Accelerating Apache Hadoop through High-Performance Networking and I/O Technologies

Accelerating Apache Hadoop through High-
Performance Networking and I/O Technologies
Dhabaleswar K. (DK) Panda
The Ohio State University
E-mail: panda@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~panda
Talk at Hadoop Summit, Dublin, Ireland (April 2016)
by
Xiaoyi Lu
The Ohio State University
E-mail: luxi@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~luxi

Hadoop Summit@Dublin (April ‘16) 2Network 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

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PercentageofClusters
NumberofClusters
Timeline
Percentage of Clusters
Number of Clusters
Trends for Commodity Computing Clusters in the Top 500
List (http://www.top500.org)
85%

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

• Advanced Interconnects and RDMA protocols
– InfiniBand
– 10-40 Gigabit Ethernet/iWARP
– RDMA over Converged Enhanced Ethernet (RoCE)
• Delivering excellent performance (Latency, Bandwidth and CPU Utilization)
• Has influenced re-designs of enhanced HPC middleware
– Message Passing Interface (MPI) and PGAS
– Parallel File Systems (Lustre, GPFS, ..)
• SSDs (SATA and NVMe)
• NVRAM and Burst Buffer
Trends in HPC Technologies

Interconnects and Protocols in OpenFabrics Stack for HPC
(http://openfabrics.org)
Kernel
Space
Application /
Middleware
Verbs
Ethernet
Adapter
Ethernet
Switch
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
RoCE
Adapter
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

• 235 IB Clusters (47%) in the Nov’ 2015 Top500 list (http://www.top500.org)
• Installations in the Top 50 (21 systems):
Large-scale InfiniBand Installations
462,462 cores (Stampede) at TACC (10th) 76,032 cores (Tsubame 2.5) at Japan/GSIC (25th)
185,344 cores (Pleiades) at NASA/Ames (13th) 194,616 cores (Cascade) at PNNL (27th)
72,800 cores Cray CS-Storm in US (15th) 76,032 cores (Makman-2) at Saudi Aramco (32nd)
72,800 cores Cray CS-Storm in US (16th) 110,400 cores (Pangea) in France (33rd)
265,440 cores SGI ICE at Tulip Trading Australia (17th) 37,120 cores (Lomonosov-2) at Russia/MSU (35th)
124,200 cores (Topaz) SGI ICE at ERDC DSRC in US (18th) 57,600 cores (SwiftLucy) in US (37th)
72,000 cores (HPC2) in Italy (19th) 55,728 cores (Prometheus) at Poland/Cyfronet (38th)
152,692 cores (Thunder) at AFRL/USA (21st ) 50,544 cores (Occigen) at France/GENCI-CINES (43rd)
147,456 cores (SuperMUC) in Germany (22nd) 76,896 cores (Salomon) SGI ICE in Czech Republic (47th)
86,016 cores (SuperMUC Phase 2) in Germany (24th) and many more!

• 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

Communication in the Memory Semantics (RDMA Model)
InfiniBand Device
Memory Memory
InfiniBand Device
CQ QPSend Recv
Memory
Segment
Send WQE contains information about the
send buffer (multiple segments) and the
receive buffer (single segment)
Processor Processor
CQQP
Send Recv
Memory
Segment
Hardware ACK
Memory
Segment
Memory
Segment
Initiator processor is involved only to:
1. Post send WQE
2. Pull out completed CQE from the send CQ
No involvement from the target processor

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 GigE
and 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-Point
Communication
QoS
Threaded Models
and 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 GigE
Network
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, Hortonworks (HDP) and Cloudera (CDH) 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 (based on voluntary registration): 160 organizations from 22 countries
• More than 16,000 downloads from the project site
• RDMA for Apache HBase and Impala (upcoming)
The High-Performance Big Data (HiBD) Project
Available for InfiniBand and RoCE

• 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
– Plugin-based architecture supporting RDMA-based designs for Apache Hadoop, CDH and HDP
– 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: 0.9.9
– Based on Apache Hadoop 2.7.1
– Compliant with Apache Hadoop 2.7.1, HDP 2.3.0.0 and CDH 5.6.0 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
RDMA for Apache Hadoop 2.x Distribution

• 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
– Non-blocking and chunk-based data transfer
– Easily configurable for different protocols (native InfiniBand, RoCE, and IPoIB)
• Current release: 0.9.1
– Based on Apache Spark 1.5.1
– Tested with
• Mellanox InfiniBand adapters (DDR, QDR and FDR)
• 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

• 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
• Current release: 0.8
• http://hibd.cse.ohio-state.edu
OSU HiBD Micro-Benchmark (OHB) Suite – HDFS & Memcached

• 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
– 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
OSU Design
Applications
1/10/40/100 GigE, IPoIB
Network
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

• 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
Advanced Overlapping among Different Phases
Default Architecture
Enhanced Overlapping with In-Memory Merge
Advanced Hybrid 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

• Design Features
– JVM-bypassed buffer
management
– RDMA or send/recv based
adaptive communication
– Intelligent buffer allocation and
adjustment for serialization
– On-demand connection setup
Design Overview of Hadoop RPC with RDMA
Hadoop RPC
Verbs
Applications
1/10/40/100 GigE, IPoIB
Network
Our DesignDefault
OSU Design
• JNI Layer bridges Java based RPC with communication library written in native code
X. Lu, N. Islam, M. W. Rahman, J. Jose, H. Subramoni, H. Wang, and D. K. Panda, High-Performance Design of Hadoop RPC with
RDMA over InfiniBand, Int'l Conference on Parallel Processing (ICPP '13), October 2013.

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ExecutionTime(s)
Data Size (GB)
IPoIB (FDR)
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ExecutionTime(s)
Data Size (GB)
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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

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ExecutionTime(s)
Data Size (GB)
IPoIB (FDR) OSU-IB (FDR)
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ExecutionTime(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
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ExecutionTime(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 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)
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8:50 16:100 32:200
ExecutionTime(s)
Cluster Size : Data Size (GB)
IPoIB (QDR) Tachyon OSU-IB (QDR)
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8:50 16:100 32:200
ExecutionTime(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

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/sort
reduce
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/sort
reduce

• 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)
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1200
300 400 500
JobExecutionTime(sec)
Data Size (GB)
IPoIB (FDR)
OSU-IB (FDR)
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20 GB 40 GB 80 GB 160 GB 320 GB 640 GB
Cluster: 4 Cluster: 8 Cluster: 16 Cluster: 32 Cluster: 64 Cluster: 128
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 directory
Reduced 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
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Data Size (GB)
IPoIB (QDR)
OSU-Lustre-Read (QDR)
OSU-RDMA-IB (QDR)
OSU-Hybrid-IB (QDR)
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Data Size (GB)
Reduced by 25%Reduced by 34%

– HDFS
– MapReduce
– RPC
– HBase
– Spark

HBase-RDMA Design Overview
• JNI Layer bridges Java based HBase with communication library written in native code
HBase
IB Verbs
OSU-IB Design
Applications
1/10/40/100 GigE, IPoIB
Networks

HBase – YCSB Read-Write Workload
• HBase Get latency (QDR, 10GigE)
– 64 clients: 2.0 ms; 128 Clients: 3.5 ms
– 42% improvement over IPoIB for 128 clients
• HBase Put latency (QDR, 10GigE)
– 64 clients: 1.9 ms; 128 Clients: 3.5 ms
– 40% improvement over IPoIB for 128 clients
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No. of Clients
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Time(us)
No. of Clients
10GigE
Read Latency Write Latency
OSU-IB (QDR)IPoIB (QDR)
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

HBase – YCSB Get Latency and Throughput on SDSC-Comet
• HBase Get average latency (FDR)
– 4 client threads: 38 us
– 59% improvement over IPoIB for 4 client threads
• HBase Get total throughput
– 4 client threads: 102 Kops/sec
– 2.4x improvement over IPoIB for 4 client threads
Get Latency Get Throughput
0
0.02
0.04
0.06
0.08
0.1
0.12
1 2 3 4
AverageLatency(ms)
Number of Client Threads
0
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60
80
100
120
1 2 3 4
TotalThroughput
(Kops/sec)
Number of Client Threads
59%
2.4x

– 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
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Time(sec)
Data Size (GB)
IPoIB
RDMA
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Time(sec)
Data Size (GB)
IPoIB
RDMA
57%80%

• InfiniBand FDR, SSD, 32/64 Worker Nodes, 768/1536 Cores, (768/1536M 768/1536R)
• RDMA-based design for Spark 1.5.1
• 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
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Time(sec)
Data Size (GB)
IPoIB
RDMA
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Huge BigData Gigantic
Time(sec)
Data Size (GB)
IPoIB
RDMA
43%37%

– 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?

Big Data Middleware
Programming Models
(Sockets)
Applications
Architectures
Other Protocols?
Point-to-Point
Communication
QoS
Threaded Models
and Synchronization
Virtualization
Benchmarks
RDMA Protocols
Challenges in Benchmarking of RDMA-based Designs
Current
Benchmarks
No Benchmarks
Correlation?

Big Data Middleware
Programming Models
(Sockets)
Applications
Architectures
Other Protocols?
Point-to-Point
Communication
QoS
Threaded Models
and Synchronization
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

• Upcoming Releases of RDMA-enhanced Packages will support
– HBase
– Impala
• Upcoming Releases of OSU HiBD Micro-Benchmarks (OHB) will support
– MapReduce
– RPC
• Advanced designs with upper-level changes and optimizations
– Memcached with Non-blocking API
– HDFS + Memcached-based Burst Buffer
On-going and Future Plans of OSU High Performance Big Data
(HiBD) Project

• Discussed challenges in accelerating Hadoop and Spark with HPC technologies
• Presented initial designs to take advantage of InfiniBand/RDMA for HDFS,
MapReduce, RPC, and Spark
• 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
• Looking forward to collaboration with the community
Concluding Remarks

Funding Acknowledgments
Funding Support by
Equipment Support by

Personnel Acknowledgments
Current 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, 2016
Keynote 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
http://web.cse.ohio-state.edu/~luxi/hpbdc2015

{panda, luxi}@cse.ohio-state.edu
Thank You!
The High-Performance Big Data Project
http://hibd.cse.ohio-state.edu/
Network-Based Computing Laboratory
http://nowlab.cse.ohio-state.edu/

Accelerating Apache Hadoop through High-Performance Networking and I/O Technologies

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