Hands-on Seminar of Remote
Visualiza5on System PBVR
Takuma Kawamura
Japan Atomic Energy Agency, Chiba, Japan
Siggraph Asia 2015 Symposium On Visualiza5on
In High Performance Compu5ng, Kobe
Conven5on Center, Japan1

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QPBLAS-GPU

Remote Visualization Software：PBVR

LicenseThe source code is licensed underPurpose and Overview of the Program
Development
The development of Peta-scale supercomputers such as the Kcomputer has dramatically extended the capabilities of numericalsimulations. However, simulation data produced in such Petascale simulations is becoming too large compared with thetheGNU Lesser General Public
License (LPGL)

Download
Version：1.05

network bandwidth, the storage and processing speed on PCs,

Release date：June 9, 2015

and therefore, the conventional post-processing approaches, in

Binarywhich the simulation data is transferred and processed using
commodity visualization softwares, are facing difficulties.

Source code
Manual (Japanese version)

To resolve this issue, the Center for Computational Science & eSystems in the Japan Atomic Energy Agency (JAEA) developed aManual (English version)client-sever type remote visualization software PBVR based on theParticle Based Volume Rendering techniques and the KVS library,

Sample data

which are developed at Koyamada laboratory in the Kyoto

Old Version

Click “PBVR” tab
Get hands-on package ofversion 1.06a

University.
This software enables the following remote visualization
procedures with minimum data transfer.
1: A filter program on a server machine (supercomputers,
clusters, workstations) decompose large-scale
structured/unstructured volume data into sub-volume data forparallel visualization.
2: A server program on the server machine compresses thevolume data to small particle data using massively parallel
processing.

SIGGRAPHASIA2015 tutorial
Version：1.06a
Release date：November 2, 2015
Package (Windows)
Package (Mac)Manual (Japanese version)
Manual (English version)
Tutorial (Coming Soon)

3: The small particle data is transferred by the socket
communication between the server machine and a client PC.

Developer

4: A client program on the client PC renders the particle data with

Computer Science Research andinteractive frame rates.

Development Office, Center for
Computational Science & e-Systems,2• Overview of PBVR
– Background– Client-server visualiza5on based on PBVR

• Brief demonstra5on of PBVR with client servermode– Remote visualiza5on of large data on K computer• Hands-on with PBVR
– Install prebuilt binary of PBVR
– Run PBVR system with standalone mode
– Become familiar with viewer window– Image quality control– Designing transfer func5ons– Saving results3Visualiza5on of Large-Scale Data
Various Visualization for Analysis
Hydrogencharge density

Pump structure

Tornado ﬂow

v Iso-surface, Boundary surface, Streamline, etc......
v Volume renderingØ Efficient technique on various fields

Volume Renderingv Interac5ve opera5on of visualiza5onparameter (Threshold value, color,viewing posi5on, , etc......)Ø Discovering important feature
Breast CT scan

Pump structure

Transfer func5on
Oral air ﬂow

Horizontal：physical value
Ver5cal：color and opacity

Increasing Simulation Scalev Large-scale data (tera, peta, and larger)
v Low interactivity inhibits analysis

Interac5ve visualiza5on for remotelarge-scale data (few second scale)4

Background: Remote Visualiza5on of Large-scale Data
Traditional Visualization

Parallel Volume Rendering3,4,5v Transferring original data to PC
v Impossible to transfer large datav Suitable for extreme-scalevisualization on parallel environment

Data Compression1v Strong scaling is limited by image
composition phase (alpha blending)v Data redundancy of time variance or
frequency characteristics

v Low interactivity of viewing positionv Difficult to select compression
method suitable to data type

Client-Server Type Visualization2v Transferring visualization primitive(polygon)v High interactivity of viewing positionv Increasing rendering primitive in
extreme-scale visualization

Visibility Order for Alpha Blendingv Semi-transparent primitives (polygon,
sampling point, etc...) are compositedfrom the order of viewing position

Sor5ng miss

Sor5ng correct

1. H. Ohtani, K. Hagita, A. M. Ito, et al. “Time-Order Kine5c Irreversible Compression Scheme for ......”, IEEE VIS 2013
2. Ensight, Visit, ParaView, etc…
3. M.Howison, E.W.Bethel, et al. “Hybrid parallelism for volume rendering on large ......”, IEEE Trans. VCG 2012
4. T.Peterka, H.Yu, R.Ross, K.L.Ma, “Parallel Volume Rendering on the IBM Blue Gene/P”, EGPGV 2008
5
5. K. Ono, J. Nonaka, “Design of coopera5ve visualiza5on environment with intensive data ......”, Ultra Vis 2008

Background: Remote Visualiza5on of Large-scale Data
Polygon-based Volume Rendering

Particle-based Volume Renderingv Large polygon data (~=volume data)v Small par5cle datav Image generation for sub-regionv Par5cle genera5on for sub-regionv Polygon sorting requires heavy comm. v Par5cle composi5on without sor5ng→ Limited strong scaling→ Suitable for parallel processing
v Recalculation for interactive process v Interactive processing on PC
Volume data

Subdivision/
Polygon gen.

Subdivision/
Par5cle gen.

Sor5ng/Rendering

Composite par5cle
Supercomputer
User PC

Image produc5onComposite sub-image

Par5cle projec5on and rendering

6Mechanism of PBVR1,2
v Par5cle density calculated from
opacityv Par5cle genera5on by Monte-Carlosampling

Ls = 1105 par5cles

Ls = 2
4*105 par5cles

Ls= 10107 par5cles…………

Ø Metropolis /Rejec5on /Uniform
samplingv Pixel division (sub-pixel)Ø Par5cle diameter equals to sub-pixel
lengthØ Sub-pixel expression by frame buﬀer

v Fast Z-buﬀer algorithm for depth
comparison

A pixel on screen
Par5cle projec5on
Divided into 32 sub-pixels onto sub-pixels(sub-pixel level=3)
1.
2.

…………
Sub-pixel level (Ls) / Image quality / Frame buﬀer size

Depth comparisonand forefront
par5cles stored

Averaging stored
par5cle value

N. Sakamoto, J. Nonaka, K. Koyamada, S. Tanaka, “Par5cle-based Volume Rendering”, APVIS 2007
T. Kawamura, N. Sakamoto, K. Koyamada,”Level-of-detail Rendering of a Large-scale Irregular Volume .... ”, JCST 2010

Op5cal Model of Volume Rendering
Density Emimer Model1

Alpha Blendingv Particle distribution in volume datav Emission and absorption of lightv Calculation of brightness

v Discretization of brightness
equationv Opacity defined by density
tk−1

Volume data

α k := 1− ∫ t exp (−π r 2 ρ (t)) dt
kScreenB(t)+dB(t)View point

B(t)

ρ(t)

B

c(t)
dt

t : distance from view point r : particle radius
B(t) : brightness ρ(t) : density c(t) : brightness per particlet0

t0

B(t0 ) = ∫ c(t) × π r ρ (t) × exp(− ∫ π r 2 ρ (λ )d λ )dttn

2

v Shielding rate of lightemimed from a par5cle toview pointØ Light reaching rate
follows Poisson
distribu5ontBrightness equa5on
1. P. Williams, N. Max, “A volume density op5cal model”, VVS 1992
8

Remote Visualiza5on System using PBVR1Client-server distributed processing model
1. Par5cle data size smaller than volume data size
2. Sort-less algorithm and Monte-Carlo sampling
3. Rendering process using Graphic card
4. Image quality controlled by number of par5cles

1. Client-Server
2. Highly parallelized
3. Interac5ve viewing posi5on
4. Flexible LoD control

ClientImage
10242 pixels

ServerPar5cle projec5on usinggraphics cards

Graphic card
1.

Nodes

Par5cle data

ClientmemoryInternet~1MB/sec
Par5cle data

10-100MB/stepVolume data Par5cle data

Par5cle genera5on using
parallel processing
Volume data
1-10GB/stepStorageT. Kawamura, Y. Idomura, et al. “Remote Visualiza5on for Large-scale Simula5on using PBVR”, SC 2012

Parallel Par5cle Genera5on（GPGPU）
GPGPU server (node spec.)v Small par5cle data sizev No sor5ng for par5cle
Thread parallel for cell
SerialIntegral of ρ
Num. of particles

IntegralIntegral

Sampling

Dynamic

Cell 2Thread2

Cell 1Thread1

Cell 1

GPU:448 cores(Tesla M2075)

Peak perform.

11.72GFLOPS

1030 GFLOPS

Bandwidth

32 Gbyte/sec

150 Gbyte/sec

Simply parallelCPU

Dynamic
allocation

CPU:1 core
(Xeon E5607)

Sampling

Serial[msec]

66070.4

Sampling

Sampling

Par5cle transfer from GPU to CPU mem.

Dynamic
Sampling

64.3

100.0

[msec]

Dynamic

Integral
Cell 4Cell 3Cell 2

Dynamic

Integral

25000.3

Limited by dynamic memory allocation
on GPU
10.0

Integral

Simp. para.

Only 3x speed up using 448 cores

Dynamic
Sampling

4.2 GB test data

Cost of 27MB memory allocation
0.4

1.0
0.1

0.2

0.0

0.0

Xeon
Sta0c

Xeon
Dynamic

Tesla
Sta0c

Tesla
Dynamic

10Parallel Par5cle Genera5on（GPGPU）1
Static allocation of particle array

Particle index arrayv Memory addresses given by cumulative sums of particles at each cellv Separation of density integral and sampling by memory allocation for particles
v Overlapping particle generation and transfer from GPU to CPU

IntegralIntegralDynamic
Sampling

Dynamic
Sampling

Cell 2IntegralIntegral

Cell 4IntegralParticle index array
Static allocation
Sampling
Sampling

Cell 2

Sampling

Integral

Sampling

Cell 4

Sampling

Cell 1

Dynamic

Dynamic

Processing 5me of 4.2 GB
test data [msec]

Thread2

Cell 3IntegralThread1

Cell 1

IntegralOp5mized parallel

Cell 3Thread2

Cell 2Thread1

Cell 4Cell 3Cell 1

Original parallel

Sampling

Serial66070.4

Orig. para.

Opt. para.

25000.3

3726.4

Improved parallel accelerated 22
5mes faster than serial
processing and 8 5mes fasterthan simply parallel processing.
Dramatic improvement inparticle generation speed on
GPU.

Transfer
CPU

1.

Takuma Kawamura, Yasuhiro Idomura, et al. “Remote Visualiza5on System based on PBVR”, JSST 201411

Parallel Par5cle Genera5on on Supercomputerv Accelerate par5cle genera5on via
massively parallel environmentv Paralleliza5on with hybrid MPIー
OpenMP programming model

Particle distribution
Dense

Non-uniform par5cle distribu5on
and load imbalancev FilterØ Octree decomposi5on of volumedata
v Parallel ﬁle I/O of sub-volume datav Dynamic task alloca5onØ Assign sub-volume data to eachnode using Master-Slave MPI
model
Ø Random number is generated bymassively parallel Mersenne Twister
libraly, Kmath Random1

SparseMasterSlave task
allocation

Slave
Read
sub-volume
Particle
generation

Synchroniza5on
Detect end of
Send particles
Slave
to Client
Write particles
1.

Kmath Random [Imamura, AICS, RIKEN]

12

Numerical Experiment
Server machine (node spec.)K

SPARC64VIIIfx (8 cores), 128GFLOPS

6D Torus interconnect
BX900 2 x Xeon x5570 (4 cores), 94GFLOPS
Inﬁniband QDR(Fat tree)
The electrosta5c poten5al of turbulent
ﬂuctua5ons in a fusion plasma

User interface of PBVR

Data size and image resoluYon
Num. Cells

286x106

Data size

1.1GB x 100 step

ResoluYon

1024x1024

Num. parYcles~107
ITER size full-f simula5on [Idomura,POP14]
13

Strong Scaling on Supercomputer1
K

sec/step
35
30
25
20
15
10
5
0

Par0cle genera0on
Read sub-volume
Task alloca0on

BX900

sec/step
50

Par1cle genera1on
Read sub-volume
Task alloca1on

40
30
20
10

32

64

130
256
Cores

512

1024

0
32

64

128 256
Cores

512

1024

Computa5onal cost of par5cle genera5on for 100 stepv Strong scaling up to 1024 coresØ The parallel eﬃciency is more than 90％
Ø 1.36 5mes faster than BX900v K computer achieved 0.84 [sec/step] at 1024 cores
1.

T. Kawamura, Y. Idomura, H. Miyamura, et al. ” Remote Visualiza5on System based on PBVR”, VDA 201514

Par5cle Rendering using GPU
Enabling of interac5ve rendering using GPU accelera5on• Frame buﬀer u5lized pixel synthesis• Small par5cle data stored on GPU memory
Par5cle data
10-100MB
Par5cle datatransfer
CPU
Change view pointGPU

Par5cleprojec5on

Pixel synthesis
Image data
10242

Vertex buﬀer object(VBO)

Frame buﬀer

GPU Memory 500-1000MB
15

Benchmark Test against the EnSight
Client-Server Environment
Client (PC)CPU
Xeon E5 2690 x 2
GPU
Quadro K5000
Server1 (CPU)
BX900 ( 6nodes/48 cores )Server2 (GPGPU)
Tesla M2075 (6 nodes)
Network
3.4 [MB/sec ]EnSight

sec/step

Ensight PBVR
(CPU)
(CPU)

PBVR
(GPU)

Filter [sec/step]

-

0.7

0.7

Image gen. [sec/step]

-

51.4

3.8

Transfer [sec/step]

-

75.7

75.7

Total [sec/step]

3873

127.8

79.5

Frame rates [fps]

2.7

60.0

60.0

Client memory [MB]
900
257
PBVR
v ~30x and ~50x speed up in the total performance on CPU and on GPGPUv Par5cle genera5on on GPGPU is ~13.5x faster than CPUv ~22x speed up in the frame rates

257

16

Client-Server Communica5on
User PC（Lab.,Pocket WiFi, etc…）
Linux
Mac
Windows

Interactive data exploration
Abstract
analysis

Detailanalysis

Find Region of Interest (ROI)
Control of Level of Detail (LOD)

ssh tunnel(Internet)
Supercomputer（K computer, BX900, etc….）

Coarse / Fast（~1MB par5cles）

Fine / Slow(~100MB par5cles)

ssh port
forwarding
Calculation node, Analysis nodePar5cle data
10-100MB

Login node

Network bandwidth
1-100MB/sec
Small number of par5cles

Large number of par5cles

17General Visualiza5on Interface1
Important visualiza5on opera5ons
v Shape extrac5on using iso-surface
v Mul5-variables assigned to diﬀerent proper5esv Composi5on of diﬀerent visualiza5on methods
Above opera5ons are feasible by PBVR and integrated transfer func5on

Temperature

Electrosta5c poten5al＋

Water pressure response

Iso-surfaces extracted
by spiky opacity func5on

1.

Charge density

Two variables assigned
to color and opacity

Z-coordinate

Composi5on of two
variables and methods

T. Kawamura , Y. Idomura , et al. “Mul5variate Volume Rendering Using Transfer......”, SIGGRAPH ASIA 201518

Summary
Clinet-Server visualiza5on based on PBVR
v Sort-less volume rendering using par5cle datav Scalable Monte-Carlo par5cle genera5onv Transfer small par5cle data and rendering using graphic card
Interac5ve remote visualiza5on of extreme scale data
Present status and future issues
v Open-source souware PBVR was released in Mar. 2015
hmp://ccse.jaea.go.jp/ja/download/souware_eng.html

v Supported data formats: KVS, AVSFLD/UCD, VTK, PLOT3D, STLv Server on massively parallel systems (K, BX900…), GPGPU clusters, and PCs
v Client on PCs (Linux/Mac/Windows)
v Advanced transfer func5on design for mul5variate datav High dimensional data (5D par5cle distribu5on, 4D spa5o-temporal data)v Mul5-scale data

19

Brief Demo. of PBVR with Client-Server
Connec5on via Internet• Client: user PC, Server: K computer– Pre-post node of K computer for interac5ve processing• Local and remote Port forwarding using ssh tunnel
Visualiza5on• Nuclear fusion plasma simula5on result composed of 4variables– electrosta5c poten5al– electron density ﬂuctua5on– electron temperature ﬂuctua5on– magne5c poten5al• Mul5variate transfer func5on– Visualize rela5on of 4 variables
20

Hands-on with PBVR
Today’s Goals• Install prebuilt binary of PBVR
• Run PBVR system with standalone mode
• Become familiar with viewer window• Image quality control• Designing transfer func5ons• Saving results• Example of batch mode21

Installa5on of Prebuilt Load Modules• Decompress the hands-on package– Mac: PBVR_mac.tgz– Windows: PBVR_Windows.zip• Choose the suitable package to your plaxorm
and copy it to any working directory– Don’t change the directory structure in the package
PBVR_macor_windows

PBVR_Filter

PBVR_Server

PBVR_Viewer

run.sh or .bat

exit.sh or .bat

gt5d

ﬁlter_out

22

Package Contents
Prebuilt load modules• v1.06a contains only serial prebuilt binaries for Mac and Windows– Compiled without OpenMP– Including sta5c link for various libraries

Start script•

Test data (gt5d)

Launching prebuilt load
modules•

Nuclear fusion plasma simula5on
result composed of 2 variables– Electrosta5c poten5al– Magne5c poten5al

Stop script•

Termina5ng PBVR clientand server

Filterd data (ﬁlter_out)•

Store decomposed data by ﬁlter

PBVR_macor_windows
PBVR_Filter

PBVR_Server

PBVR_Viewer

Prebuilt load modules

run.sh or .bat

exit.sh or .bat

gt5d

ﬁlter_out

Start script

Stop script

Test data

Filtered data

Package Contents
Start script./PBVR_Filter ./gt5d/param.txt./PBVR_Server &./PBVR_Viewer -sl 4 -plimit 1000 -pa demo.tf-vin ./filter_out/case.pfi -shading P,0.6,0.6,0.6,30

PBVR_Filter•

Octree decomposi5onparameters– I/O directories– Number of layers– Output ﬁle format•

Supported data formats– KVS, AVSFLD/UCD

PBVR_Server•

Launching in the
background

PBVR_Client•

Rendering parameters– vin: ﬁltered data
– pa: transfer func5on– sl: subpixel level– plimit: par5cle limit– shading: shading modeland parameters24Package Contents
Test data (gt5d)

param.txt

gt5d.ﬂd An AVS ﬁeld ﬁlein_dir=./gt5dfield_file=gt5d.fld
out_dir=./filter_out

co3d.dat A coordinate data ﬁleout_prefix=casestart_step=0

The variable 1

pd3d.dat (Electrosta5c poten5al)
gt5d

The variable2

psid.dat (Magne5c poten5al)
param.txt Input parameters

end_step=4

Omimed default parameters• SPLIT format– output_type=0

for PBVR Filter• A single sub-volume

demo.x A transfer func5on ﬁle– n_layer=0

for demonstra5on(Generated by client )25

Package Contents
Filtered data (ﬁlter_out)
case.pﬁ

A decomposi5on parameters

case_Y_Z_connect.dat An element conﬁgura5on ﬁleﬁlter_out

case_Y_Z_coord.dat

A node coordinate ﬁlecase_X_Y_Z.kvsml

A header ﬁlecase_X_Y_Z_value.dat A variable ﬁle• X : number of steps（in 5 digits）• Y : index for sub-volume (in 7 digits)• Z : total number of sub-volumes（in 7 digits）
26

Launching PBVR Client-Server System• Windows– Execute the batch ﬁle “run.bat” by double-clicking• Mac– Execute the script “run.sh” in the terminal

27

Launching PBVR Client-Server System

Press “Apply” bumon

28

Viewer Opera5ons• Opera5ons– Rota5on: move the mouse while pressing the leu-bumon– Transla5on: move the mouse while pressing the rightbumon– Zoom: scroll up/down the mouse wheel, or move themouse up/down while pressing the Ctrl key– Reset posi5on: home bumon (fn + leu arrow on Mac)
– Changing rota5on target: l key → light, o key → object• Display– Yme step: current 5me step of the data
– fps: the frame rate [frame/sec]– Orienta5on box: x-y-z axes
Try viewer opera5ons

29

Image Quality Control
Change visualiza5on parameters• SUB PIXEL LEVEL– Division number of pixel– Propor5onal to image
quality and number ofpar5cles• PARTICLE LIMIT– Maximum number ofpar5cles– The number mul5pliedby 106.

• RESOLUTION– Viewer’s resolu5on.

Press “Set Parameter” bumonauer se{ng30Designing Transfer Func5ons• Transfer FuncYon Editor provides a mul5dimensional transfer func5on design– Two independent variable quan55es to color and opacity– Deﬁni5on of each variablewith an arbitrary func5on• X-Y-Z coordinates• variables q1, q2, q3…

– Mul5dimensional transfer
func5on• Synthesis of one-dimensionaltransfer func5ons t1-t5

31

Designing Transfer Func5ons
Volume Rendering for a Single Variable• variable q1 by se{ng the transfer func5on t1
Select “t1” from Transfer FuncYon Name

Input “t1” to Transfer FuncYon SYNTHESIZER

Press “Apply” bumonauer the se{ngsDrag the mouse up/down on Histogram32

Designing Transfer Func5ons
Mul5variate Volume Rendering• Colors assigned to the variable q1• Opaci5es assigned to the variable q2.
Select “t2” from Transfer FuncYon Name

Input “t2” to Transfer FuncYon SYNTHESIZER

Press “Apply” bumonauer the se{ngs33Designing Transfer Func5ons
Extrac5ng a Slice• Colors assigned to the variable q1• Cylindrical surface (X^2+Z^2=const.) as the variable of opacity
Select “t3” from Transfer FuncYon Name

Input “t3” to Transfer FuncYon SYNTHESIZER

Press “Apply” bumonauer the se{ngs34Designing Transfer Func5ons
Synthesis of Transfer Func5ons• Transfer func5on t4 makes the region Y > 0transparent.
Select “t4” from Transfer FuncYon Name

35

Designing Transfer Func5ons
Synthesis of Transfer Func5ons• Flexible composi5on through arithme5c opera5ons– synthesizing t1, t2, t3 and t4 as (t1 + t2) * t4 + t3
Input “(t1 + t2) * t4 + t3” to Transfer FuncYon SYNTHESIZER

36

Designing Transfer Func5ons
Synthesis of Transfer Func5ons• Colors of t2 and t3 set to (R, G, B) = (0, 0, 0)• Color of t4 set to (R, G, B) = (1, 1, 1)
Select “t2/t3/t4” fromTransfer FuncYon Name

Press “Color Map Editor (freeform curve)”
bumon

37

Designing Transfer Func5ons
Color Map Editor (freeform curve)
Pick up satura5on and brightness

Pick up color hue(R, G, B) color normalized to [0,1](R, G, B) = (0, 0, 0) means black(R, G, B) = (1, 1, 1) means white

Brightness

Satura5on

Overpaint the color by dragging themouse cursor while pressing the leu
mouse bumon

The blending ra5o of the originalcolor and the overpain5ng color isdetermined by the mouse cursor’s
ver5cal posi5on
38

Designing Transfer Func5ons
Color Map Editor (freeform curve)
Press “Save” auer edit color

Press “Cancel” to close• Reset– Resets the panel.

• Undo– Undoes the last mouseac5on.

• Redo– Redoes the last mouseac5on undone.

• Save– Saves the transfer
func5on.

• Cancel– Closes the panel.
39Designing Transfer Func5ons
Synthesis of Transfer Func5ons• Final colors obey the colormap deﬁned for t1• In case of red: ( Rt1 + Rt2 )*Rt4 + Rt3 where Rt2 ,Rt3=0,
Rt4=1.
Press “Apply” bumon

40

Designing Transfer Func5ons
Synthesis of Transfer Func5ons• (t1 + t2)– Composi5on of t1’s volume rendering and t2’s torus surface• (t1 + t2) * t4
– Extrac5ng the lower half region (Y < 0) of t1 and t2• (t1 + t2) * t4 + t3
– Composi5on of cylindrical surface given by t3

Try above
synthesis
41

Saving Results
Image Output• capture– Controls on/oﬀ of image
produc5on– 1 shot per 5me step• image ﬁle– A preﬁx of image dataﬁles

• s key– Capturing a snapshot

42

Saving Results
Transfer Func5on File
• File Path
– Speciﬁes a ﬁle path forsaving and loading atransfer func5on ﬁle.

• Export File
– Saves a transfer func5ondeﬁned with this panel to aﬁle in the same format as
the parameter ﬁle speciﬁedwith the command line
op5on ‘-pa’.

• Import File
– Loads a transfer func5on
stored in a ﬁle to this panel
43

Example of Batch Mode
Server./PBVR_Server -B -vin ./filter_out/case.pfi -pa demo.tf -sl 4-plimit 100 -pout ./output/case

Client./PBVR_Viewer -pin1 ./output/case -sl 4v Useful also for high speed rendering of 5me series datawith PBVR Client in stand-alone modev Sub-pixel should be speciﬁed also when launching PBVRClient in stand-alone mode
44

Acknowledgement• The benchmark tests were performed on
BX900 @ JAEA and K-computer @ Riken.
• This work is supported by the MEXT, grant for
HPCI Strategic Program Field No.4.
• The client-sever type remote visualiza5on
souware PBVR developed based on the KVS
library, which are developed at Koyamada
laboratory in the Kyoto University.

45