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Big Data Meets HPC: Exploiting HPC Technologies for Accelerating Big Data Processing Dhabaleswar K. (DK) Panda The Ohio State University E-mail: [email protected] http://www.cse.ohio-state.edu/~panda Talk at HPCAC-Switzerland (April ‘17) by

Transcript of Big Data Meets HPC: Exploiting HPC Technologies for ... · Big Data Meets HPC: Exploiting HPC...

Big Data Meets HPC: Exploiting HPC Technologies for Accelerating Big Data Processing

Dhabaleswar K. (DK) Panda

The Ohio State University

E-mail: [email protected]

http://www.cse.ohio-state.edu/~panda

Talk at HPCAC-Switzerland (April ‘17)

by

HPCAC-Switzerland (April ’17) 2Network Based Computing Laboratory

• Big Data has changed the way people understand

and harness the power of data, both in the

business and research domains

• Big Data has become one of the most important

elements in business analytics

• Big Data and High Performance Computing (HPC)

are converging to meet large scale data processing

challenges

• Running High Performance Data Analysis (HPDA) workloads in the cloud is gaining popularity

• According to the latest OpenStack survey, 27% of cloud deployments are running HPDA workloads

Introduction to Big Data Analytics and Trends

http://www.coolinfographics.com/blog/tag/data?currentPage=3

http://www.climatecentral.org/news/white-house-brings-together-big-data-and-climate-change-17194

HPCAC-Switzerland (April ’17) 3Network Based Computing Laboratory

• Substantial impact on designing and utilizing 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

Data Management and Processing on Modern Clusters

HPCAC-Switzerland (April ’17) 4Network Based Computing Laboratory

Drivers of Modern HPC Cluster Architectures

Tianhe – 2 Titan Stampede Tianhe – 1A

• Multi-core/many-core technologies

• Remote Direct Memory Access (RDMA)-enabled networking (InfiniBand and RoCE)

• Solid State Drives (SSDs), Non-Volatile Random-Access Memory (NVRAM), NVMe-SSD

• Accelerators (NVIDIA GPGPUs and Intel Xeon Phi)

Accelerators / Coprocessors high compute density, high

performance/watt>1 TFlop DP on a chip

High Performance Interconnects -InfiniBand

<1usec latency, 100Gbps Bandwidth>Multi-core Processors SSD, NVMe-SSD, NVRAM

HPCAC-Switzerland (April ’17) 5Network Based Computing Laboratory

Interconnects and Protocols in OpenFabrics Stack for HPC(http://openfabrics.org)

Kernel

Space

Application / Middleware

Verbs

Ethernet

Adapter

EthernetSwitch

Ethernet Driver

TCP/IP

1/10/40/100 GigE

InfiniBand

Adapter

InfiniBand

Switch

IPoIB

IPoIB

Ethernet

Adapter

Ethernet Switch

Hardware Offload

TCP/IP

10/40 GigE-TOE

InfiniBand

Adapter

InfiniBand

Switch

User Space

RSockets

RSockets

iWARP Adapter

Ethernet Switch

TCP/IP

User Space

iWARP

RoCEAdapter

Ethernet Switch

RDMA

User Space

RoCE

InfiniBand

Switch

InfiniBand

Adapter

RDMA

User Space

IB Native

Sockets

Application / Middleware Interface

Protocol

Adapter

Switch

InfiniBand

Adapter

InfiniBand

Switch

RDMA

SDP

SDP

HPCAC-Switzerland (April ’17) 6Network Based Computing Laboratory

How Can HPC Clusters with High-Performance Interconnect and Storage Architectures Benefit Big Data Applications?

Bring HPC and Big Data processing into a “convergent trajectory”!

What are the major

bottlenecks in current Big

Data processing

middleware (e.g. Hadoop,

Spark, and Memcached)?

Can the bottlenecks be alleviated with new

designs by taking advantage of HPC

technologies?

Can RDMA-enabled

high-performance

interconnects

benefit Big Data

processing?

Can HPC Clusters with

high-performance

storage systems (e.g.

SSD, parallel file

systems) benefit Big

Data applications?

How much

performance benefits

can be achieved

through enhanced

designs?

How to design

benchmarks for

evaluating the

performance of Big

Data middleware on

HPC clusters?

HPCAC-Switzerland (April ’17) 7Network Based Computing Laboratory

Can We Run Big Data Jobs on Existing HPC Infrastructure?

HPCAC-Switzerland (April ’17) 8Network Based Computing Laboratory

Can We Run Big Data Jobs on Existing HPC Infrastructure?

HPCAC-Switzerland (April ’17) 9Network Based Computing Laboratory

Can We Run Big Data Jobs on Existing HPC Infrastructure?

HPCAC-Switzerland (April ’17) 10Network Based Computing Laboratory

Designing Communication and I/O Libraries for Big Data Systems: Challenges

Big Data Middleware(HDFS, MapReduce, HBase, Spark and Memcached)

Networking Technologies

(InfiniBand, 1/10/40/100 GigE and Intelligent NICs)

Storage Technologies(HDD, SSD, NVM, and NVMe-

SSD)

Programming Models(Sockets)

Applications

Commodity Computing System Architectures

(Multi- and Many-core architectures and accelerators)

Other Protocols?

Communication and I/O Library

Point-to-PointCommunication

QoS & Fault Tolerance

Threaded Modelsand Synchronization

Performance TuningI/O and File Systems

Virtualization (SR-IOV)

Benchmarks

Upper level

Changes?

HPCAC-Switzerland (April ’17) 11Network Based Computing Laboratory

• Sockets not designed for high-performance

– Stream semantics often mismatch for upper layers

– Zero-copy not available for non-blocking sockets

Can Big Data Processing Systems be Designed with High-Performance Networks and Protocols?

Current Design

Application

Sockets

1/10/40/100 GigENetwork

Our Approach

Application

OSU Design

10/40/100 GigE or InfiniBand

Verbs Interface

HPCAC-Switzerland (April ’17) 12Network Based Computing Laboratory

• RDMA for Apache Spark

• RDMA for Apache Hadoop 2.x (RDMA-Hadoop-2.x)

– Plugins for Apache, Hortonworks (HDP) and Cloudera (CDH) Hadoop distributions

• RDMA for Apache HBase

• RDMA for Memcached (RDMA-Memcached)

• RDMA for Apache Hadoop 1.x (RDMA-Hadoop)

• OSU HiBD-Benchmarks (OHB)

– HDFS, Memcached, HBase, and Spark Micro-benchmarks

• http://hibd.cse.ohio-state.edu

• Users Base: 215 organizations from 29 countries

• More than 21,100 downloads from the project site

The High-Performance Big Data (HiBD) Project

Available for InfiniBand and RoCE

Also run on Ethernet

HPCAC-Switzerland (April ’17) 13Network Based Computing Laboratory

• High-Performance Design of Hadoop over RDMA-enabled Interconnects

– High performance RDMA-enhanced 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

– Memcached-based burst buffer for MapReduce over Lustre-integrated HDFS (HHH-L-BB mode)

– Plugin-based architecture supporting RDMA-based designs for Apache Hadoop, CDH and HDP

– Easily configurable for different running modes (HHH, HHH-M, HHH-L, HHH-L-BB, and MapReduce over Lustre) and different

protocols (native InfiniBand, RoCE, and IPoIB)

• Current release: 1.1.0

– Based on Apache Hadoop 2.7.3

– Compliant with Apache Hadoop 2.7.1, HDP 2.5.0.3 and CDH 5.8.2 APIs and applications

– Tested with

• Mellanox InfiniBand adapters (DDR, QDR, FDR, and EDR)

• RoCE support with Mellanox adapters

• Various multi-core platforms

• Different file systems with disks and SSDs and Lustre

RDMA for Apache Hadoop 2.x Distribution

http://hibd.cse.ohio-state.edu

HPCAC-Switzerland (April ’17) 14Network Based Computing Laboratory

• HHH: Heterogeneous storage devices with hybrid replication schemes are supported in this mode of operation to have better fault-tolerance as well

as performance. This mode is enabled by default in the package.

• HHH-M: A high-performance in-memory based setup has been introduced in this package that can be utilized to perform all I/O operations in-

memory and obtain as much performance benefit as possible.

• HHH-L: With parallel file systems integrated, HHH-L mode can take advantage of the Lustre available in the cluster.

• HHH-L-BB: This mode deploys a Memcached-based burst buffer system to reduce the bandwidth bottleneck of shared file system access. The burst

buffer design is hosted by Memcached servers, each of which has a local SSD.

• MapReduce over Lustre, with/without local disks: Besides, HDFS based solutions, this package also provides support to run MapReduce jobs on top

of Lustre alone. Here, two different modes are introduced: with local disks and without local disks.

• Running with Slurm and PBS: Supports deploying RDMA for Apache Hadoop 2.x with Slurm and PBS in different running modes (HHH, HHH-M, HHH-

L, and MapReduce over Lustre).

Different Modes of RDMA for Apache Hadoop 2.x

HPCAC-Switzerland (April ’17) 15Network Based Computing Laboratory

• High-Performance Design of Spark over RDMA-enabled Interconnects

– High performance RDMA-enhanced design with native InfiniBand and RoCE support at the verbs-level for Spark

– RDMA-based data shuffle and SEDA-based shuffle architecture

– Support pre-connection, on-demand connection, and connection sharing

– Non-blocking and chunk-based data transfer

– Off-JVM-heap buffer management

– Easily configurable for different protocols (native InfiniBand, RoCE, and IPoIB)

• Current release: 0.9.4

– Based on Apache Spark 2.1.0

– Tested with

• Mellanox InfiniBand adapters (DDR, QDR, FDR, and EDR)

• RoCE support with Mellanox adapters

• Various multi-core platforms

• RAM disks, SSDs, and HDD

– http://hibd.cse.ohio-state.edu

RDMA for Apache Spark Distribution

HPCAC-Switzerland (April ’17) 16Network Based Computing Laboratory

• High-Performance Design of HBase over RDMA-enabled Interconnects

– High performance RDMA-enhanced design with native InfiniBand and RoCE support at the verbs-level

for HBase

– Compliant with Apache HBase 1.1.2 APIs and applications

– On-demand connection setup

– Easily configurable for different protocols (native InfiniBand, RoCE, and IPoIB)

• Current release: 0.9.1

– Based on Apache HBase 1.1.2

– Tested with

• Mellanox InfiniBand adapters (DDR, QDR, FDR, and EDR)

• RoCE support with Mellanox adapters

• Various multi-core platforms

– http://hibd.cse.ohio-state.edu

RDMA for Apache HBase Distribution

HPCAC-Switzerland (April ’17) 17Network Based Computing Laboratory

• High-Performance Design of Memcached over RDMA-enabled Interconnects

– High performance RDMA-enhanced design with native InfiniBand and RoCE support at the verbs-level for Memcached and

libMemcached components

– High performance design of SSD-Assisted Hybrid Memory

– Non-Blocking Libmemcached Set/Get API extensions

– Support for burst-buffer mode in Lustre-integrated design of HDFS in RDMA for Apache Hadoop-2.x

– Easily configurable for native InfiniBand, RoCE and the traditional sockets-based support (Ethernet and InfiniBand with

IPoIB)

• Current release: 0.9.5

– Based on Memcached 1.4.24 and libMemcached 1.0.18

– Compliant with libMemcached APIs and applications

– Tested with

• Mellanox InfiniBand adapters (DDR, QDR, FDR, and EDR)

• RoCE support with Mellanox adapters

• Various multi-core platforms

• SSD

– http://hibd.cse.ohio-state.edu

RDMA for Memcached Distribution

HPCAC-Switzerland (April ’17) 18Network Based Computing Laboratory

• Micro-benchmarks for Hadoop Distributed File System (HDFS)

– Sequential Write Latency (SWL) Benchmark, Sequential Read Latency (SRL) Benchmark,

Random Read Latency (RRL) Benchmark, Sequential Write Throughput (SWT) Benchmark,

Sequential Read Throughput (SRT) Benchmark

– Support benchmarking of

• Apache Hadoop 1.x and 2.x HDFS, Hortonworks Data Platform (HDP) HDFS, Cloudera Distribution of

Hadoop (CDH) HDFS

• Micro-benchmarks for Memcached

– Get Benchmark, Set Benchmark, and Mixed Get/Set Benchmark, Non-Blocking API Latency

Benchmark, Hybrid Memory Latency Benchmark

• Micro-benchmarks for HBase

– Get Latency Benchmark, Put Latency Benchmark

• Current release: 0.9.1

• http://hibd.cse.ohio-state.edu

OSU HiBD Micro-Benchmark (OHB) Suite – HDFS, Memcached, and HBase

HPCAC-Switzerland (April ’17) 19Network Based Computing Laboratory

Using HiBD Packages on Existing HPC Infrastructure

HPCAC-Switzerland (April ’17) 20Network Based Computing Laboratory

Using HiBD Packages on Existing HPC Infrastructure

HPCAC-Switzerland (April ’17) 21Network Based Computing Laboratory

• RDMA for Apache Hadoop 2.x and RDMA for Apache Spark are installed and

available on SDSC Comet.

– Examples for various modes of usage are available in:

• RDMA for Apache Hadoop 2.x: /share/apps/examples/HADOOP

• RDMA for Apache Spark: /share/apps/examples/SPARK/

– Please email [email protected] (reference Comet as the machine, and SDSC as the

site) if you have any further questions about usage and configuration.

• RDMA for Apache Hadoop is also available on Chameleon Cloud as an

appliance

– https://www.chameleoncloud.org/appliances/17/

HiBD Packages on SDSC Comet and Chameleon Cloud

M. Tatineni, X. Lu, D. J. Choi, A. Majumdar, and D. K. Panda, Experiences and Benefits of Running RDMA Hadoop and Spark on SDSC

Comet, XSEDE’16, July 2016

HPCAC-Switzerland (April ’17) 22Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• RDMA-Hadoop with DDN IME Burst Buffer Technology

• Advanced Designs

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 23Network Based Computing Laboratory

• Enables high performance RDMA communication, while supporting traditional socket interface

• JNI Layer bridges Java based HDFS with communication library written in native code

Design Overview of HDFS with RDMA

HDFS

Verbs

RDMA Capable Networks(IB, iWARP, RoCE ..)

Applications

1/10/40/100 GigE, IPoIB Network

Java Socket Interface Java Native Interface (JNI)

WriteOthers

OSU Design

• Design Features

– RDMA-based HDFS write

– RDMA-based HDFS replication

– Parallel replication support

– On-demand connection setup

– InfiniBand/RoCE support

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

HPCAC-Switzerland (April ’17) 24Network Based Computing Laboratory

Triple-H

Heterogeneous Storage

• 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

Enhanced HDFS with In-Memory and Heterogeneous Storage

Hybrid Replication

Data Placement Policies

Eviction/Promotion

RAM Disk SSD HDD

Lustre

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

Applications

HPCAC-Switzerland (April ’17) 25Network Based Computing Laboratory

Design Overview of MapReduce with RDMA

MapReduce

Verbs

RDMA Capable Networks

(IB, iWARP, RoCE ..)

OSU Design

Applications

1/10/40/100 GigE, IPoIB Network

Java Socket Interface Java Native Interface (JNI)

Job

Tracker

Task

Tracker

Map

Reduce

• Enables high performance RDMA communication, while supporting traditional socket interface

• JNI Layer bridges Java based MapReduce with communication library written in native code

• 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

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

HPCAC-Switzerland (April ’17) 26Network Based Computing Laboratory

0

50

100

150

200

250

300

350

400

80 120 160

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (EDR)OSU-IB (EDR)

0

100

200

300

400

500

600

700

800

80 160 240

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (EDR)OSU-IB (EDR)

Performance Numbers of RDMA for Apache Hadoop 2.x –RandomWriter & TeraGen in OSU-RI2 (EDR)

Cluster with 8 Nodes with a total of 64 maps

• RandomWriter

– 3x improvement over IPoIB

for 80-160 GB file size

• TeraGen

– 4x improvement over IPoIB for

80-240 GB file size

RandomWriter TeraGen

Reduced by 3x Reduced by 4x

HPCAC-Switzerland (April ’17) 27Network Based Computing Laboratory

0

100

200

300

400

500

600

700

800

80 120 160

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (EDR)

OSU-IB (EDR)

Performance Numbers of RDMA for Apache Hadoop 2.x – Sort & TeraSort in OSU-RI2 (EDR)

Cluster with 8 Nodes with a total of 64 maps and 32 reduces

• Sort

– 61% improvement over IPoIB for

80-160 GB data

• TeraSort

– 18% improvement over IPoIB for

80-240 GB data

Reduced by 61%

Reduced by 18%

Cluster with 8 Nodes with a total of 64 maps and 14 reduces

Sort TeraSort

0

100

200

300

400

500

600

80 160 240

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (EDR)

OSU-IB (EDR)

HPCAC-Switzerland (April ’17) 28Network Based Computing Laboratory

Evaluation with Spark on SDSC Gordon (HHH vs. Tachyon/Alluxio)

• For 200GB TeraGen on 32 nodes

– Spark-TeraGen: HHH has 2.4x improvement over Tachyon; 2.3x over HDFS-IPoIB (QDR)

– Spark-TeraSort: HHH has 25.2% improvement over Tachyon; 17% over HDFS-IPoIB (QDR)

0

20

40

60

80

100

120

140

160

180

8:50 16:100 32:200

Exe

cuti

on

Tim

e (

s)

Cluster Size : Data Size (GB)

IPoIB (QDR) Tachyon OSU-IB (QDR)

0

100

200

300

400

500

600

700

8:50 16:100 32:200

Exe

cuti

on

Tim

e (

s)

Cluster Size : Data Size (GB)

Reduced by 2.4x

Reduced by 25.2%

TeraGen TeraSort

N. Islam, M. W. Rahman, X. Lu, D. Shankar, and D. K. Panda, Performance Characterization and Acceleration of In-Memory File

Systems for Hadoop and Spark Applications on HPC Clusters, IEEE BigData ’15, October 2015

HPCAC-Switzerland (April ’17) 29Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• RDMA-Hadoop with DDN IME Burst Buffer Technology

• Advanced Designs

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 30Network Based Computing Laboratory

• Design Features

– RDMA based shuffle plugin

– SEDA-based architecture

– Dynamic connection management and sharing

– Non-blocking data transfer

– Off-JVM-heap buffer management

– InfiniBand/RoCE support

Design Overview of Spark with RDMA

• 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

X. Lu, D. Shankar, S. Gugnani, and D. K. Panda, High-Performance Design of Apache Spark with RDMA and Its Benefits on Various Workloads, IEEE BigData ‘16, Dec. 2016.

Spark Core

RDMA Capable Networks(IB, iWARP, RoCE ..)

Apache Spark Benchmarks/Applications/Libraries/Frameworks

1/10/40/100 GigE, IPoIB Network

Java Socket Interface Java Native Interface (JNI)

Native RDMA-based Comm. Engine

Shuffle Manager (Sort, Hash, Tungsten-Sort)

Block Transfer Service (Netty, NIO, RDMA-Plugin)

Netty

Server

NIO

ServerRDMA

Server

Netty

Client

NIO

ClientRDMA

Client

HPCAC-Switzerland (April ’17) 31Network Based Computing Laboratory

• InfiniBand FDR, SSD, 64 Worker Nodes, 1536 Cores, (1536M 1536R)

• RDMA vs. IPoIB with 1536 concurrent tasks, single SSD per node.

– SortBy: Total time reduced by up to 80% over IPoIB (56Gbps)

– GroupBy: Total time reduced by up to 74% over IPoIB (56Gbps)

Performance Evaluation on SDSC Comet – SortBy/GroupBy

64 Worker Nodes, 1536 cores, SortByTest Total Time 64 Worker Nodes, 1536 cores, GroupByTest Total Time

0

50

100

150

200

250

300

64 128 256

Tim

e (s

ec)

Data Size (GB)

IPoIB

RDMA

0

50

100

150

200

250

64 128 256

Tim

e (s

ec)

Data Size (GB)

IPoIB

RDMA

74%80%

HPCAC-Switzerland (April ’17) 32Network Based Computing Laboratory

• InfiniBand FDR, SSD, 32/64 Worker Nodes, 768/1536 Cores, (768/1536M 768/1536R)

• RDMA vs. IPoIB with 768/1536 concurrent tasks, single SSD per node.

– 32 nodes/768 cores: Total time reduced by 37% over IPoIB (56Gbps)

– 64 nodes/1536 cores: Total time reduced by 43% over IPoIB (56Gbps)

Performance Evaluation on SDSC Comet – HiBench PageRank

32 Worker Nodes, 768 cores, PageRank Total Time 64 Worker Nodes, 1536 cores, PageRank Total Time

0

50

100

150

200

250

300

350

400

450

Huge BigData Gigantic

Tim

e (s

ec)

Data Size (GB)

IPoIB

RDMA

0

100

200

300

400

500

600

700

800

Huge BigData Gigantic

Tim

e (s

ec)

Data Size (GB)

IPoIB

RDMA

43%37%

HPCAC-Switzerland (April ’17) 33Network Based Computing Laboratory

Application Evaluation on SDSC Comet

• Kira Toolkit: Distributed astronomy image processing

toolkit implemented using Apache Spark

– https://github.com/BIDS/Kira

• Source extractor application, using a 65GB dataset from

the SDSS DR2 survey that comprises 11,150 image files.

0

20

40

60

80

100

120

RDMA Spark Apache Spark(IPoIB)

21 %

Execution times (sec) for Kira SE benchmark using 65 GB dataset, 48 cores.

M. Tatineni, X. Lu, D. J. Choi, A. Majumdar, and D. K. Panda, Experiences and Benefits of Running RDMA Hadoop and Spark on SDSC

Comet, XSEDE’16, July 2016

0

200

400

600

800

1000

24 48 96 192 384

On

e E

po

ch T

ime

(se

c)

Number of cores

IPoIB RDMA

• BigDL: Distributed Deep Learning Tool using Apache

Spark

– https://github.com/intel-analytics/BigDL

• VGG training model on the CIFAR-10 dataset

4.58x

HPCAC-Switzerland (April ’17) 34Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• RDMA-Hadoop with DDN IME Burst Buffer Technology

• Advanced Designs and Studies

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 35Network Based Computing Laboratory

1

10

100

1000

1 2 4 8

16

32

64

12

8

25

6

51

2

1K

2K

4K

Tim

e (

us)

Message Size

OSU-IB (FDR)

0

200

400

600

800

16 32 64 128 256 512 102420484080

Tho

usa

nd

s o

f Tr

ansa

ctio

ns

pe

r Se

con

d (

TPS)

No. of Clients

• Memcached Get latency

– 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

Memcached GET Latency Memcached Throughput

Memcached Performance (FDR Interconnect)

Experiments on TACC Stampede (Intel SandyBridge Cluster, IB: FDR)

Latency Reduced by nearly 20X 2X

J. Jose, H. Subramoni, M. Luo, M. Zhang, J. Huang, M. W. Rahman, N. Islam, X. Ouyang, H. Wang, S. Sur and D. K. Panda, Memcached Design on High

Performance RDMA Capable Interconnects, ICPP’11

J. Jose, H. Subramoni, K. Kandalla, M. W. Rahman, H. Wang, S. Narravula, and D. K. Panda, Scalable Memcached design for InfiniBand Clusters using Hybrid

Transport, CCGrid’12

HPCAC-Switzerland (April ’17) 36Network Based Computing Laboratory

• 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)

Micro-benchmark Evaluation for OLDP workloads

012345678

64 96 128 160 320 400

Late

ncy

(se

c)

No. of Clients

Memcached-IPoIB (32Gbps)

Memcached-RDMA (32Gbps)

0

1000

2000

3000

4000

64 96 128 160 320 400

Thro

ugh

pu

t (

Kq

/s)

No. of Clients

Memcached-IPoIB (32Gbps)

Memcached-RDMA (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

Reduced by 66%

HPCAC-Switzerland (April ’17) 37Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• RDMA-Hadoop with DDN IME Burst Buffer Technology

• Advanced Designs

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 38Network Based Computing Laboratory

0

100

200

300

400

500

32 64 128 256 512

Thro

ugh

ou

t/ta

sk (

MB

/s)

Write Data Size (GB)

Lustre IME

Lustre vs. DDN IME - TestDFSIO with RDMA-Hadoop

• TestDFSIO Throughput with RDMA-Hadoop; throughput per task; 12 (11 node managers + 1 resource manager) nodes on CSCS Greina Cluster, 72 concurrent map tasks

• For smaller data sizes, Lustre and IME demonstrate similar performance (<32 GB)

• For larger data sizes (>32 GB)

– Lustre saturates as data size read/written per client increases

– IME maintains performance and an improvement of about 2.3x – 4.9x over Lustre

0

50

100

150

200

250

300

32 64 128 256 512

Thro

ugh

ou

t/ta

sk (

MB

/s)

Read Data Size (GB)

Lustre IME

HPCAC-Switzerland (April ’17) 39Network Based Computing Laboratory

0

20

40

60

80

100

32 64 72 96 128

Job

Tim

e (s

)

TeraGen Data Size (GB)

Lustre IME

Lustre vs. DDN IME - TeraSort with RDMA-Hadoop

• Hadoop TeraGen/TeraSort with RDMA-Hadoop; throughput per task; 12 (11 node managers + 1 resource manager) nodes on CSCS Greina Cluster, 72 concurrent maps w/ block size of 256M

• Similar to throughput, for smaller data sizes, Lustre and IME demonstrate similar performance

• For larger data sizes (>32 GB), IME improves over Lustre by,

– 28-33% improvement over Lustre for TeraGen (data generation i.e., write intensive)

– 8-14% improvement over Lustre for TeraSort (shuffle intensive and input size = output size)

0

500

1000

1500

2000

32 64 72 96 128

Job

Tim

e (s

)

TeraSort Data Size (GB)

Lustre IME

HPCAC-Switzerland (April ’17) 40Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• RDMA-Hadoop with DDN IME Burst Buffer Technology

• Advanced Designs

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 41Network Based Computing Laboratory

– Memcached latency test with Zipf distribution, server with 1 GB memory, 32 KB key-value pair size, total

size of data accessed is 1 GB (when data fits in memory) and 1.5 GB (when data does not fit in memory)

– When data fits in memory: RDMA-Mem/Hybrid gives 5x improvement over IPoIB-Mem

– When data does not fit in memory: RDMA-Hybrid gives 2x-2.5x over IPoIB/RDMA-Mem

Performance Evaluation on IB FDR + SATA/NVMe SSDs (Hybrid Memory)

0

500

1000

1500

2000

2500

Set Get Set Get Set Get Set Get Set Get Set Get Set Get Set Get

IPoIB-Mem RDMA-Mem RDMA-Hybrid-SATA RDMA-Hybrid-NVMe

IPoIB-Mem RDMA-Mem RDMA-Hybrid-SATA RDMA-Hybrid-NVMe

Data Fits In Memory Data Does Not Fit In Memory

Late

ncy

(u

s)

slab allocation (SSD write) cache check+load (SSD read) cache update server response client wait miss-penalty

HPCAC-Switzerland (April ’17) 42Network Based Computing Laboratory

– Data does not fit in memory: Non-blocking Memcached Set/Get API Extensions can achieve• >16x latency improvement vs. blocking API over RDMA-Hybrid/RDMA-Mem w/ penalty• >2.5x throughput improvement vs. blocking API over default/optimized RDMA-Hybrid

– Data fits in memory: Non-blocking Extensions perform similar to RDMA-Mem/RDMA-Hybrid and >3.6x improvement over IPoIB-Mem

Performance Evaluation with Non-Blocking Memcached API

0

500

1000

1500

2000

2500

Set Get Set Get Set Get Set Get Set Get Set Get

IPoIB-Mem RDMA-Mem H-RDMA-Def H-RDMA-Opt-Block H-RDMA-Opt-NonB-i

H-RDMA-Opt-NonB-b

Ave

rage

Lat

en

cy (

us)

Miss Penalty (Backend DB Access Overhead)

Client Wait

Server Response

Cache Update

Cache check+Load (Memory and/or SSD read)

Slab Allocation (w/ SSD write on Out-of-Mem)

H = Hybrid Memcached over SATA SSD Opt = Adaptive slab manager Block = Default Blocking API NonB-i = Non-blocking iset/iget API NonB-b = Non-blocking bset/bget API w/ buffer re-use guarantee

HPCAC-Switzerland (April ’17) 43Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• Advanced Designs

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 44Network Based Computing Laboratory

• Hybrid and resilient key-value store-based Burst-Buffer system Over Lustre

• Overcome limitations of local storage on HPC cluster nodes

• Light-weight transparent interface to Hadoop/Spark applications

• Accelerating I/O-intensive Big Data workloads

– Non-blocking Memcached APIs to maximize overlap

– Client-based replication for resilience

– Asynchronous persistence to Lustre parallel file system

Burst-Buffer Over Lustre for Accelerating Big Data I/O (Boldio)

D. Shankar, X. Lu, D. K. Panda, Boldio: A Hybrid and Resilient Burst-Buffer over Lustre for Accelerating Big Data I/O, IEEE Big Data 2016.

Dir

ect

ove

r Lu

stre

Hadoop Applications/Benchmarks (E.g. MapReduce, Spark)

Hadoop File System Class Abstraction (LocalFileSystem)

Burst-Buffer Memcached Cluster

Burst-Buffer Libmemcached Client

RDMA-enhanced Communication EngineNon-Blocking API Blocking API

RDMA-enhanced Comm. Engine

Hyb-Mem Manager (RAM/SSD)

Persistence Mgr.

RDMA-enhanced Comm. Engine

Hyb-Mem Manager (RAM/SSD)

Persistence Mgr.

Bo

ldio

…..

Co-Design BoldioFileSystem Abs.

Lustre Parallel File SystemMDS OSS OSS OSS

MDT MDT OST OST OST OST OST OST

Bo

ldio

Ser

ver

Bo

ldio

Clie

nt

HPCAC-Switzerland (April ’17) 45Network Based Computing Laboratory

• Based on RDMA-based Libmemcached/Memcached 0.9.3, Hadoop-2.6.0

• InfiniBand QDR, 24GB RAM + PCIe-SSDs, 12 nodes, 32/48 Map/Reduce Tasks, 4-node Memcached cluster

• Boldio can improve

– throughput over Lustre by about 3x for write throughput and 7x for read throughput

– execution time of Hadoop benchmarks over Lustre, e.g. Wordcount, Cloudburst by >21%

• Contrasting with Alluxio (formerly Tachyon) – Performance degrades about 15x when Alluxio cannot leverage local storage (Alluxio-Local vs. Alluxio-Remote)

– Boldio can improve throughput over Alluxio with all remote workers by about 3.5x - 8 .8x (Alluxio-Remote vs. Boldio)

Performance Evaluation with Boldio

Hadoop/Spark Workloads

21%

0

50

100

150

200

250

300

350

400

450

WordCount InvIndx CloudBurst Spark TeraGen

Late

ncy

(se

c)

Lustre-Direct Alluxio-Remote Boldio

DFSIO Throughput

0.00

10000.00

20000.00

30000.00

40000.00

50000.00

60000.00

70000.00

20 GB 40 GB 20 GB 40 GB

Write Read

Agg

. Th

rou

ghp

ut

(MB

ps)

Lustre-Direct Alluxio-Local

Alluxio-Remote Boldio

~3x

~7x

HPCAC-Switzerland (April ’17) 46Network Based Computing Laboratory

• Basic Designs for HiBD Packages

– HDFS and MapReduce

– Spark

– Memcached

• Advanced Designs

– Memcached with Hybrid Memory and Non-blocking APIs

– Accelerating Big Data I/O (Lustre + Burst-Buffer)

• High-Performance Hadoop over Cloud

Acceleration Case Studies and Performance Evaluation

HPCAC-Switzerland (April ’17) 47Network Based Computing Laboratory

Overview of RDMA-Hadoop-Virt Architecture• Virtualization-aware modules in all the four

main Hadoop components:

– HDFS: Virtualization-aware Block Management

to improve fault-tolerance

– YARN: Extensions to Container Allocation Policy

to reduce network traffic

– MapReduce: Extensions to Map Task Scheduling

Policy to reduce network traffic

– Hadoop Common: Topology Detection Module

for automatic topology detection

• Communications in HDFS, MapReduce, and RPC

go through RDMA-based designs over SR-IOV

enabled InfiniBand

HDFS

YARN

Ha

do

op

Co

mm

on

MapReduce

HBase Others

Virtual Machines Bare-Metal nodesContainers

Big Data ApplicationsT

opolo

gy D

ete

ction M

odule Map Task Scheduling

Policy Extension

Container Allocation

Policy Extension

CloudBurst MR-MS Polygraph Others

Virtualization Aware

Block Management

S. Gugnani, X. Lu, D. K. Panda. Designing Virtualization-aware and Automatic Topology Detection Schemes for Accelerating Hadoop on

SR-IOV-enabled Clouds. CloudCom, 2016.

HPCAC-Switzerland (April ’17) 48Network Based Computing Laboratory

Evaluation with Applications

– 14% and 24% improvement with Default Mode for CloudBurst and Self-Join

– 30% and 55% improvement with Distributed Mode for CloudBurst and Self-Join

0

20

40

60

80

100

Default Mode Distributed Mode

EXEC

UTI

ON

TIM

ECloudBurst

RDMA-Hadoop RDMA-Hadoop-Virt

0

50

100

150

200

250

300

350

400

Default Mode Distributed Mode

EXEC

UTI

ON

TIM

E

Self-Join

RDMA-Hadoop RDMA-Hadoop-Virt

30% reduction

55% reduction

Will be released in future

HPCAC-Switzerland (April ’17) 49Network Based Computing Laboratory

• Upcoming Releases of RDMA-enhanced Packages will support

– Upgrades to the latest versions of Hadoop and Spark

– Streaming

– MR-Advisor

– Impala

– Virtualization support

• Upcoming Releases of OSU HiBD Micro-Benchmarks (OHB) will support

– MapReduce, RPC

• Advanced designs with upper-level changes and optimizations

– Boldio

– Efficient Indexing

On-going and Future Plans of OSU High Performance Big Data (HiBD) Project

HPCAC-Switzerland (April ’17) 50Network Based Computing Laboratory

Funding Acknowledgments

Funding Support by

Equipment Support by

HPCAC-Switzerland (April ’17) 51Network Based Computing Laboratory

Personnel AcknowledgmentsCurrent Students

– A. Awan (Ph.D.)

– R. Biswas (M.S.)

– M. Bayatpour (Ph.D.)

– S. Chakraborthy (Ph.D.)

Past Students

– A. Augustine (M.S.)

– P. Balaji (Ph.D.)

– S. Bhagvat (M.S.)

– A. Bhat (M.S.)

– D. Buntinas (Ph.D.)

– L. Chai (Ph.D.)

– B. Chandrasekharan (M.S.)

– N. Dandapanthula (M.S.)

– V. Dhanraj (M.S.)

– T. Gangadharappa (M.S.)

– K. Gopalakrishnan (M.S.)

– R. Rajachandrasekar (Ph.D.)

– G. Santhanaraman (Ph.D.)

– A. Singh (Ph.D.)

– J. Sridhar (M.S.)

– S. Sur (Ph.D.)

– H. Subramoni (Ph.D.)

– K. Vaidyanathan (Ph.D.)

– A. Venkatesh (Ph.D.)

– A. Vishnu (Ph.D.)

– J. Wu (Ph.D.)

– W. Yu (Ph.D.)

Past Research Scientist

– K. Hamidouche

– S. Sur

Past Post-Docs

– D. Banerjee

– X. Besseron

– H.-W. Jin

– W. Huang (Ph.D.)

– N. Islam (Ph.D.)

– W. Jiang (M.S.)

– J. Jose (Ph.D.)

– S. Kini (M.S.)

– M. Koop (Ph.D.)

– K. Kulkarni (M.S.)

– R. Kumar (M.S.)

– S. Krishnamoorthy (M.S.)

– K. Kandalla (Ph.D.)

– P. Lai (M.S.)

– J. Liu (Ph.D.)

– M. Luo (Ph.D.)

– A. Mamidala (Ph.D.)

– G. Marsh (M.S.)

– V. Meshram (M.S.)

– A. Moody (M.S.)

– S. Naravula (Ph.D.)

– R. Noronha (Ph.D.)

– X. Ouyang (Ph.D.)

– S. Pai (M.S.)

– S. Potluri (Ph.D.)

– M.-W. Rahman (Ph.D.)

– C.-H. Chu (Ph.D.)

– S. Guganani (Ph.D.)

– J. Hashmi (Ph.D.)

– H. Javed (Ph.D.)

– J. Lin

– M. Luo

– E. Mancini

Current Research Scientists

– X. Lu

– H. Subramoni

Past Programmers

– D. Bureddy

– M. Arnold

– J. Perkins

Current Research Specialist

– J. Smith– M. Li (Ph.D.)

– D. Shankar (Ph.D.)

– H. Shi (Ph.D.)

– J. Zhang (Ph.D.)

– S. Marcarelli

– J. Vienne

– H. Wang

HPCAC-Switzerland (April ’17) 52Network Based Computing Laboratory

The 3rd International Workshop on High-Performance Big Data Computing (HPBDC)

HPBDC 2017 will be held with IEEE International Parallel and Distributed Processing

Symposium (IPDPS 2017), Orlando, Florida USA, May, 2017

Keynote Speaker: Prof. Satoshi Matsuoka, Tokyo Institute of Technology, Japan

Panel Moderator: Prof. Jianfeng Zhan (ICT/CAS)Panel Topic: Sunrise or Sunset: Exploring the Design Space of Big Data Software Stack

Panel Members (Confirmed so far): Prof. Geoffrey C. Fox (Indiana University Bloomington); Dr. Raghunath Nambiar (Cisco); Prof. D. K. Panda (The Ohio State University)

Six Regular Research Papers and One Short Research PapersSession I: High-Performance Graph Processing

Session II: Benchmarking and Performance Analysis

http://web.cse.ohio-state.edu/~luxi/hpbdc2017

HPCAC-Switzerland (April ’17) 53Network Based Computing Laboratory

Birds of Feather (BoF) at ISC ‘17

A BoF on

Accelerating Big Data Processing System Software on

Modern HPC Clusters

will be held at Int’l Supercomputing Conference (ISC 2017), Frankfurt

Date/Time: The schedule is being finalized

HPCAC-Switzerland (April ’17) 54Network Based Computing Laboratory

[email protected]

http://www.cse.ohio-state.edu/~panda

Thank You!

Network-Based Computing Laboratoryhttp://nowlab.cse.ohio-state.edu/

The High-Performance Big Data Projecthttp://hibd.cse.ohio-state.edu/