東北大学新スーパーコンピュータシステムの紹介と高性能計算に関する研究開発活動

Hiroaki Kobayashi

Director and Professor
Cyberscience Center
Tohoku University

koba@cc.tohoku.ac.jp
PC Cluster Workshop in Sendai
Feb. 19, 2016

Hiroaki Kobayashi, Tohoku University

Missions of Cyberscience Center
As a National Supercomputer Center
High-Performance Computing Center founded in 1969
Offering leading-edge high-performance computing environments
to academic users nationwide in Japan
24/7 operations of large-scale vector-parallel and scalarparallel systems
1500 users registered in AY 2014
1969
User supports
Benchmarking, analyzing, and tuning users’ programs
Holding seminars and lectures
SX-1 in 1985
Supercomputing R&D, collaborating work with NEC
Designing next-generation high-performance computing
systems and their applications for highly-productive
supercomputing
SX-3 in 1994
57-year history of collaboration between Tohoku University
and NEC on High Performance Computing
Education
Teaching and supervising BS, MS and Ph.D. Students as a
SX-7 in 2003
cooperative laboratory of graduate school of information
sciences, Tohoku university
PC Cluster Workshop in Sendai

2

1982

SX-2 in 1989

SX-4 in 1998

SX-9 in 2008

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Cooling Facility of HPC Building
Evaporation
R-1-1

R-1-2

R-1-3

Fresh Air

R-1-4

RF


AHU-2-1

Coolant

2F

EV

PC-1-2         PC-1-1

Hot Air

AHU-2-3

PC-1-3PC-1-4

AHU-2-2

Cold Air

1F

AHU-1-1

Cluster 5HE-1-3HE-1-4HE-1-2

AHU-1-3

AHU-1-5

AHU-1-7

AHU-1-9

Cluster 4

Cluster 3

Cluster 2

Cluster 1

AHU-1-6

AHU-1-8

CappingareaEVof cold-aisle

HE-1-1HE-1-5

AHU-1-2

PC Cluster Workshop in Sendai

AHU-1-4

AHU-1-10

4February 19, 2016

Hiroaki Kobayashi, Tohoku University

Organization of Tohoku Univ. SX-ACE System

IXS

IXS

Cluster 0

Cluster 4Core

CPU(Socket)

NodeCluster

Total System

Size

1

4 Cores

1CPU

512 Nodes

5 Clusters

Performance(VPU+SPU)

69GFlop/s(68GF+1GF)

141Tflop/s(139TF+ 2TF)

707Tflop/s(697TF+10TF)

276GFlop/s(272GF+4GF)

Mem. BW

256GB/s

131TB/s

655TB/sMemory Cap.

64GB

32TB

160TB

IXS Node BW
PC Cluster Workshop in Sendai-

4GB/s x2
5

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Features of the SX-ACE Vector Processor
4 Core Configuration, each with High-Performance Vector-Processing Unit and Scalar Processing Unit
272Gflop/s of VPU + 4Gflop/s of SPU per socket
SX-ACE Processor Architecture
68Gflop/s + 1Gflop/s per core
1MB private ADB per core (4MB per socket)
Core 0•Software-controlled on-chip memory for vector load/store
4x compared with SX-9
4-way set-associative
MSHR with 512 entries (address+data)
256GB/s to/from Vec. Reg.
4B/F for Multiply-Add operations
256 GB/s memory bandwidth, Shared with 4 cores

256GB/s

Core
2

Core
3

4GB/s

4GB/s

MSHR

256GB/s•

Crossbar
256GB/s
MC

MC

MC

MC

MC

MC

MC

MC

MC

MC

MC

MC

MC

MC

MC

1B/F in 4-core Multiply-Add operations～ 4B/F in 1-core Multiply-Add operations

Core1

ADB 1MB

MC

•

VPU

SPU

RCU

•
•
•
•
•

256GB/s

Source: NEC
128 memory banks per socket
Other improvement and new mechanisms to enhance vector processing capability, especially for efficient handling of
short vectors operations and indirect memory accesses•

Out of Order execution for vector load/store operations
Advanced data forwarding in vector pipes chaining
Shorter memory latency than SX-9
21stPC
WSSP
Cluster Workshop in Sendai

6

February2015
February18-19,
19, 2016

Hiroaki Kobayashi, Tohoku University

High Demands for Vector Systems in Memory-Intensive,
Science and Engineering Applications
Upper Limit of 24/7
service

Floating-Point Operations Serviced(×1015)

10,000,000

Academia (Inside
Tohoku Univ) Electromagnetic
Field Analysis

Advanced CFD
model for digitalflight

SX-2R
SX-3N
SX-4
1,000,000

Industry

SX-7
SX-7&SX-7C
SX-9&SX-7C

100,000

SX-9
SX-9&SX-ACE

Academia (Outside
Tohoku Univ)

Upper Limit of 24/7 service

10,000Catalyzer Design
1,000

Atmosphere Analysis&
Whether Forecasting100

TurbineDesign10

1

Earthquake
analysis

Combustion

92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14Year

PC Cluster Workshop in Sendai

Magnetic recoding
device Design

7

February 19, 2016

Performance Evaluations of SX-ACE

8

Hiroaki Kobayashi, Tohoku University

Specifications of Modern High End Systems
No. ofSockets/

Perf./
SocketNode(Gﬂop/s)

SX-ACE

1

SX-9
ES2

System

LX 406(Ivy Bridge)
FX10(SPARK64IX)K(SPARK64VIII)
SR16K M1
(Power7)

No. of Perf. /core

Mem.
BW

On-chip

NW BW

Sys.

mem

(GB/sec)

B/F

1MB ADB /core

2 x 4 IXS

1.0

2 x 128 IXS

2.5

2 x 64IXS

2.5

5 IB

0.26

Cores

(Gﬂop/s)

256

4

64

256

16

102.4

1

102.4

256

8

102.4

1

102.4

256

2

230.4

12

19.2

59.7

1

236.5

16

14.78

85

12MB shared L2

1

128

8

16

64

6MB Shared L2

4

245.1

8

30.6

128

256KB L2/core
32MB shared L3

GB/sec

256KB
ADB/core
256KB ADB/core
256KB L2/core
30MB Shared L3

5 - 50 Tofu
NW
5 - 50 Tofu
NW

0.36
0.5

2 x 24 - 96
0.52
custom NW

Remarks: Listed performances are obtained based on total Multiply-Add performances of individual systems
PC Cluster Workshop in Sendai
February 19, 2016
9

Hiroaki Kobayashi, Tohoku University

Applications Used for Evaluation
Applications
QSFDM
GLOBE
Barotropicocean
MHD (FDM)

Seism 3D

FieldsSeismology
OGCM(Ocean General
Circulation Model)

MHD
Seismology

MHD(Spectral)

MHD

TURBINE

CFD

BCM

CFD

Methods

Mem Access

Code

Mesh Size

Characteristics

B/F

Spherical 2.5D

Stencil with sequential

FDM

memory accesses

Shallow water

Stencil with sequential

model

memory accesses

Finite Diﬀerence

Stencil with sequential

Methodmemory accesses

Finite Diﬀerence

Stencil with sequential

1024 x 512 x 512 †Modelmemory accesses

4096 x 2048 x 2048 ‡Pseudo spectral
MethodDNS

Stride memory access

ACE

2.16

0.78

4322 x 216

1.97

1.11

200 x 1920 x 32

3.04

1.41

2.15

1.68

2.21

2.18

1.78

5.47

7.01

5.86

3600 x 3072 x 2048 ‡91 x 91 x 91 x 13

with short vectors

B/F on4.3 x 107 grids

900 x 768 x 96 †Indirect memory access

ActualNavier Stokes

Stencil and Indirect(128 x 128 x 128 cells)

Equationmemory access

x 64 Cubes† for single-node evaluation‡

PC Cluster Workshop in Sendai

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for multi-node evaluation

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Sustained Memory Bandwidth•

STREAM (TRIAD)

Stream memory bandwidth (GB/sec.)

300
SX-ACE
SR16K M1(Power7)

LX406(Ivy Bridge)
FX10 (SPARC64IX)

225

150

75

0
0

2

4

6

8

10

12

14

16

Number of threads (activated cores)
PC Cluster Workshop in Sendai

11

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Sustained Single CPU Performance
SX-9
LX406(Ivy Bridge)

47%
45.9%

SX-ACE
SR16K M1(Power7)
45

41.1%

39.4%

35.6%
32.2%
30.1%

29.4%

30

27.4%

17.5%

17.5%
12.3%

15

10.4%
9%
6.7%

Efficiency [%]Sustained Performance[Gflop/s]

60

9.8%
7.7%
5.5%

7.9%

3.5%

2.8%

7.0%
3%

5.9%
3.4%
2.5%

3%
3.5%

0

QSFDM

Barotropic

2.16

1.97

MHD
(FDM）

Seism3D

MHD(Spectral)

TURBINE

BCM

1.78

7.01

Code B/F
Memory

3.04

2.15

2.21

Intensity

PC Cluster Workshop in Sendai

12February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance of Indirect Memory Accesses in TURBINE

Sustained Performance[Gflop/s]

DO 200 K=1,KF;
DO 200 J=1,JF;
DO 200 L=lstart,lend;
ADB ON
DO 200 I=IS(L),IT(L) ADB OFF
DQL = RH(AMIN0(I-2,IS(L)),J,K) - RH(AMIN0(I-3,IS(L)),J,K)
DQM = RH(I-1,J,K) - RH(AMIN0(I-2,IS(L)),J,K)
DQN = RH(I,J,K) - RH(I-1,J,K)
DQP = RH(AMIN0(I+1,IT(L)),J,K) - RH(I,J,K)
DQR = RH(AMIN0(I+2,IT(L)),J,K) - RH(AMIN0(I+1,IT(L)),J,K)
・・・・・・・・・・・・・

SX-9

DQLM=SQL*DMAX1(0.0D0,DMIN1(AQL,COEFQ*SQL*DQM,
COEFQ*SQL*DQN))
DQMM=SQM*DMAX1(0.0D0,DMIN1(AQM,COEFQ*SQM*DQL,COEFQ*SQM*DQN))
DQNM=SQN*DMAX1(0.0D0,DMIN1(AQN,COEFQ*SQN*DQM,
COEFQ*SQN*DQL))
DQNN=SQN*DMAX1(0.0D0,DMIN1(AQN,COEFQ*SQN*DQM,
COEFQ*SQN*DQP))
・・・・・・・・・・・・・

SX-ACE SX-ACE SX-ACE
1core
2cores 4cores

DDQM=DQNM-2.0D0*DQMM+DQLM
DDQN=DQPN-2.0D0*DQNN+DQMN・・・・・・・・・・・・・
QLL(I,J,K,M)=DQL0+COEFB*DPQM0+COEFC*DMQN0QRR(I,J,K,M)=DQR0-COEFB*DMQP0-COEFC*DPQN0200 CONTINUEPC Cluster Workshop in Sendai

13

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance of Indirect Memory Accesses in TURBINE
on Modern HPC Processors

20

15
11.1%

10.6%

9.6%

10

5

Efficiency [%]Sustained Performance[Gflop/s]

18%0

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Sustained Performance[Gflop/s]

Performance of Short-Vector Processing in TURBINE (1/2)

DO 10 M=MMIN,MMAX;
DO 10 K=KSTA,KEND;
DO 10 I=ISTA,IEND
UC11=U(I-1,J,K,1)*XIX(I-1,J,K,1)&+U(I-1,J,K,2)*XIX(I-1,J,K,4)&+U(I-1,J,K,3)*XIX(I-1,J,K,7)
UC22=U(I,J,K,1)*XIX(I,J,K,1)&+U(I,J,K,2)*XIX(I,J,K,4)&+U(I,J,K,3)*XIX(I,J,K,7)
・・・・・・・・・・・・

SX-9

SX-ACE SX-ACE SX-ACE
1core
2cores 4cores

AJXIX1=(AJR(I-1,J,k)*XIX(I-1,J,k,1)&+AJR(I,J,k)*XIX(I,J,k,KL))*0.5D0
AJXIX2=(AJR(II,JJ,kk)*XIX(II,JJ,kk,KL+3)&+AJR(I,J,k)*XIX(I,J,k,KL+3))*0.5D0・・・・・・・・・・・・
10 continue

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance of Short-Vector Processing in TURBINE (2/2)

Sustained Performance[Gflop/s]

ADB OFFSX-9

ADB ONSX-ACE SX-ACE SX-ACE
1core
2cores 4cores

PC Cluster Workshop in Sendai

16

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance of Short-Vector Processing in TURBINE
on Modern HPC Processors
10

7.5
5.6%

5
2.7%
1.8% 2.5

Efficiency [%]Sustained Performance[Gflop/s]

8.3%

0

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Sustained Performance of Barotropic Ocean Modelon Multi-Node Systems
ES2
SR16K M1(Power7)

Perforrmance (Gflop/s)

1,200

SX-ACEFX10 (SPARC64IX)

900

LX406(Ivy Bridge)

SMP-MPI Hybrid

600

300

0
0

8

16

24

32

40

48

56

64

72

80

88

96

Number of MPI processes (Nodes)
PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance Evaluation of SX-ACE by using the HPCG Benchmark
HPCG (High Performance Conjugate Gradients) is designed to exercise
computational and data access patterns that more closely match a broad set ofimportant applications,
HPL for top500 is increasingly unreliable as a true measure of system performancefor a growing collection of important science and engineering applications.
HPCG is a complete, stand-alone code that measures the performance of basic
operations in a unified code:
Sparse matrix-vector multiplication.
Sparse triangular solve.
Vector updates.
Global dot products.
Local symmetric Gauss-Seidel smoother.
Driven by multigrid preconditioned conjugate gradient algorithm that exercises the
key kernels on a nested set of coarse grids.
Reference implementation is written in C++ with MPI and OpenMP support.
PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Optimizations of the the HPCG Benchmark for SX-ACE*Komatsu et al.@SC15

Data packing for vector-friendly
matrix memory allocation of sparse
matrices
Parallelization by using coloring andhyperplane methods
Selective data caching and blocking
for effective use of ADB

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance to Peak[%]

Efficiency






CSC : Tohoku UnivEﬃciency of Sustained

Efficiency Comparison in the HPCG Performance (1/2)
HPCG/W

Xeon Phi

1.052

25.820

Xeon

2.196

35.454GPU

1.452

48.582

Blue Gene

1.438

36.706FX

3.372

48.969SX

11.259112.987
SX-ACEPower-Eﬃciency[Mﬂops/W]

Bluegene

Xeon

Phi

GPU

CSC : Tohoku Univ



FX


PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Efficiency Comparison in the HPCG Performance (2/2)

30x 





20x

Texas Advanced Computing Center
DOE/SC Oak Ridge
SURF Sara

SC Chinese Academy of Sciences
TIAP : Chinese Academy of Sciences
Chelyabinsk
CINECAHLRS/University of Stuttgart
Atos Angers
Meteo France

DKRZ
Bull Angers
SURF sara, Amsterdam

DOE/NNSA/LANL/SNL
DOE/SC/Argonne Lab.
Cyfronet
HLRS/Universitaet Stuttgart

GSIC Center TITEC
Forshungszentrum Julich
University of Rijeka

GSIC Center,TITECSaint Petersburg Polytechnic Univ
KAUST / Jeddah
CSCS

Intel
Moscow State UnivKiel
Lvliang

NSC / Linkoping
NSCC/Guangihou
Exploration and Production Eni S.p.A
Shanghai Supercomputer Center

Joint Supercomputer Center RAS
CINECA
Cambridge U.


ITC: Naogya Univ.
National Institute for Fusion Science
IFERC EU(F4E)
DOE/SC/LBNL/NERSC
CEA/TGCC-GENCIIT Services Provider
EPSRC
Univ. Heidelberg and Mannheim
LNCC
The Univ. of Tokyo
Forschungszentrum Julich
RIKEN AICS
Max-Planck
CEIST / JAMSTEC
Kiel
CMC : Osaka Univ
CSC : Tohoku Univ

PC Cluster Workshop in Sendai

Max-Planck-Institut fur Mikrostrukturphysik
NASA / Mountain View
CALMIP / University of Toulouse

Meteo France
GENCI-CINES
Stanford University / Palo Alto
Leibniz Rechenzentrum

February 19, 201622

                                
 

0

GPU
Phi
Xeon
BluegeneFX
SX-ACE

25x

15x
 

10x

5x
 

R&D of A Real-Time Tsunami Inundation
Forecasting System on SX-ACE

23

Hiroaki Kobayashi, Tohoku University

Background: 2011 East-Japan Great Earthquake

Sendai, Miyagi•

Around 20,000 victims (dead or missing), mainly due to Tsunami,
100,000 people evacuated to shelters in the first several months.

•

A huge of debris of houses, cars and buildings remained in the
coastal area of Tohoku over one year•

Important infrastructures such as gas/water/electricity/train/roadare destroyed and/or stopped their services for one month insendai city

Magnitude 9.0, the Largest in Japan and the 5th largest in the
world

Cent
er of
quake

Main shock at 2:46pm, March 11, & huge Tsunami 30 min later...
Toho
ku

•
•

Area

(Hometown of Tohoku Univ)

Courtesy of Prof. Furumura,U.of Tokyo

Sendai Station
PC Cluster Workshop in Sendai

24

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Motivation:
Serious Damage to Sendai Area Due to 2011
Tsunami Inundation    

             


Huge
Tsunami hits coastal area of Tohoku regionrea 

東北大 越村教授提供
TohokuUniversity

Inu
nd

atio

nA

 

Courtesy ofProf. Koshimura     

PC Cluster Workshop in Sendai

25

Sendai Airport

February 19, 2016

Hiroaki Kobayashi, Tohoku University

It’s not End: High Probability of Big Earthquakes in Japan• Japan may be hit by severe earthquakes and large tsunamis
in the next 30 years

Seismogenic Zones

Ishinomaki

70 % probability in thenext 30 years

SendaiTokyo

PC Cluster Workshop in Sendai

Kochi

東北大 越村教授提供
The 2011 Great
TohokuEarthquake 

88 % probability in thenext 30 years

Shizuoka
26

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Objective of Our Work

Prompt responses to disaster to reduce damages such as warning
evacuation from dangerous zones and rescuing survivors as soon as
possible.
Detailed and highly accurate analysis and forecasting of Tsunami
Inundation soon after the Big Earthquake is mandatory.
Enhancement of social resiliency against natural disasters by precise
simulation using HPC to satisfy these demands

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Design and Development of
A Real-Time Tsunami Inundation Forecasting System
GPS-Observation

Simulation on SX-ACE

Information Delivery

Inundation depth




()
Inundation
simulation

based on GPS data

10-m mesh modelsof coastal cities< 8 min< 8 min

Fault estimation





etc

Just-In-Time accessof Visualized information
by local governments< 4 min< 20 minPC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

System Organization
SX-ACE joins the system as soon as alarge earthquake (>M7) occurs

Earthquake

RAPiD SystemSX-ACE

RTK-GPS analysis
Quake Information
GPS Obs. data

Local
Government

Inundation forecasts
Damage estimations 

PC Cluster Workshop in Sendai

Fault model estimation
SX Job submit

NQSII: Urgency
Function

Delivery Server

Tsunami Analysis

Tohoku Univ.
International Researchof Disaster Science

Tohoku Univ.
Cyberscience Center

29

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Emergency Job Handling by NQSII of SX-ACE

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Simulation: Target Code & Areas
Target Code TUNAMI: Tohoku University’s Numerical Analysis
Model for Investigating Tsunami••

Developed by Prof. Koshimura of Tohoku University
Authorized by UNESCO and Japanese Government

Governing Equations•

Non-Liner Shallow Water Equations

Numerical Scheme•

Staggered Leap-Frog Finite Difference Method

Miyagi
Miyagi

Memory-intensive application•

Shizuoka
Shizuoka

B/F = 1.82 (single precision)

Kochi
Kochi

Target Areas: Miyagi, Shizuoka & Kochi
PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Computation Domain
Hierarchical multi-level grid models

Region 1

Computation Domain of Kochi City:

Region 2

• 1244km x 826km• 5 nested grids• 6 hours of Tsunami InundationΔt = 0.1 sec.
Region

PC Cluster Workshop in Sendai

Grid
Size
(m)

Num. ofGrid(X)

1

810

1536

1020

2

270

1680

990

3

90

2292

1260

4

30

1782

1188

5

10

3504

2364
32

Num. ofGrid(Y)Reg. 5 
Region 4

NankaiTroughEarthquake M9.1
Region 3

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Program Structure Doubly nested loops

START
Initial PhaseTime Step

Calculate Mass
Conservation
Data interchange
between Regions

gh
i
H

t
os

er

k

s

l
e
n

DO J=2, JF
DO I=2, IF(Longitude)

Parallelized 
Vectorizedc

ZZ = Z(I,J,1) - RX*(M(I,J,1)-M(I-1,J,1))- RY*(N(I,J,1)-N(I,J-1,1))

StencilEND DO

Calculate Motion
EquationEND DO Tuning

Data interchange
between Regions

Inlining subroutines
 Optimization of I/O routines Vectorization&Parallelization


Output Data



END

PC Cluster Workshop in Sendai(Latitude)33

Vect. Ration=99.6%,Vect. Length=235

ADB Tuning of Stencil kernels

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Visualization of Simulation Results by Delivery Server








 























Inundation Start Time    








Web
Interface

















 







 

（Tsunami Arrival Time）
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Maximum Inundation Depth
February 19, 2016

Hiroaki Kobayashi, Tohoku University

Real-Time Tsunami Inundation Forecasting

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Demo:Visualization of Simulation Results

PC Cluster Workshop in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Performance of Tunami Code on SX-ACE

LX 406Re-2

SX-9

SX-ACE

Single Core Performance
SystemPerf. /
Socket(Gflop/s)

No. ofCores

Perf. / Core
(Gflop/s)

Mem.
BW(GB/s)

Socket
B/F

Core
B/F

SX-ACE

256

4

64

256

1

4

SX-9

102.4

1

102.4

256

2.5

2.5

230.4

12

19.2

59.7

0.26

N/A

PC Cluster WorkshopLX406Re-2
in Sendai

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February 19, 2016

Hiroaki Kobayashi, Tohoku University

Scalability of Tunami Code
K computer (SPARC64VIII)
LX 406(Ivy Bridge)

Execution Time（Sec）

1,000,000

SX-ACE

100,000
10,0001,000

Target of 8-min

100
10
1

64

128

256

512

1K

2K

4K

Number of Cores (Processes)
PC Cluster Workshop in Sendai

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8K

13k
February 19, 2016

Hiroaki Kobayashi, Tohoku University

Total Execution Time of Tsunami Forecasting Workflow(Kochi-City Case）

Estimation

Inundation
Simulation

4
16
:4

Post-

13
:1
1

TsunamiPreProcessing

12
:5
8

7:
52

Coseismic Fault

7:
22

0:
00

Elapsed Time
Visualization

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PC Cluster Workshop in Sendai

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

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Summary
SX-ACE is involved as the social infrastructure for
Tsunami Inundation forecasting, like a wether forecasting
system, in addition to the research infrastructure forcomputational science and engineering in Japan

330 K
m

Regionally-distributed
multiplexing system

Current work

Osaka
University

380 KmTarget Area Extension:
Full coverage of JapanPC Cluster Workshop in Sendai

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TohokuUniversity

JAMSTEC

System Extension:
Complemental operationsof multiple systems

February 19, 2016

Hiroaki Kobayashi, Tohoku University

Summary
SX-ACE shows high sustained performance compared with SX-9, inparticular a significant improvement in short-vector processing and indirect
memory accesses
achieved the same single core performance in practical applications evenwith 60% of peak performanceNo1. computing-efficiency and power-efficiency in the HPCG Benchmark
ranking
Pave the way to a new social infrastructure for homeland safety in Japan
Well balanced HEC systems regarding memory performance is the key tosuccess for realizing high productivity in science and engineering simulations
Demands for Supercomputers for the rest of us, especially for 2020 andbeyond!
Brute force to Smart Force in HPC design
Quality, not Quantity!
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February 19, 2016

Hiroaki Kobayashi, Tohoku University

WSSP開催案内•

23rd Workshop on Sustained Simulation Performance•••

Held on March 16-17, 2016 at Tohoku University, Sendai Japan•

Confirmed invited Speakers

21st WSSP in Sendai

Organized by Tohoku University and HLRS, Stuttgart, JAMSTEC, NECInternational researchers and engineers get together to discuss andexchange ideas, experience and perspectives on current and future HPC
technologies• Michael Resch (HLRS)• Sabine Roller (University of Siegen)• Vladimir Voevodin (Moscow State University)• Toshimitsu Yokobori (Tohoku Univ)• Mitsuo Yokokawa (Kobe Univ)• Akiko Matsuo (Keio Univ)• Ken-ichi Itakura (JAMSTEC)• and more!
https://www.sc.cc.tohoku.ac.jp/wssp23/index.ja.html•
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February 19, 2016