Big Data Meets NVM: Accelerating Big Data Processing
with Non-Volatile Memory (NVM)
DataWorks Summit 2019 | Washington, DC
by
Xiaoyi Lu
The Ohio State University
luxi@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~luxi
Dhabaleswar K. (DK) Panda
The Ohio State University
panda@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~panda
Dipti Shankar
The Ohio State University
shankard@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~shankar.50

DataWorks Summit, 2019 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
Big Data Management and Processing on Modern Clusters

DataWorks Summit, 2019 3Network Based Computing Laboratory
Big Data Processing with Apache Big Data Analytics Stacks
• Major components included:
– MapReduce (Batch)
– Spark (Iterative and Interactive)
– HBase (Query)
– HDFS (Storage)
– RPC (Inter-process communication)
• Underlying Hadoop Distributed File
System (HDFS) used by MapReduce,
Spark, HBase, and many others
• Model scales but high amount of
communication and I/O can be further
optimized!
HDFS
MapReduce
Apache Big Data Analytics Stacks
User Applications
HBase
Hadoop Common (RPC)
Spark

DataWorks Summit, 2019 4Network Based Computing Laboratory
Drivers of Modern HPC Cluster and Data Center Architecture
• Multi-core/many-core technologies
• Remote Direct Memory Access (RDMA)-enabled networking (InfiniBand and RoCE)
– Single Root I/O Virtualization (SR-IOV)
• NVM and NVMe-SSD
• Accelerators (NVIDIA GPGPUs and FPGAs)
High Performance Interconnects –
InfiniBand (with SR-IOV)
<1usec latency, 200Gbps Bandwidth>
Multi-/Many-core
Processors
Cloud CloudSDSC Comet TACC Stampede
Accelerators / Coprocessors
high compute density, high
performance/watt
>1 TFlop DP on a chip
SSD, NVMe-SSD, NVRAM

DataWorks Summit, 2019 5Network Based Computing Laboratory
• RDMA for Apache Spark
• RDMA for Apache Hadoop 3.x (RDMA-Hadoop-3.x)
• RDMA for Apache Hadoop 2.x (RDMA-Hadoop-2.x)
– Plugins for Apache, Hortonworks (HDP) and Cloudera (CDH) Hadoop distributions
• RDMA for Apache Kafka
• 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: 305 organizations from 35 countries
• More than 29,750 downloads from the project site (April ‘19)
The High-Performance Big Data (HiBD) Project
Available for InfiniBand and RoCE
Also run on Ethernet
Available for x86 and OpenPOWER
Significant performance
improvement with ‘RDMA+DRAM’
compared to default Sockets-
based designs;
How about RDMA+NVRAM?

DataWorks Summit, 2019 6Network Based Computing Laboratory
Non-Volatile Memory (NVM) and NVMe-SSD
3D XPoint from Intel & Micron Samsung NVMe SSD Performance of PMC Flashtec NVRAM [*]
• Non-Volatile Memory (NVM) provides byte-addressability with persistence
• The huge explosion of data in diverse fields require fast analysis and storage
• NVMs provide the opportunity to build high-throughput storage systems for data-intensive
applications
• Storage technology is moving rapidly towards NVM
[*] http://www.enterprisetech.com/2014/08/06/ flashtec-nvram-15-million-iops-sub-microsecond- latency/

DataWorks Summit, 2019 7Network Based Computing Laboratory
• Popular methods employed by recent works to emulate NVRAM performance
model over DRAM
• Two ways:
– Emulate byte-addressable NVRAM over DRAM
– Emulate block-based NVM device over DRAM
NVRAM Emulation based on DRAM
Application
Virtual File System
Block Device PCMDisk
(RAM-Disk + Delay)
DRAM
mmap/memcpy/msync (DAX)
Application
Persistent Memory Library
Clflush + Delay
DRAM
pmem_memcpy_persist (DAX)
Load/store
Load/Store
open/read/write/close

DataWorks Summit, 2019 8Network Based Computing Laboratory
• NRCIO: NVM-aware RDMA-based Communication
and I/O Schemes
• NRCIO for Big Data Analytics
• NVMe-SSD based Big Data Analytics
• Conclusion and Q&A
Presentation Outline

DataWorks Summit, 2019 9Network Based Computing Laboratory
Design Scope (NVM for RDMA)
D-to-N over RDMA N-to-D over RDMA N-to-N over RDMA
D-to-N over RDMA: Communication buffers for client are allocated in DRAM; Server uses NVM
N-to-D over RDMA: Communication buffers for client are allocated in NVM; Server uses DRAM
N-to-N over RDMA: Communication buffers for client and server are allocated in NVM
DRAM NVM
HDFS-RDMA
(RDMADFSClient)
HDFS-RDMA
(RDMADFSServer)
Client
CPU
Server
CPU
PCIe
NIC
PCIe
NIC
Client Server
NVM DRAM
HDFS-RDMA
(RDMADFSClient)
HDFS-RDMA
(RDMADFSServer)
Client
CPU
Server
CPU
PCIePCIe
NIC NIC
Client Server
NVM NVM
HDFS-RDMA
(RDMADFSClient)
HDFS-RDMA
(RDMADFSServer)
Client
CPU
Server
CPU
PCIePCIe
NIC NIC
Client Server
D-to-D over RDMA: Communication buffers for client and server are allocated in DRAM (Common)

DataWorks Summit, 2019 10Network Based Computing Laboratory
NVRAM-aware RDMA-based Communication in NRCIO
NRCIO RDMA Write over NVRAM NRCIO RDMA Read over NVRAM

DataWorks Summit, 2019 11Network Based Computing Laboratory
DRAM-TO-NVRAM RDMA-Aware Communication with NRCIO
• Comparison of communication latency using NRCIO RDMA read and write communication
protocols over InfiniBand EDR HCA with DRAM as source and NVRAM as destination
• {NxDRAM} NVRAM emulation mode = Nx NVRAM write slowdown vs. DRAM with clflushopt
(emulated) + sfence
• Smaller impact of time-for-persistence on the end-to-end latencies for small messages vs.
large messages => larger number of cache lines to flush
0
5
10
15
20
25
256 4K 16K 256 4K 16K 256 4K 16K
1xDRAM 2xDRAM 5xDRAM
Latency(us)
Data Size (Bytes)
NRCIO-RW NRCIO-RR
0
0.5
1
1.5
2
2.5
3
3.5
256K
1M
4M
256K
1M
4M
256K
1M
4M
1xDRAM 2xDRAM 5xDRAM
Latency(ms)
Data Size (Bytes)
NRCIO-RW NRCIO-RR

DataWorks Summit, 2019 12Network Based Computing Laboratory
NVRAM-TO-NVRAM RDMA-Aware Communication with NRCIO
• Comparison of communication latency using NRCIO RDMA read and write communication
protocols over InfiniBand EDR HCA vs. DRAM
• {Ax, By} NVRAM emulation mode = Ax NVRAM read slowdown and Bx NVRAM write slowdown
vs. NVRAM
• High end-to-end latencies due to slower writes to non-volatile persistent memory
• E.g., 3.9x for {1x,2x} and 8x for {2x,5x}
0
0.5
1
1.5
2
2.5
3
3.5
256K 1M 4M 256K 1M 4M 256K 1M 4M
No Persist
(D2D)
1x,2x 2x,5x
Latency(ms)
Data Size (Bytes)
NRCIO-RW NRCIO-RR
0
5
10
15
20
25
64 1K 16K 64 1K 16K 64 1K 16K
No Persist
(D2D)
1x,2x 2x,5x
Latency(us)
Data Size (Bytes)
NRCIO-RW NRCIO-RR

DataWorks Summit, 2019 13Network Based Computing Laboratory
• NRCIO: NVM-aware RDMA-based Communication
and I/O Schemes
• NRCIO for Big Data Analytics
• NVMe-SSD based Big Data Analytics
• Conclusion and Q&A
Presentation Outline

DataWorks Summit, 2019 14Network Based Computing Laboratory
• Files are divided into fixed sized blocks
– Blocks divided into packets
• NameNode: stores the file system namespace
• DataNode: stores data blocks in local storage
devices
• Uses block replication for fault tolerance
– Replication enhances data-locality and read
throughput
• Communication and I/O intensive
• Java Sockets based communication
• Data needs to be persistent, typically on
SSD/HDD
NameNode
DataNodes
Client
Opportunities of Using NVRAM+RDMA in HDFS

DataWorks Summit, 2019 15Network Based Computing Laboratory
Design Overview of NVM and RDMA-aware HDFS (NVFS)
• Design Features
• RDMA over NVM
• HDFS I/O with NVM
• Block Access
• Memory Access
• Hybrid design
• NVM with SSD as a hybrid
storage for HDFS I/O
• Co-Design with Spark and HBase
• Cost-effectiveness
• Use-case
Applications and Benchmarks
Hadoop MapReduce Spark HBase
Co-Design
(Cost-Effectiveness, Use-case)
RDMA
Receiver
RDMA
Sender
DFSClient
RDMA
Replicator
RDMA
Receiver
NVFS
-BlkIO
Writer/Reader
NVM
NVFS-
MemIO
SSD SSD SSD
NVM and RDMA-aware HDFS (NVFS)
DataNode
N. S. Islam, M. W. Rahman , X. Lu, and D. K.
Panda, High Performance Design for HDFS with
Byte-Addressability of NVM and RDMA, 24th
International Conference on Supercomputing
(ICS), June 2016

DataWorks Summit, 2019 16Network Based Computing Laboratory
Evaluation with Hadoop MapReduce
0
50
100
150
200
250
300
350
Write Read
AverageThroughput(MBps)
HDFS (56Gbps)
NVFS-BlkIO (56Gbps)
NVFS-MemIO (56Gbps)
• TestDFSIO on SDSC Comet (32 nodes)
– Write: NVFS-MemIO gains by 4x over
HDFS
– Read: NVFS-MemIO gains by 1.2x over
HDFS
TestDFSIO
0
200
400
600
800
1000
1200
1400
Write Read
AverageThroughput(MBps)
HDFS (56Gbps)
NVFS-BlkIO (56Gbps)
NVFS-MemIO (56Gbps)
4x
1.2x
4x
2x
SDSC Comet (32 nodes: 80 GB, SATA-SSDs) OSU Nowlab (4 nodes: 8 GB, NVMe-SSDs)
• TestDFSIO on OSU Nowlab (4 nodes)
– Write: NVFS-MemIO gains by 4x over
HDFS
– Read: NVFS-MemIO gains by 2x over
HDFS

DataWorks Summit, 2019 17Network Based Computing Laboratory
Evaluation with HBase
0
100
200
300
400
500
600
700
800
8:800K 16:1600K 32:3200K
Throughput(ops/s)
Cluster Size : No. of Records
HDFS (56Gbps) NVFS (56Gbps)
HBase 100% insert
0
200
400
600
800
1000
1200
8:800K 16:1600K 32:3200K
Throughput(ops/s)
Cluster Size : Number of Records
HBase 50% read, 50% update
• YCSB 100% Insert on SDSC Comet (32 nodes)
– NVFS-BlkIO gains by 21% by storing only WALs to NVM
• YCSB 50% Read, 50% Update on SDSC Comet (32 nodes)
– NVFS-BlkIO gains by 20% by storing only WALs to NVM
20%21%

DataWorks Summit, 2019 18Network Based Computing Laboratory
Opportunities to Use NVRAM+RDMA in MapReduce
Disk Operations
• Map and Reduce Tasks carry out the total job execution
– Map tasks read from HDFS, operate on it, and write the intermediate data to local disk (persistent)
– Reduce tasks get these data by shuffle from NodeManagers, operate on it and write to HDFS (persistent)
• Communication and I/O intensive; Shuffle phase uses HTTP over Java Sockets; I/O operations take
place in SSD/HDD typically
Bulk Data Transfer

DataWorks Summit, 2019 19Network Based Computing Laboratory
Opportunities to Use NVRAM in MapReduce-RDMA
DesignInputFiles
OutputFiles
IntermediateData
Map Task
Read Map
Spill
Merge
Map Task
Read Map
Spill
Merge
Reduce Task
Shuffle Reduce
In-
Mem
Merge
Reduce Task
Shuffle Reduce
In-
Mem
Merge
RDMA
All Operations are In-
Memory
Opportunities exist to
improve the
performance with
NVRAM

DataWorks Summit, 2019 20Network Based Computing Laboratory
NVRAM-Assisted Map Spilling in MapReduce-RDMA
InputFiles
OutputFiles
IntermediateData
Map Task
Read Map
Spill
Merge
Map Task
Read Map
Spill
Merge
Reduce Task
Shuffle Reduce
In-
Mem
Merge
Reduce Task
Shuffle Reduce
In-
Mem
Merge
RDMA
NVRAM
 Minimizes the disk operations in Spill phase
M. W. Rahman, N. S. Islam, X. Lu, and D. K. Panda, Can Non-Volatile Memory Benefit MapReduce Applications on HPC Clusters? PDSW-DISCS, with SC 2016.
M. W. Rahman, N. S. Islam, X. Lu, and D. K. Panda, NVMD: Non-Volatile Memory Assisted Design for Accelerating MapReduce and DAG Execution Frameworks on
HPC Systems? IEEE BigData 2017.

DataWorks Summit, 2019 21Network Based Computing Laboratory
Comparison with Sort and TeraSort
• RMR-NVM achieves 2.37x benefit for Map
phase compared to RMR and MR-IPoIB;
overall benefit 55% compared to MR-IPoIB,
28% compared to RMR
2.37x
55%
2.48x
51%
• RMR-NVM achieves 2.48x benefit for Map
phase compared to RMR and MR-IPoIB;
overall benefit 51% compared to MR-IPoIB,
31% compared to RMR

DataWorks Summit, 2019 22Network Based Computing Laboratory
Evaluation of Intel HiBench Workloads
• We evaluate different HiBench
workloads with Huge data sets
on 8 nodes
• Performance benefits for
Shuffle-intensive workloads
compared to MR-IPoIB:
– Sort: 42% (25 GB)
– TeraSort: 39% (32 GB)
– PageRank: 21% (5 million pages)
• Other workloads:
– WordCount: 18% (25 GB)
– KMeans: 11% (100 million samples)

DataWorks Summit, 2019 23Network Based Computing Laboratory
Evaluation of PUMA Workloads
• We evaluate different PUMA
workloads on 8 nodes with
30GB data size
• Performance benefits for
Shuffle-intensive workloads
compared to MR-IPoIB :
– AdjList: 39%
– SelfJoin: 58%
– RankedInvIndex: 39%
• Other workloads:
– SeqCount: 32%
– InvIndex: 18%

DataWorks Summit, 2019 24Network Based Computing Laboratory
• NRCIO: NVM-aware RDMA-based Communication
and I/O Schemes
• NRCIO for Big Data Analytics
• NVMe-SSD based Big Data Analytics
• Conclusion and Q&A
Presentation Outline

DataWorks Summit, 2019 25Network Based Computing Laboratory
Overview of NVMe Standard
• NVMe is the standardized interface
for PCIe SSDs
• Built on ‘RDMA’ principles
– Submission and completion I/O
queues
– Similar semantics as RDMA send/recv
queues
– Asynchronous command processing
• Up to 64K I/O queues, with up to 64K
commands per queue
• Efficient small random I/O operation
• MSI/MSI-X and interrupt aggregation
NVMe Command Processing
Source: NVMExpress.org

DataWorks Summit, 2019 26Network Based Computing Laboratory
Overview of NVMe-over-Fabric
• Remote access to flash with NVMe
over the network
• RDMA fabric is of most importance
– Low latency makes remote access
feasible
– 1 to 1 mapping of NVMe I/O queues
to RDMA send/recv queues
NVMf Architecture
I/O
Submission
Queue
I/O
Completion
Queue
RDMA Fabric
SQ RQ
NVMe
Low latency
overhead compared
to local I/O

DataWorks Summit, 2019 27Network Based Computing Laboratory
Design Challenges with NVMe-SSD
• QoS
– Hardware-assisted QoS
• Persistence
– Flushing buffered data
• Performance
– Consider flash related design aspects
– Read/Write performance skew
– Garbage collection
• Virtualization
– SR-IOV hardware support
– Namespace isolation
• New software systems
– Disaggregated Storage with NVMf
– Persistent Caches
Co-design

DataWorks Summit, 2019 28Network Based Computing Laboratory
Evaluation with RocksDB
0
5
10
15
Insert Overwrite Random Read
Latency (us)
POSIX SPDK
0
100
200
300
400
500
Write Sync Read Write
Latency (us)
POSIX SPDK
• 20%, 33%, 61% improvement for Insert, Write Sync, and Read Write
• Overwrite: Compaction and flushing in background
– Low potential for improvement
• Read: Performance much worse; Additional tuning/optimization required

DataWorks Summit, 2019 29Network Based Computing Laboratory
Evaluation with RocksDB
0
5000
10000
15000
20000
Write Sync Read Write
Throughput (ops/sec)
POSIX SPDK
0
100000
200000
300000
400000
500000
600000
Insert Overwrite Random Read
Throughput (ops/sec)
POSIX SPDK
• 25%, 50%, 160% improvement for Insert, Write Sync, and Read Write
• Overwrite: Compaction and flushing in background
– Low potential for improvement
• Read: Performance much worse; Additional tuning/optimization required

DataWorks Summit, 2019 30Network Based Computing Laboratory
QoS-aware SPDK Design
0
50
100
150
1 5 9 13 17 21 25 29 33 37 41 45 49
Bandwidth(MB/s)
Time
Scenario 1
High Priority Job (WRR) Medium Priority Job (WRR)
High Priority Job (OSU-Design) Medium Priority Job (OSU-Design)
0
1
2
3
4
5
2 3 4 5
JobBandwidthRatio
Scenario
Synthetic Application Scenarios
SPDK-WRR OSU-Design Desired
• Synthetic application scenarios with different QoS requirements
– Comparison using SPDK with Weighted Round Robbin NVMe arbitration
• Near desired job bandwidth ratios
• Stable and consistent bandwidth
S. Gugnani, X. Lu, and D. K. Panda, Analyzing, Modeling, and
Provisioning QoS for NVMe SSDs, 11th IEEE/ACM International
Conference on Utility and Cloud Computing (UCC), Dec 2018

DataWorks Summit, 2019 31Network Based Computing Laboratory
Conclusion and Future Work
• Big Data Analytics needs high-performance NVM-aware RDMA-based
Communication and I/O Schemes
• Proposed a new library, NRCIO (work-in-progress)
• Re-design HDFS storage architecture with NVRAM
• Re-design RDMA-MapReduce with NVRAM
• Design Big Data analytics stacks with NVMe and NVMf protocols
• Results are promising
• Further optimizations in NRCIO
• Co-design with more Big Data analytics frameworks
• TensorFlow, Object Storage, Database, etc.

DataWorks Summit, 2019 32Network Based Computing Laboratory
Thank You!
Network-Based Computing Laboratory
http://nowlab.cse.ohio-state.edu/
The High-Performance Big Data Project
http://hibd.cse.ohio-state.edu/
luxi@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~luxi
shankard@cse.ohio-state.edu
http://www.cse.ohio-state.edu/~shankar.50