HPC clusters Lustre-based fault-tolerance (No HDFS replication) Reduced local storage space usage 23

Total Throughput (MBps) Execution Time (s) Performance Improvement on TACC Stampede (HHH) 6000 5000 IPoIB (FDR) OSU-IB (FDR) Increased by 7x Increased by 2x 300 250 IPoIB (FDR) OSU-IB (FDR) Reduced by 3x 4000 3000 2000 1000 200 150 100 50 0 0 write read TestDFSIO 8:30 16:60 32:120 Cluster Size:Data Size RandomWriter For 160GB TestDFSIO in 32 nodes For 120GB RandomWriter in 32 Write Throughput: 7x improvement over IPoIB (FDR) nodes 3x improvement over IPoIB (QDR) Read Throughput: 2x improvement over IPoIB (FDR) 24

Execution Time (s) Performance Improvement on SDSC Gordon (HHH-L) 500 450 400 350 300 250 200 150 100 50 0 HDFS-IPoIB (QDR) Lustre-IPoIB (QDR) OSU-IB (QDR) 20 40 60 Data Size (GB) Sort For 60GB Sort in 8 nodes Reduced by 54% 24% improvement over default HDFS 54% improvement over Lustre 33% storage space saving compared to default HDFS Storage Used (GB) HDFS-IPoIB (QDR) Lustre-IPoIB (QDR) 360 120 OSU-IB (QDR) 240 Storage space for 60GB Sort 25

Execution Time (s) Evaluation with PUMA and CloudBurst (HHH-L/HHH) 2500 2000 1500 Reduced by 17% HDFS-IPoIB (QDR) Lustre-IPoIB (QDR) OSU-IB (QDR) HDFS-IPoIB (FDR) OSU-IB (FDR) 1000 Reduced by 29.5% 60.24 s 48.3 s 500 0 SequenceCount PUMA PUMA on OSU RI Grep SequenceCount with HHH-L: 17% benefit over Lustre, 8% over HDFS Grep with HHH: 29.5% benefit over Lustre, 13.2% over HDFS CloudBurst CloudBurst on TACC Stampede With HHH: 19% improvement over HDFS 26

Acceleration Case Studies and In-Depth Performance Evaluation RDMA-based Designs and Performance Evaluation HDFS MapReduce Spark 27

Design Overview of MapReduce with RDMA Java Socket Interface MapReduce Job Tracker 1/10 GigE, IPoIB Network Applications Task Tracker Map Reduce Java Native Interface (JNI) OSU Design Verbs RDMA Capable Networks (IB, 10GE/ iwarp, RoCE..) Design Features RDMA-based shuffle Prefetching and caching map output Efficient Shuffle Algorithms In-memory merge On-demand Shuffle Adjustment Advanced overlapping map, shuffle, and merge shuffle, merge, and reduce On-demand connection setup InfiniBand/RoCE support Enables high performance RDMA communication, while supporting traditional socket interface JNI Layer bridges Java based MapReduce with communication library written in native code 28

Advanced Overlapping among different phases Default Architecture A hybrid approach to achieve maximum possible overlapping in MapReduce across all phases compared to other approaches Efficient Shuffle Algorithms Dynamic and Efficient Switching On-demand Shuffle Adjustment Enhanced Overlapping Advanced Overlapping M. W. Rahman, X. Lu, N. S. Islam, and D. K. Panda, HOMR: A Hybrid Approach to Exploit Maximum Overlapping in MapReduce over High Performance Interconnects, ICS, June 2014. 29

Job Execution Time (sec) Job Execution Time (sec) Performance Evaluation of Sort and TeraSort 1200 1000 800 IPoIB (QDR) UDA-IB (QDR) OSU-IB (QDR) Reduced by 40% 700 600 500 IPoIB (FDR) UDA-IB (FDR) OSU-IB (FDR) Reduced by 38% 600 400 200 0 Data Size: 60 GB Data Size: 120 GB Data Size: 240 GB Cluster Size: 16 Cluster Size: 32 Cluster Size: 64 Sort in OSU Cluster For 240GB Sort in 64 nodes (512 cores) 40% improvement over IPoIB (QDR) with HDD used for HDFS 400 300 200 100 0 Data Size: 80 GB Data Size: 160 GBData Size: 320 GB Cluster Size: 16 Cluster Size: 32 Cluster Size: 64 TeraSort in TACC Stampede For 320GB TeraSort in 64 nodes (1K cores) 38% improvement over IPoIB (FDR) with HDD used for HDFS 30

Normalized Execution Time Evaluations using PUMA Workload 1 10GigE IPoIB (QDR) OSU-IB (QDR) Reduced by 50% Reduced by 49% 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 AdjList (30GB) SelfJoin (80GB) SeqCount (30GB) WordCount (30GB) InvertIndex (30GB) Benchmarks 50% improvement in Self Join over IPoIB (QDR) for 80 GB data size 49% improvement in Sequence Count over IPoIB (QDR) for 30 GB data size 31

Optimize Hadoop YARN MapReduce over Parallel File Systems Compute Nodes App Master Map Reduce Lustre Client MetaData Servers Object Storage Servers Lustre Setup HPC Cluster Deployment Hybrid topological solution of Beowulf architecture with separate I/O nodes Lean compute nodes with light OS; more memory space; small local storage Sub-cluster of dedicated I/O nodes with parallel file systems, such as Lustre MapReduce over Lustre Local disk is used as the intermediate data directory Lustre is used as the intermediate data directory 32

Design Overview of Shuffle Strategies for MapReduce over Lustre Map 1 Map 2 Map 3 In-memory merge/sort Intermediate Data Directory Lustre Lustre Read / RDMA Reduce 1 Reduce 2 reduce In-memory merge/sort reduce Design Features Two shuffle approaches Lustre read based shuffle RDMA based shuffle Hybrid shuffle algorithm to take benefit from both shuffle approaches Dynamically adapts to the better shuffle approach for each shuffle request based on profiling values for each Lustre read operation In-memory merge and overlapping of different phases are kept similar to RDMAenhanced MapReduce design M. W. Rahman, X. Lu, N. S. Islam, R. Rajachandrasekar, and D. K. Panda, High Performance Design of YARN MapReduce on Modern HPC Clusters with Lustre and RDMA, IPDPS, May 2015. 33

Job Execution Time (sec) Job Execution Time (sec) Performance Improvement of MapReduce over Lustre on TACC-Stampede 1200 1000 800 Local disk is used as the intermediate data directory Reduced by 44% IPoIB (FDR) OSU-IB (FDR) 500 450 400 350 300 IPoIB (FDR) OSU-IB (FDR) Reduced by 48% 600 250 200 400 150 200 100 50 0 300 400 500 0 20 GB 40 GB 80 GB 160 GB 320 GB 640 GB Data Size (GB) Cluster: 4 Cluster: 8 Cluster: 16 Cluster: 32 Cluster: 64 Cluster: 128 For 500GB Sort in 64 nodes For 640GB Sort in 128 nodes 44% improvement over IPoIB (FDR) 48% improvement over IPoIB (FDR) M. W. Rahman, X. Lu, N. S. Islam, R. Rajachandrasekar, and D. K. Panda, MapReduce over Lustre: Can RDMAbased Approach Benefit?, Euro-Par, August 2014. 34

Job Execution Time (sec) Job Execution Time (sec) Case Study - Performance Improvement of MapReduce over Lustre on SDSC-Gordon Lustre is used as the intermediate data directory 900 800 700 600 IPoIB (QDR) OSU-Lustre-Read (QDR) OSU-RDMA-IB (QDR) OSU-Hybrid-IB (QDR) Reduced by 34% 900 800 700 600 IPoIB (QDR) OSU-Lustre-Read (QDR) OSU-RDMA-IB (QDR) OSU-Hybrid-IB (QDR) Reduced by 25% 500 500 400 400 300 300 200 200 100 100 0 40 60 80 Data Size (GB) 0 40 80 120 Data Size (GB) For 80GB Sort in 8 nodes For 120GB TeraSort in 16 nodes 34% improvement over IPoIB (QDR) 25% improvement over IPoIB (QDR) 35

Acceleration Case Studies and In-Depth Performance Evaluation RDMA-based Designs and Performance Evaluation HDFS MapReduce Spark 36

Design Overview of Spark with RDMA Design Features RDMA based shuffle SEDA-based plugins Dynamic connection management and sharing Non-blocking and out-oforder data transfer Off-JVM-heap buffer management InfiniBand/RoCE support Enables high performance RDMA communication, while supporting traditional socket interface JNI Layer bridges Scala based Spark with communication library written in native code X. Lu, M. W. Rahman, N. Islam, D. Shankar, and D. K. Panda, Accelerating Spark with RDMA for Big Data Processing: Early Experiences, Int'l Symposium on High Performance Interconnects (HotI'14), August 2014 37

GroupBy Time (sec) GroupBy Time (sec) Preliminary Results of Spark-RDMA Design - GroupBy 9 8 7 6 Reduced by 18% 9 8 7 6 10GigE IPoIB RDMA Reduced by 20% 5 5 4 4 3 3 2 2 1 1 0 4 6 8 10 Data Size (GB) 0 8 12 16 20 Data Size (GB) Cluster with 4 HDD Nodes, GroupBy with 32 cores Cluster with 8 HDD Nodes, GroupBy with 64 cores Cluster with 4 HDD Nodes, single disk per node, 32 concurrent tasks 18% improvement over IPoIB (QDR) for 10GB data size Cluster with 8 HDD Nodes, single disk per node, 64 concurrent tasks 20% improvement over IPoIB (QDR) for 20GB data size 38

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 39

Total Throughput (MBps) Execution Time (s) Performance Benefits TestDFSIO in TACC Stampede (HHH) 8000 7000 6000 5000 300 Increased by 7.8x IPoIB (FDR) IPoIB (FDR) Reduced by 2.5x OSU-IB (FDR) 250 OSU-IB (FDR) 200 4000 3000 2000 1000 0 150 100 50 0 80 100 120 80 100 120 Data Size (GB) Data Size (GB) Cluster with 32 Nodes with HDD, TestDFSIO with a total 128 maps For Throughput, 6-7.8x improvement over IPoIB for 80-120 GB file size For Latency, 2.5-3x improvement over IPoIB for 80-120 GB file size 40

Execution Time (s) Execution Time (s) Performance Benefits RandomWriter & TeraGen in TACC-Stampede (HHH) 250 200 IPoIB (FDR) OSU-IB (FDR) Reduced by 3x 250 200 IPoIB (FDR) OSU-IB (FDR) Reduced by 4x 150 150 100 100 50 50 0 0 80 100 120 80 100 120 Data Size (GB) Data Size (GB) RandomWriter Cluster with 32 Nodes with a total of 128 maps TeraGen RandomWriter 3-4x improvement over IPoIB for 80-120 GB file size TeraGen 4-5x improvement over IPoIB for 80-120 GB file size 41

Execution Time (s) Execution Time (s) 900 800 700 600 Performance Benefits Sort & TeraSort in TACC-Stampede (HHH) IPoIB (FDR) OSU-IB (FDR) 600 500 400 IPoIB (FDR) Reduced by 52% Reduced by 44% OSU-IB (FDR) 500 400 300 300 200 200 100 100 0 80 100 120 Data Size (GB) 0 80 100 120 Data Size (GB) Cluster with 32 Nodes with a total of 128 maps and 57 reduces Cluster with 32 Nodes with a total of 128 maps and 64 reduces Sort with single HDD per node 40-52% improvement over IPoIB for 80-120 GB data TeraSort with single HDD per node 42-44% improvement over IPoIB for 80-120 GB data 42

Total Throughput (MBps) Total Throughput (MBps) Performance Benefits TestDFSIO on TACC Stampede and SDSC Gordon (HHH-M) 30000 25000 IPoIB (FDR) OSU-IB (FDR) Increased by 28x 9000 8000 7000 IPoIB (QDR) OSU-IB (QDR) Increased by 6x 20000 15000 10000 5000 0 60 80 100 Data Size (GB) 6000 5000 4000 3000 2000 1000 0 60 80 100 Data Size (GB) TACC Stampede Cluster with 32 Nodes with HDD, TestDFSIO with a total 128 maps TestDFSIO Write on TACC Stampede 28x improvement over IPoIB for 60-100 GB file size SDSC Gordon Cluster with 16 Nodes with SSD, TestDFSIO with a total 64 maps TestDFSIO Write on SDSC Gordon 6x improvement over IPoIB for 60-100 GB file size 43

Total Throughput (MBps) Execution Time (s) Performance Benefits TestDFSIO and Sort in SDSC-Gordon (HHH-L) 16000 14000 12000 10000 8000 6000 4000 2000 0 HDFS Lustre OSU-IB Increased by 9x Increased by 29% TestDFSIO TestDFSIO for 80GB data size Write: 9x improvement over HDFS Read: 29% improvement over Lustre Write Read 500 450 400 350 300 250 200 150 100 50 0 Cluster with 16 Nodes Sort HDFS OSU-IB Lustre 40 60 80 Data Size (GB) up to 28% improvement over HDFS up to 50% improvement over Lustre Sort Reduced by 50% 44

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 45

Memcached-RDMA Design Sockets Client RDMA Client 1 2 1 2 Master Thread Sockets Worker Thread Verbs Worker Thread Sockets Worker Thread Verbs Worker Thread Shared Data Memory Slabs Items Server and client perform a negotiation protocol Master thread assigns clients to appropriate worker thread Once a client is assigned a verbs worker thread, it can communicate directly and is bound to that thread All other Memcached data structures are shared among RDMA and Sockets worker threads Native IB-verbs-level Design and evaluation with Server : Memcached (http://memcached.org) Client : libmemcached (http://libmemcached.org) Different networks and protocols: 10GigE, IPoIB, native IB (RC, UD) 46

1 2 4 8 16 32 64 128 256 512 1K 2K 4K Time (us) Thousands of Transactions per Second (TPS) 1000 100 Memcached Performance (FDR Interconnect) 10 Memcached GET Latency OSU-IB (FDR) IPoIB (FDR) Latency Reduced by nearly 20X 700 600 500 400 300 200 Memcached Throughput 2X 1 Message Size Memcached Get latency Experiments on TACC Stampede (Intel SandyBridge Cluster, IB: FDR) 4 bytes OSU-IB: 2.84 us; IPoIB: 75.53 us 2K bytes OSU-IB: 4.49 us; IPoIB: 123.42 us Memcached Throughput (4bytes) 4080 clients OSU-IB: 556 Kops/sec, IPoIB: 233 Kops/s Nearly 2X improvement in throughput 100 0 16 32 64 128 256 512 1024 2048 4080 No. of Clients 47

Latency (sec) Throughput (Kq/s) Micro-benchmark Evaluation for OLDP workloads 8 7 6 5 4 3 2 1 0 Memcached-IPoIB (32Gbps) Reduced by 66% 3500 Memcached-RDMA (32Gbps) 3000 2500 2000 1500 1000 500 0 64 96 128 160 320 400 No. of Clients Memcached-IPoIB (32Gbps) Memcached-RDMA (32Gbps) 64 96 128 160 320 400 No. of Clients Illustration with Read-Cache-Read access pattern using modified mysqlslap load testing tool Memcached-RDMA can - improve query latency by up to 66% over IPoIB (32Gbps) - throughput by up to 69% over IPoIB (32Gbps) D. Shankar, X. Lu, J. Jose, M. W. Rahman, N. Islam, and D. K. Panda, Can RDMA Benefit On-Line Data Processing Workloads with Memcached and MySQL, ISPASS 15 48

Average latency (us) Throughput (million trans/sec) Performance Benefits on SDSC-Gordon OHB Latency & Throughput Micro-Benchmarks 500 400 300 200 100 0 IPoIB (32Gbps) RDMA-Mem (32Gbps) RDMA-Hyb (32Gbps) 10 9 8 7 6 5 4 3 2 1 0 IPoIB (32Gbps) RDMA-Mem (32Gbps) RDMA-Hybrid (32Gbps) 2X 64 128 256 512 1024 Message Size (Bytes) No. of Clients ohb_memlat & ohb_memthr latency & throughput micro-benchmarks Memcached-RDMA can - improve query latency by up to 70% over IPoIB (32Gbps) - improve throughput by up to 2X over IPoIB (32Gbps) - No overhead in using hybrid mode when all data can fit in memory 49

Latency with penalty (us) Success Rate Performance Benefits on OSU-RI-SSD OHB Micro-benchmark for Hybrid Memcached 1000 800 RDMA-Mem (32Gbps) RDMA-Hybrid (32Gbps) 600 53% 400 200 0 512 2K 8K 32K 128K Message Size (Bytes) 125 RDMA-Mem-Uniform 115 RDMA-Hybrid 105 95 85 75 65 55 1 1.15 1.3 1.45 Spill Factor ohb_memhybrid Uniform Access Pattern, single client and single server with 64MB Success Rate of In-Memory Vs. Hybrid SSD-Memory for different spill factors 100% success rate for Hybrid design while that of pure In-memory degrades Average Latency with penalty for In-Memory Vs. Hybrid SSD-Assisted mode for spill factor 1.5. up to 53% improvement over In-memory with server miss penalty as low as 1.5 ms 50

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 51

HBase-RDMA Design Overview Applications HBase Java Socket Interface Java Native Interface (JNI) OSU-IB Design IB Verbs 1/10 GigE, IPoIB Networks RDMA Capable Networks (IB, 10GE/ iwarp, RoCE..) JNI Layer bridges Java based HBase with communication library written in native code Enables high performance RDMA communication, while supporting traditional socket interface 52

Time (us) Time (us) HBase YCSB Read-Write Workload 7000 10000 6000 9000 8000 5000 7000 4000 3000 2000 1000 6000 5000 4000 3000 2000 1000 IPoIB (QDR) 10GigE OSU-IB (QDR) 0 8 16 32 64 96 128 No. of Clients 0 8 16 32 64 96 128 No. of Clients Read Latency HBase Get latency (Yahoo! Cloud Service Benchmark) 64 clients: 2.0 ms; 128 Clients: 3.5 ms 42% improvement over IPoIB for 128 clients HBase Put latency 64 clients: 1.9 ms; 128 Clients: 3.5 ms 40% improvement over IPoIB for 128 clients Write Latency J. Huang, X. Ouyang, J. Jose, M. W. Rahman, H. Wang, M. Luo, H. Subramoni, Chet Murthy, and D. K. Panda, High-Performance Design of HBase with RDMA over InfiniBand, IPDPS 12 53

Presentation Outline Challenges for Accelerating Big Data Processing The High-Performance Big Data (HiBD) Project RDMA-based designs for Apache Hadoop and Spark Case studies with HDFS, MapReduce, and Spark Sample Performance Numbers for Hadoop 2.x 0.9.6 Release RDMA-based designs for Memcached and HBase RDMA-based Memcached with SSD-assisted Hybrid Memory RDMA-based HBase Challenges in Designing Benchmarks for Big Data Processing OSU HiBD Benchmarks Conclusion and Q&A 54

Designing Communication and I/O Libraries for Big Data Systems: Solved a Few Initial Challenges Applications Benchmarks Big Data Middleware (HDFS, MapReduce, HBase, Spark and Memcached) Upper level Changes? Programming Models (Sockets) Other RDMA Protocols? Point-to-Point Communication Communication and I/O Library Threaded Models and Synchronization Virtualization I/O and File Systems QoS Fault-Tolerance Networking Technologies (InfiniBand, 1/10/40GigE and Intelligent NICs) Commodity Computing System Architectures (Multi- and Many-core architectures and accelerators) Storage Technologies (HDD and SSD) 55

Are the Current Benchmarks Sufficient for Big Data Management and Processing? The current benchmarks provide some performance behavior However, do not provide any information to the designer/developer on: What is happening at the lower-layer? Where the benefits are coming from? Which design is leading to benefits or bottlenecks? Which component in the design needs to be changed and what will be its impact? Can performance gain/loss at the lower-layer be correlated to the performance gain/loss observed at the upper layer? 56

OSU MPI Micro-Benchmarks (OMB) Suite A comprehensive suite of benchmarks to Compare performance of different MPI libraries on various networks and systems Validate low-level functionalities Provide insights to the underlying MPI-level designs Started with basic send-recv (MPI-1) micro-benchmarks for latency, bandwidth and bi-directional bandwidth Extended later to MPI-2 one-sided Collectives GPU-aware data movement OpenSHMEM (point-to-point and collectives) UPC Has become an industry standard Extensively used for design/development of MPI libraries, performance comparison of MPI libraries and even in procurement of large-scale systems Available from http://mvapich.cse.ohio-state.edu/benchmarks Available in an integrated manner with MVAPICH2 stack 57

Challenges in Benchmarking of RDMA-based Designs Applications Big Data Middleware (HDFS, MapReduce, HBase, Spark and Memcached) Programming Models (Sockets) Benchmarks Other RDMA Protocols? Current Benchmarks Correlation? Communication and I/O Library Point-to-Point Communication Threaded Models and Synchronization Virtualization No Benchmarks I/O and File Systems QoS Fault-Tolerance Networking Technologies (InfiniBand, 1/10/40GigE and Intelligent NICs) Commodity Computing System Architectures (Multi- and Many-core architectures and accelerators) Storage Technologies (HDD and SSD) 58

Iterative Process Requires Deeper Investigation and Design for Benchmarking Next Generation Big Data Systems and Applications Applications Benchmarks Big Data Middleware (HDFS, MapReduce, HBase, Spark and Memcached) Applications- Level Benchmarks Programming Models (Sockets) Other RDMA Protocols? Communication and I/O Library Point-to-Point Communication I/O and File Systems Threaded Models and Synchronization QoS Virtualization Fault-Tolerance Micro- Benchmarks Networking Technologies (InfiniBand, 1/10/40GigE and Intelligent NICs) Commodity Computing System Architectures (Multi- and Many-core architectures and accelerators) Storage Technologies (HDD and SSD) 59

OSU HiBD Micro-Benchmark (OHB) Suite - HDFS Evaluate the performance of standalone HDFS Five different benchmarks Sequential Write Latency (SWL) Sequential or Random Read Latency (SRL or RRL) Sequential Write Throughput (SWT) Sequential Read Throughput (SRT) Sequential Read-Write Throughput (SRWT) N. S. Islam, X. Lu, M. W. Rahman, J. Jose, and D. K. Panda, A Micro-benchmark Suite for Evaluating HDFS Operations on Modern Clusters, Int'l Workshop on Big Data Benchmarking (WBDB '12), December 2012. Benchmark File Name File Size HDFS Parameter Readers Writers Random/ Sequential Read Seek Interval SWL SRL/RRL (RRL) SWT SRT SRWT 60

OSU HiBD Micro-Benchmark (OHB) Suite - RPC Two different micro-benchmarks to evaluate the performance of standalone Hadoop RPC Latency: Single Server, Single Client Throughput: Single Server, Multiple Clients A simple script framework for job launching and resource monitoring Calculates statistics like Min, Max, Average Network configuration, Tunable parameters, DataType, CPU Utilization Component Network Address Port Data Type Min Msg Size Max Msg Size No. of Iterations Handlers Verbose lat_client lat_server Component Network Address Port Data Type Min Msg Size Max Msg Size No. of Iterations No. of Clients Handlers Verbose thr_client thr_server X. Lu, M. W. Rahman, N. Islam, and D. K. Panda, A Micro-Benchmark Suite for Evaluating Hadoop RPC on High- Performance Networks, Int'l Workshop on Big Data Benchmarking (WBDB '13), July 2013. 61

OSU HiBD Micro-Benchmark (OHB) Suite - MapReduce Evaluate the performance of stand-alone MapReduce Does not require or involve HDFS or any other distributed file system Considers various factors that influence the data shuffling phase underlying network configuration, number of map and reduce tasks, intermediate shuffle data pattern, shuffle data size etc. Three different micro-benchmarks based on intermediate shuffle data patterns MR-AVG micro-benchmark: intermediate data is evenly distributed among reduce tasks. MR-RAND micro-benchmark: intermediate data is pseudo-randomly distributed among reduce tasks. MR-SKEW micro-benchmark: intermediate data is unevenly distributed among reduce tasks. D. Shankar, X. Lu, M. W. Rahman, N. Islam, and D. K. Panda, A Micro-Benchmark Suite for Evaluating Hadoop MapReduce on High-Performance Networks, BPOE-5 (2014). 62

Future Plans of OSU High Performance Big Data Project Upcoming Releases of RDMA-enhanced Packages will support Spark HBase Plugin-based designs Upcoming Releases of OSU HiBD Micro-Benchmarks (OHB) will support HDFS MapReduce RPC Exploration of other components (Threading models, QoS, Virtualization, Accelerators, etc.) Advanced designs with upper-level changes and optimizations 63

Concluding Remarks Presented an overview of Big Data processing middleware Discussed challenges in accelerating Big Data middleware Presented initial designs to take advantage of InfiniBand/RDMA for Hadoop, Spark, Memcached, and HBase Presented challenges in designing benchmarks Results are promising Many other open issues need to be solved Will enable Big processing community to take advantage of modern HPC technologies to carry out their analytics in a fast and scalable manner 64

Personnel Acknowledgments Current Students A. Awan (Ph.D.) M. Li (Ph.D.) A. Bhat (M.S.) M. Rahman (Ph.D.) S. Chakraborthy (Ph.D.) D. Shankar (Ph.D.) C.-H. Chu (Ph.D.) A. Venkatesh (Ph.D.) N. Islam (Ph.D.) J. Zhang (Ph.D.) Past Students P. Balaji (Ph.D.) W. Huang (Ph.D.) D. Buntinas (Ph.D.) W. Jiang (M.S.) S. Bhagvat (M.S.) J. Jose (Ph.D.) L. Chai (Ph.D.) S. Kini (M.S.) B. Chandrasekharan (M.S.) M. Koop (Ph.D.) N. Dandapanthula (M.S.) R. Kumar (M.S.) V. Dhanraj (M.S.) S. Krishnamoorthy (M.S.) T. Gangadharappa (M.S.) K. Kandalla (Ph.D.) K. Gopalakrishnan (M.S.) P. Lai (M.S.) Past Post-Docs J. Liu (Ph.D.) H. Wang E. Mancini X. Besseron S. Marcarelli H.-W. Jin J. Vienne M. Luo Current Senior Research Associates K. Hamidouche H. Subramoni X. Lu Current Post-Doc Current Programmer J. Lin J. Perkins Current Research Specialist M. Arnold M. Luo (Ph.D.) G. Santhanaraman (Ph.D.) A. Mamidala (Ph.D.) A. Singh (Ph.D.) G. Marsh (M.S.) J. Sridhar (M.S.) V. Meshram (M.S.) S. Sur (Ph.D.) S. Naravula (Ph.D.) H. Subramoni (Ph.D.) R. Noronha (Ph.D.) K. Vaidyanathan (Ph.D.) X. Ouyang (Ph.D.) A. Vishnu (Ph.D.) S. Pai (M.S.) J. Wu (Ph.D.) S. Potluri (Ph.D.) W. Yu (Ph.D.) R. Rajachandrasekar (Ph.D.) Past Research Scientist Past Programmers S. Sur D. Bureddy 65

Thank You! panda@cse.ohio-state.edu Network-Based Computing Laboratory http://nowlab.cse.ohio-state.edu/ The MVAPICH2/MVAPICH2-X Project http://mvapich.cse.ohio-state.edu/ The High-Performance Big Data Project http://hibd.cse.ohio-state.edu/ 66

Call For Participation International Workshop on High-Performance Big Data Computing (HPBDC 2015) In conjunction with International Conference on Distributed Computing Systems (ICDCS 2015) In Hilton Downtown, Columbus, Ohio, USA, Monday, June 29th, 2015 http://web.cse.ohio-state.edu/~luxi/hpbdc2015 67

Multiple Positions Available in My Group Looking for Bright and Enthusiastic Personnel to join as Post-Doctoral Researchers PhD Students Hadoop/Big Data Programmer/Software Engineer MPI Programmer/Software Engineer If interested, please contact me at this conference and/or send an e-mail to panda@cse.ohio-state.edu HPCAC China, Nov. '14 68