Big Data Acceleration - HPC Advisory Council · 2020-01-15 · N. Islam, X. Lu, M. W. Rahman, D. Shankar, and D. K. Panda, Triple-H: A Hybrid Approach to Accelerate HDFS on HPC Clusters

Big Data Acceleration

Dhabaleswar K. (DK) Panda

The Ohio State University

E-mail: [email protected]

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

Talk at HPCAC-Stanford (Feb 2016)

by

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

HPCAC-Stanford (Feb ‘16) 2Network Based Computing Laboratory

• 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

Introduction to Big Data Applications and Analytics


• Webpages (content, graph)

• Clicks (ad, page, social)

• Users (OpenID, FB Connect, etc.)

• e-mails (Hotmail, Y!Mail, Gmail, etc.)

• Photos, Movies (Flickr, YouTube, Video, etc.)

• Cookies / tracking info (see Ghostery)

• Installed apps (Android market, App Store, etc.)

• Location (Latitude, Loopt, Foursquared, Google Now, etc.)

• User generated content (Wikipedia & co, etc.)

• Ads (display, text, DoubleClick, Yahoo, etc.)

• Comments (Discuss, Facebook, etc.)

• Reviews (Yelp, Y!Local, etc.)

• Social connections (LinkedIn, Facebook, etc.)

• Purchase decisions (Netflix, Amazon, etc.)

• Instant Messages (YIM, Skype, Gtalk, etc.)

• Search terms (Google, Bing, etc.)

• News articles (BBC, NYTimes, Y!News, etc.)

• Blog posts (Tumblr, Wordpress, etc.)

• Microblogs (Twitter, Jaiku, Meme, etc.)

• Link sharing (Facebook, Delicious, Buzz, etc.)

Data Generation in Internet Services and Applications

Number of Apps in the Apple App Store, Android Market, Blackberry, and Windows Phone (2013)• Android Market: <1200K• Apple App Store: ~1000KCourtesy: http://dazeinfo.com/2014/07/10/apple-inc-aapl-ios-google-inc-goog-android-growth-mobile-ecosystem-2014/


• Scientific Data Management, Analysis, and Visualization

• Applications examples

– Climate modeling

– Combustion

– Fusion

– Astrophysics

– Bioinformatics

• Data Intensive Tasks

– Runs large-scale simulations on supercomputers

– Dump data on parallel storage systems

– Collect experimental / observational data

– Move experimental / observational data to analysis sites

– Visual analytics – help understand data visually

Not Only in Internet Services - Big Data in Scientific Domains


• Hadoop: http://hadoop.apache.org

– The most popular framework for Big Data Analytics

– HDFS, MapReduce, HBase, RPC, Hive, Pig, ZooKeeper, Mahout, etc.

• Spark: http://spark-project.org

– Provides primitives for in-memory cluster computing; Jobs can load data into memory and query it repeatedly

• Storm: http://storm-project.net

– A distributed real-time computation system for real-time analytics, online machine learning, continuous

computation, etc.

• S4: http://incubator.apache.org/s4

– A distributed system for processing continuous unbounded streams of data

• GraphLab: http://graphlab.org

– Consists of a core C++ GraphLab API and a collection of high-performance machine learning and data mining

toolkits built on top of the GraphLab API.

• Web 2.0: RDBMS + Memcached (http://memcached.org)

– Memcached: A high-performance, distributed memory object caching systems

Typical Solutions or Architectures for Big Data Analytics

http://hadoop.apache.org

http://spark-project.org

http://storm-project.net

http://incubator.apache.org/s4

http://graphlab.org/

http://memcached.org


Big Data Processing with Hadoop Components

• Major components included in this tutorial:

– MapReduce (Batch)

– HBase (Query)

– HDFS (Storage)

– RPC (Inter-process communication)

• Underlying Hadoop Distributed File System (HDFS) used by both MapReduce and HBase

• Model scales but high amount of communication during intermediate phases can be further optimized

HDFS

MapReduce

Hadoop Framework

User Applications

HBase

Hadoop Common (RPC)


Memcached Architecture

• 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

Main

memoryCPUs

SSD HDD

High Performance Networks

... ...

...

Main

memoryCPUs

SSD HDD

Main

memoryCPUs

SSD HDD

Main

memoryCPUs

SSD HDD

Main

memoryCPUs

SSD HDD


• 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


• Introduced in Oct 2000• High Performance Data Transfer

– Interprocessor communication and I/O– Low latency (<1.0 microsec), High bandwidth (up to 12.5 GigaBytes/sec -> 100Gbps), and

low CPU utilization (5-10%)

• Multiple Operations– Send/Recv– RDMA Read/Write– Atomic Operations (very unique)

• high performance and scalable implementations of distributed locks, semaphores, collective communication operations

• Leading to big changes in designing – HPC clusters– File systems– Cloud computing systems– Grid computing systems

Open Standard InfiniBand Networking Technology


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?


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 GigEand Intelligent NICs)

Storage Technologies(HDD, SSD, 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

Threaded Modelsand Synchronization

Fault-ToleranceI/O and File Systems

Virtualization

Benchmarks

Upper level

Changes?


• 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


• RDMA for Apache Spark

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

– Plugins for Apache and HDP Hadoop distributions

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

• RDMA for Memcached (RDMA-Memcached)

• OSU HiBD-Benchmarks (OHB)

– HDFS and Memcached Micro-benchmarks

• http://hibd.cse.ohio-state.edu

• Users Base: 145 organizations from 20 countries

• More than 14,900 downloads from the project site

• RDMA for Apache HBase and CDH (upcoming)

The High-Performance Big Data (HiBD) Project

Available for InfiniBand and RoCE

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

mailto:[email protected]


• 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.

• 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


• RDMA-based Designs and Performance Evaluation

– HDFS

– MapReduce

– RPC

– HBase

– Spark

– Memcached (Basic and Hybrid)

– HDFS + Memcached-based Burst Buffer

– OSU HiBD Benchmarks (OHB)

Acceleration Case Studies and Performance Evaluation


• 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


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


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


• 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


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


0

50

100

150

200

250

80 100 120

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (FDR)

0

50

100

150

200

250

80 100 120

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (FDR)

Performance Benefits – RandomWriter & TeraGen in TACC-Stampede

Cluster with 32 Nodes with a total of 128 maps

• RandomWriter

– 3-4x improvement over IPoIB

for 80-120 GB file size

• TeraGen

– 4-5x improvement over IPoIB

for 80-120 GB file size

RandomWriter TeraGen

Reduced by 3x Reduced by 4x


0

100

200

300

400

500

600

700

800

900

80 100 120

Exec

uti

on

Tim

e (s

)

Data Size (GB)

IPoIB (FDR) OSU-IB (FDR)

0

100

200

300

400

500

600

80 100 120

Exec

uti

on

Tim

e (s

)

Data Size (GB)


Performance Benefits – Sort & TeraSort in TACC-Stampede

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

Reduced by 52% Reduced by 44%

Cluster with 32 Nodes with a total of 128 maps and 57 reduces


Evaluation of HHH and HHH-L with Applications

HDFS (FDR) HHH (FDR)

60.24 s 48.3 s

CloudBurstMR-MSPolyGraph

0

200

400

600

800

1000

4 6 8

Exec

uti

on

Tim

e (s

)

Concurrent maps per host

HDFS Lustre HHH-L Reduced by 79%

• MR-MSPolygraph on OSU RI with

1,000 maps

– HHH-L reduces the execution time

by 79% over Lustre, 30% over HDFS

• CloudBurst on TACC Stampede

– With HHH: 19% improvement over

HDFS


Evaluation of MapReduce and Comparison with Tachyon

• RandomWriter: 200GB on 32 Nodes on SDSC Gordon

– HHH reduces the execution time by 47% over Tachyon, 56% over HDFS

• Sort: 200GB on 32 Nodes on SDSC Gordon

– HHH reduces the execution time by 19% over Tachyon, 31% over HDFS

0

20

40

60

80

100

120

140

160

180

200

8:50 16:100 32:200

Exe

cuti

on

Tim

e (

s)

Cluster Size: Data Size (GB)

HDFS Tachyon HHH

0

50

100

150

200

250

300

350

400

450

500

8:50 16:100 32:200

Exe

cuti

on

Tim

e (

s)

Cluster Size: Data Size (GB)

Reduced by 47% Reduced by 19%


Intermediate Data Directory

Design Overview of Shuffle Strategies for MapReduce over Lustre

• 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 RDMA-enhanced MapReduce design

Map 1 Map 2 Map 3

Lustre

Reduce 1 Reduce 2

Lustre Read / RDMA

In-memory

merge/sortreduce

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

In-memory

merge/sortreduce


• For 500GB Sort in 64 nodes

– 44% improvement over IPoIB (FDR)

Performance Improvement of MapReduce over Lustre on TACC-Stampede


– 48% improvement over IPoIB (FDR)

0

200

400

600

800

1000

1200

300 400 500

Job

Exe

cuti

on

Tim

e (s

ec)

Data Size (GB)

IPoIB (FDR)OSU-IB (FDR)

0

50

100

150

200

250

300

350

400

450

500

20 GB 40 GB 80 GB 160 GB 320 GB 640 GB

Cluster: 4 Cluster: 8 Cluster: 16 Cluster: 32 Cluster: 64 Cluster: 128

Job

Exe

cuti

on

Tim

e (s

ec)


M. W. Rahman, X. Lu, N. S. Islam, R. Rajachandrasekar, and D. K. Panda, MapReduce over Lustre: Can RDMA-

based Approach Benefit?, Euro-Par, August 2014.

• Local disk is used as the intermediate data directoryReduced by 48%Reduced by 44%



– 34% improvement over IPoIB (QDR)

Case Study - Performance Improvement of MapReduce over Lustre on SDSC-Gordon

• For 120GB TeraSort in 16 nodes

– 25% improvement over IPoIB (QDR)

• Lustre is used as the intermediate data directory

0

100

200

300

400

500

600

700

800

900

40 60 80

Job

Exe

cuti

on

Tim

e (

sec)

Data Size (GB)

IPoIB (QDR)

OSU-Lustre-Read (QDR)

OSU-RDMA-IB (QDR)

OSU-Hybrid-IB (QDR)

0

100

200

300

400

500

600

700

800

900

40 80 120

Job

Exe

cuti

on

Tim

e (s

ec)

Data Size (GB)

Reduced by 25%Reduced by 34%



– HDFS

– MapReduce

– RPC

– HBase

– Spark






• Design Features

– RDMA based shuffle

– SEDA-based plugins

– Dynamic connection management and sharing

– Non-blocking data transfer

– Off-JVM-heap buffer management


Design Overview of Spark with RDMA


• 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


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

• RDMA-based design for Spark 1.5.1

• 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 57% 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

57%80%


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


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

– Sort: Total time reduced by 38% over IPoIB (56Gbps)

– TeraSort: Total time reduced by 15% over IPoIB (56Gbps)

Performance Evaluation on SDSC Comet – HiBench Sort/TeraSort

64 Worker Nodes, 1536 cores, Sort Total Time 64 Worker Nodes, 1536 cores, TeraSort Total Time

0

50

100

150

200

250

300

350

400

450

64 128 256

Tim

e (s

ec)

Data Size (GB)

IPoIB

RDMA

0

100

200

300

400

500

600

64 128 256

Tim

e (s

ec)

Data Size (GB)

IPoIB

RDMA

15%38%


• 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%



– HDFS

– MapReduce

– RPC

– HBase

– Spark






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)IPoIB (FDR)

0

100

200

300

400

500

600

700

16 32 64 128 256 512 1024 2048 4080Tho

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

RDMA-Memcached Performance (FDR Interconnect)

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

Latency Reduced by nearly 20X

2X


• 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%


• 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

Performance Benefits of Hybrid Memcached (Memory + SSD) on SDSC-Gordon

0

2

4

6

8

10

64 128 256 512 1024

Thro

ugh

pu

t (m

illio

n t

ran

s/se

c)

No. of Clients

IPoIB (32Gbps)

RDMA-Mem (32Gbps)

RDMA-Hybrid (32Gbps)

0

100

200

300

400

500

Ave

rage

late

ncy

(u

s)

Message Size (Bytes)

2X


– 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

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


0

5000

10000

15000

20000

25000

30000

35000

Read-Only (100 % GET) Write-Heavy (50 GET: 50 SET)

Agg

r. T

hro

ugh

pu

t (O

ps/

sec)

H-RDMA-Def-Block H-RDMA-Opt-Block H-RDMA-Opt-NonB-i H-RDMA-Opt-NonB-b

Opt = Adaptive slab manager; Block = Default Blocking Memc. APINonB-i = Non-blocking iset/iget Memcached APINonB-b = Non-blocking bset/bget Memcached API w/ buffer re-use guarantee

Non-Blocking Memcached API extensions (IB FDR + SATA/NVMe SSDs)

– RDMA-Accelerated Communication + Hybrid ‘RAM+SSD’ slab management

– Non-blocking API extensions • memcached_(iset/iget/bset/bget/test/wait)

• Near in-memory speeds by hiding network and SSD I/O bottlenecks

• Exploit communication/computation overlap + optional buffer re-use guarantees

• Adaptive slab manager w/ different SSD I/O schemes

– Data does not fit in memory: Non-blocking Extensions give

• >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

D. Shankar, X. Lu, N. S. Islam, M. W. Rahman, and D. K. Panda, High-Performance Hybrid Key-Value Store on Modern Clusters with RDMA Interconnects and SSDs: Non-blocking Extensions, Designs, and Benefits, IPDPS, May 2016



– HDFS

– MapReduce

– RPC

– HBase

– Spark






• Design Features

– Memcached-based burst-buffer

system

• Hides latency of parallel file

system access

• Read from local storage and

Memcached

– Data locality achieved by writing data

to local storage

– Different approaches of integration

with parallel file system to guarantee

fault-tolerance

Accelerating I/O Performance of Big Data Analytics through RDMA-based Key-Value Store

Application

I/O Forwarding Module

Map/Reduce Task DataNode

Local Disk

Data LocalityFault-tolerance

Lustre

Memcached-based Burst Buffer System


Evaluation with PUMA Workloads

Gains on OSU RI with our approach (Mem-bb) on 24 nodes

• SequenceCount: 34.5% over Lustre, 40% over HDFS

• RankedInvertedIndex: 27.3% over Lustre, 48.3% over HDFS

• HistogramRating: 17% over Lustre, 7% over HDFS

0

500

1000

1500

2000

2500

3000

3500

4000

4500

SeqCount RankedInvIndex HistoRating

Exe

cuti

on

Tim

e (

s)

Workloads

HDFS (32Gbps)

Lustre (32Gbps)

Mem-bb (32Gbps)

48.3%

40%

17%

N. S. Islam, D. Shankar, X. Lu, M.

W. Rahman, and D. K. Panda,

Accelerating I/O Performance of

Big Data Analytics with RDMA-

based Key-Value Store, ICPP

’15, September 2015



– HDFS

– MapReduce

– RPC

– HBase

– Spark






• 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?

Are the Current Benchmarks Sufficient for Big Data?







Applications



Other Protocols?



QoS



Virtualization

Benchmarks

RDMA Protocols

Challenges in Benchmarking of RDMA-based Designs

Current

Benchmarks

No Benchmarks

Correlation?


• 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

OSU MPI Micro-Benchmarks (OMB) Suite

http://mvapich.cse.ohio-state.edu/benchmarks







Applications



Other Protocols?



QoS



Virtualization

Benchmarks

RDMA Protocols

Iterative Process – Requires Deeper Investigation and Design for Benchmarking Next Generation Big Data Systems and Applications

Applications-Level

Benchmarks

Micro-

Benchmarks


• HDFS Benchmarks

– 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

• Memcached Benchmarks

– Get Benchmark

– Set Benchmark

– Mixed Get/Set Benchmark

• Available as a part of OHB 0.8

OSU HiBD Benchmarks (OHB)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

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)

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

To be Released

mailto:[email protected]


• Upcoming Releases of RDMA-enhanced Packages will support

– CDH

– HBase

– Impala

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

– MapReduce

– RPC

• Advanced designs with upper-level changes and optimizations


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


• Discussed challenges in accelerating Hadoop, Spark and Memcached

• Presented initial designs to take advantage of InfiniBand/RDMA for HDFS,

MapReduce, RPC, Spark, and Memcached

• Results are promising

• Many other open issues need to be solved

• Will enable Big Data community to take advantage of modern HPC

technologies to carry out their analytics in a fast and scalable manner

Concluding Remarks


Funding Acknowledgments

Funding Support by

Equipment Support by


Personnel AcknowledgmentsCurrent Students

– A. Augustine (M.S.)

– A. Awan (Ph.D.)

– S. Chakraborthy (Ph.D.)

– C.-H. Chu (Ph.D.)

– N. Islam (Ph.D.)

– M. Li (Ph.D.)

Past Students

– 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.)

– 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. Vishnu (Ph.D.)

– J. Wu (Ph.D.)

– W. Yu (Ph.D.)

Past Research Scientist

– S. Sur

Current Post-Doc

– J. Lin

– D. Banerjee

Current Programmer

– J. Perkins

Past Post-Docs

– H. Wang

– X. Besseron

– H.-W. Jin

– M. Luo

– W. Huang (Ph.D.)

– W. Jiang (M.S.)

– J. Jose (Ph.D.)

– S. Kini (M.S.)

– M. Koop (Ph.D.)

– 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.)

– R. Rajachandrasekar (Ph.D.)

– K. Kulkarni (M.S.)

– M. Rahman (Ph.D.)

– D. Shankar (Ph.D.)

– A. Venkatesh (Ph.D.)

– J. Zhang (Ph.D.)

– E. Mancini

– S. Marcarelli

– J. Vienne

Current Research Scientists Current Senior Research Associate

– H. Subramoni

– X. Lu

Past Programmers

– D. Bureddy

- K. Hamidouche

Current Research Specialist

– M. Arnold


Second International Workshop on High-Performance Big Data Computing (HPBDC)

HPBDC 2016 will be held with IEEE International Parallel and Distributed Processing

Symposium (IPDPS 2016), Chicago, Illinois USA, May 27th, 2016Keynote Talk: Dr. Chaitanya Baru,

Senior Advisor for Data Science, National Science Foundation (NSF); Distinguished Scientist, San Diego Supercomputer Center (SDSC)

Panel Moderator: Jianfeng Zhan (ICT/CAS)Panel Topic: Merge or Split: Mutual Influence between Big Data and HPC Techniques

Six Regular Research Papers and Two Short Research Papers

http://web.cse.ohio-state.edu/~luxi/hpbdc2016

HPBDC 2015 was held in conjunction with ICDCS’15





[email protected]

Thank You!

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

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

The MVAPICH2 Projecthttp://mvapich.cse.ohio-state.edu/

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

Documents

Big Data Acceleration - HPC Advisory Council · 2020-01-15 · N. Islam, X. Lu, M. W. Rahman, D. Shankar, and D. K. Panda, Triple-H: A Hybrid Approach to Accelerate HDFS on HPC Clusters