Big Data

Cloud Infrastructure for Big Data Analysis
Inter-Cloud Infrastructure for Big Data Analysis
Building a Testbed Infrastructure on Overlay Cloud
Using SINET5 network infrastructure (100Gbps Network)
Cooperation with various computing resources
Private cloudPublic cloudSupercomputer
Providing the Science Data Repository
Testbed: Peta Bytes class object storage
Real System: Exa Bytes class object storage

High Bandwidth& Secure Networkby SINET5

Academic Cloud@ Hokkaido Univ.

etc.
Public Cloud Resource@ SINET Inter-Cloud

Exabyte DB on
Global Object Storage
Cloud Resource@ NII

Collaboration work with NII, NIG, Hokkaido Univ. and Kyushu Univ.
For more details, Please visit following booths#2908, “National Institute of Informatics”#3658, “Hokkaido University”#3637, “Kyushu University”

Cloud Platform@ Kyushu Univ.

TSUBAME2.5 & 3.0@Tokyo Tech.

@National Institute of Genetics

Acknowledgments. This research is supported by CREST, JST (Research Area: Advanced Core Technologies for Big Data Integration).

Cloud-Based Burst Buffer for I/O Acceleration
Background
Cloud storage cannot provide enough I/O throughput for data intensive applications.
Loose consistency model in cloud storage
No support of N-1 Concurrent write
ProposalCloud-based Burst Buffer as a new tier in cloud storage hierarchy.
Several dedicated instances as burst buffers to accelerate accesses by buffering intermediate data.

SystemThe whole system consists of several Sub CloudBBs (SCBB)
Each SCBB consists of a Master (metadata server) and several
IOnodes (data server).
Fault Tolerance supports
Computenode X

Shared Cloud Storage

Consistent hash

Master 0

IOnodeMaster Y

IOnodeMaster M-1

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnode

IOnodeSCBB 0SCBB YSCBB M-1

Burst buffers

10000

Amazon S3 (read)

8000

Amazon S3 (write)

6000
4000
2000
1248163264
The number of CNs ( = The number of IONs)

400

CloudBB write

350

Amazon S3 (read)300

Amazon S3 (write)

250
200
150
100
500

Cloud for HPC

1248163264
The number of CNs (= The number of IONs)

50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0

35000

CloudBB w/ 1 MN (left axis)
CloudBB w/ 2 MNs (left axis)
CloudBB w/ 4 MNs (left axis)
Amazon S3 (left axis)

800
700
600
500
400
300
200
100

124816 32 64 128
The number of CNs

0

CloudBB w/ 3 IONs

Real Application: Supernovae
3500

Amazon S330000

Execution Time (seconds)

CloudBB (write)

CloudBB read

Real Application: Montage
Execution Time (seconds)

12000

450

Metadata Performance
Relative CloudBB performance
to Amazon S3Throughput (MiB/sec)

CloudBB (read)

Ramdom I/O rate (ops/sec)

500

14000

0Random IOFile creation rate (ops/sec)

Sequential IO

25000
20000
15000
10000
5000
0

12481632
The number of CNs

643000

CloudBB w/ 3 IONs

Amazon S32500
2000
1500
1000
500
0128

12481632The number of CNs

64

128

Acknowledgments. This research is supported by CREST, JST (Research Area: Advanced Core Technologies for Big Data Integration).

Poster at SC16:

Execution time [sec]0.2024816# of nodes(2 MPI ranks, 12 OpenMP threads per node)“Towards Understanding HPC-Big Data Convergence Using Cloud Platforms”, Shweta Salaria, KevinBrown, Hideyuki Jitsumoto, Satoshi Matsuoka

Execution time [sec]Execution time [sec]Execution time [sec]

NICAM-DC-MINI: A compute-intensive miniapp from Fiber miniapp suite
Backgroud
TSUBAME-KFC
AWS EC2 c4.8xlarge instances
Cloud platforms exhibit elasticity, flexibility, usability and scalability, which
Computation
Communication
Stall
Computation
Communication
Stallhave been attracting users to use these environments as a cost effective
90,00090,00080,00080,00070,00070,000measure to run their applications or businesses. However, the feasibility of
60,00060,00050,00050,00040,00040,000running high performance computing applications on clouds has always
30,00030,00020,00020,000been a concern mainly due to virtualization overheads and high-latency in10,000
10,0000014161416terconnection network.
# of nodes (10 MPI ranks per node)# of instance (10 MPI ranks per instance)
Goals
To investigate the potential role of these virtual machines in addressing Graph500 Benchmark at Scale 26
TSUBAME-KFC
AWS EC2 c4.8xlarge instances
the needs of HPC and data-intensive workloads
App Time
MPI Time
App Time
MPI Time
Experimentation
1.6
1.6
1.4
1.4
1.2
1.2
Performance evaluation of applications on AWS C4 instances against the110.8
0.8
0.6
0.6
baseline results of a supercomputer, TSUABME-KFC
0.40.40.2024816# of instance(8 MPI ranks, 12 OpenMP threads per instance)* 2 MPI ranks and 12 openmp threads per node achieved the best performance on TSUBAME-KFC. In case of AWS EC2, it
was 8 MPI ranks and 12 openmp threads per instance.

TSUBAME-KFC: Intel Xeon E5-2620v2 x2, InfiniBand FDR
Amazon EC2 c4.8xlarge instance: Intel Xeon E5-2666v3, 10Gbps Ethernet

Acknowledgments. This research is supported by CREST, JST (Research Area: Advanced Core Technologies for Big Data Integration).

http://www.gsic.titech.ac.jp/sc16