Throughput Evaluation for Improving
Predictability over the Internet
Cybermedia Center, Osaka University, Japan
Experimental settings

Applications in Grid environment are required to share distributed
large blocks of data among distributed multiple sites.
Network throughput for data transfer affects total processing time
as well as the task processing.

We used PlanetLab nodes, equipped with a virtualization
mechanism called V-Server (http://www.linux-vserver.org).
Various sizes for both probe and data transfer are used.
Correlation between both probes is evaluated by Spearman’ srank correlation coefficient, one of non-parametric metrics.

The predictednetwork throughput
would be a usefulparameteron scheduling tasks
to improve the total
processing
performance.

Original result
Smaller-size probes had better conditions than larger-sizeprobes.

Purpose of our research
Improving precision of the throughput prediction method called“Network Weather Service” [1].
Adapting the prediction method to virtualized hosting environment,
which shows anomalous behavior more
frequently thanphysical nodes.

Connection pair

Results without anomalous cases
We re-evaluate the results without the anomalies, and alarger-size probe is required for improving predictability.
If throughput is decreased by the anomalies, we should carefully
review measurement results.

A network throughput prediction has been a challenging issue
due to the dynamics of network traffic and no guarantee forbandwidth reservation.
Connection pair uses a small size of probe transfer to predict thethroughput of a large size of data transfer.
In previous work [1], the restricted sets of pairs on probe anddata size were examined on limited network environment.

Conclusion and future work

Traffic anomalies
The evaluation results were affected by oversize packet
spacings, which are caused by CPU scheduling latency.
The packet spacing which is larger than the TCP transmission
period involves packet transmissions, which results in severe
throughput[2].

Anomalies from virtualized hosting environment have great
impacts on the prediction results.
We re-evaluate the results without the anomalies, and found thatlarger-size probe is required for improving predictability.
Our future works are to measure throughput with various probe
size and to devise an anomaly estimation method.

References[1] M. Swany and R. Wolski, Multivariate Resource Performance
Forecasting in the Network Weather Service, in Proceedings ofIEEE/ACM Conference on High-Performance Computing and
Networking, pp 1-10, November 2002,.
[2] C. Lee, H. Abe, T. Hirotsu, and kyoji Umemura, “Analysis ofanomalies on a virtualized network testbed,” in Proceedings of the10th IEEE International Conference on Computer and Information
Technology, pp. 297–304, June 2010.

Chunghan Lee：Toyohashi University of Tech.Dept. of Electronic and Information Eng.
Hirotake Abe：Osaka University Cybermedia Center
Toshio Hirotsu ：Hosei University Faculty of Computer and Information Sciences
Kyoji Umemura ：Toyohashi University of Tech.Dept. of Computer Science and Eng.

Contact: Hirotake Abe
E-mail: habe@cmc.osaka-u.ac.jp

Throughput Evaluation for Improving Predictability over the Internet

Motivation

About Us: Cybermedia Center, Osaka University
Cybermedia Center, Osaka University, Japan

Information Infrastructure Center

HPC System

The aim of CMC is to achieve remarkable evolution in information andcomputing infrastructure by complementarily and systematically integrating
functions of computing technology-related organizations, as well as toprovide an advanced infrastructure for the accumulation and thedissemination of digital contents and for the efficiency of the high levelutilization.
CMC provides a powerful high performance computing environment foruniversity researchers across Japan. It plays the role of the nation's hub inteaching and diffusing advanced information technology. In addition, thecenter assumes the responsibility of facilitating campus IT infrastructure andpromoting its effective use. CMC also provides facilities for advanced
education to Osaka University students. It operates an Information Education
system and Computer Assisted Language Learning (CALL) system,
connected by the Osaka Daigaku Information Network System (ODINS). The
center offers a consistent information education curriculum, covering areasfrom basic usage of e-mail communication and the Internet to advanced
computing technologies, such as programming, as well as provides foreign
language and culture education support on various levels through acomprehensive application of multimedia techniques.
As a long-term goal, CMC supports educational and research activities bymaking comprehensive use of computer-related technology. Specifically,
CMC develops various electronic and multimedia functions to improve theefficiency of educational activities. For research activities, CMC provides
facilities to improve their scalability.
In next-generation computer applications, it is necessary to effectively"digitalize" ideas of researchers, meaning that CMC will need to deal withevery stage of the research process, including information input, retrieval
and collection, reports and discussion, analysis and modeling and finallyvisualization.

Toyonaka Campus

Minoh Campus

Suita Campus

Research Divisions
Informedia Education Division develops the advanced environment for
information processing education, offers educational programs on
information processing and information ethics, and also conducts
educational research, including faculty development programs for teaching
staffs in information processing.
Multimedia Language Education Division division develops anenvironment for language education using multimedia, provides assistancein the development of multimedia-based language education materials, suchas internationalized education using networks and foreign language
programs as common subjects in Osaka University.
Large-Scale Computational Science Division supports theoperation of CMC's supercomputer system, disseminates technology forvisual presentation of computational results, and facilitates the advanced
utilization technology of large-scale computing systems. This also offers
educational programs and studies on computing science and related
subjects.
Computer Assisted Science Division supports the operation ofgeneral-purpose computer systems, makes faculty developments to improve
efficient computer applications for setting up and solving scientific problems,and it also offers educational programs and does research on subjects
related to learning process for setting up and solving scientific problems.
Cybercommunity Division supports the planning and operation of
SCS-based distance learning, plans and operates distance training in thefield of advanced technology, and studies on the operation and promotion of
cybercommunity plans.
Advanced Network Environment Division supports the operation
and utilization of ODINS (Osaka Daigaku Information Network System), tointroduce new network technologies such as high-speed networks andmobile computing environments, to facilitate the utilization technologies oflarge-scale wide-area computer networks, and to carry out research on
network-related education.
Applied Information Systems Division develops and provides
education on utilization technology for large-scale information systems, todigitalize libraries, to support the management of various databases, toimplement education on information systems and multimedia systems, andto undertake education on information explorer.

Communication Network Analysis, NTT DOCOMO
Collaborative Research Division has been founded this year.

Supercomputing Contest for High School Students(co-hosted by Tokyo Institute of Technology)

About Us: Cybermedia Center, Osaka University

In April 1969, the Computation Center (CC) of Osaka University wasestablished as a laboratory that provides researchers of universities andother institutes computation and information processing services
indispensable to their academic researches and education. CC of Osaka
University was a part of a grand-scale endeavor by the Japanese
government to found seven such supercomputer centers across the nation
after accepting the Japan Science Council's suggestion to facilitate thecollaborative use of information technology amongst researchers. The othersupercomputer centers are located in Hokkaido University, Tohoku
University, Tokyo University, Nagoya University, Kyoto University andKyushu University.
Subsequently, CC of Osaka University and the supercomputer centers ofTokyo University and Kyushu University were reorganized into the
Information Infrastructure Center for the collaborative use of information
technology for researchers in Japan and to serve additional functions,
including conducting practical researches and reinforcing information
infrastructure, all of which are aimed at disseminating information andcomputing technology.
In April of 2000, Osaka University expanded and reorganized CC to form its
branch of the Information Infrastructure Center, which was named the
Cybermedia Center (CMC). In the expansion, the Education Center for
Information Processing and a part of the university library were merged into
CMC. While CC continues to provide computers for advanced scientifictechniques and media services, the Education Center for Information
Processing contributes by promoting education in information processing
and the university library by providing digital contents.

Management of overlay network performance:
End-to-end network measurement strategy andquick failure recovery
Cybermedia Center, Osaka University, Japan
Measurement strategies for overlay networks
Route of path BD:
3(B), 4(C), 5, 6(D)Route of path AD:
1(A), 2, 3(B), 4(C), 5, 6(D)Overlay Networks

A
1

B exists on path
AC
B
3

2

Development of technologies for multiplexing high-quality,
robust overlay networks on IP networks

C exists on pathAC
C
4

Measuring path BD:

Measuring path AD:
AB: measured by node A
BD: obtained from node B

C exists on pathBD

BC: measured by node B
CD: obtained from node C
Send results to node A

Overlay node

D
6

5

Send measurement
result of path CD
to node B
IP router-Each overlay node conduct traceroute commands to other overlay
nodes-Intermediate overlay nodes capture them and record src/dst
nodes-All overlay nodes understand path overlapping status-Measurement of longer path will be omitted and measurement
result is estimated from results of shorter paths-- Delay: D=d1+d2, bandwidth: B=min(b1, b2)-- Packet loss ratio: P=1-(1-p1)(1-p2)
Users, and services can construct their own
overlay networks adaptively and dynamically
We can decrease the number of required

We must provide overlay network environment foreach user/service, according to their demand andthe condition of underlay networks

measurement paths to 1/30 – 1/50,
regardless of the underlay network
topologies and the number of overlay
nodes.

Management of overlay networks
Network measurements: Simple full-mesh measurement has the O(N2) overhead. So, we need simple, lightweight, and scalable
measurement method.
Overlay Node

Router

Proactive recovery method for overlay networks
Original topology

RLSet

All nodes are isolated
in at least one RL

RL1

RL2

Failure recovery: Since overlay networks share underlay network
equipments, a single network failure would bring multiple,
simultaneous failures in overlay networks. So, we need failure
recovery method from simultaneous failures in overlay networks.
Overlay networks

RL3

Normal node

Normal link

Lsolated node

Lsolated link

We can improve reachabilityof the overlay network from-We construct multiple
topologies for failure
recovery from the original
topology-Each topology has
isolated nodes. When anode failure occurs, we
utilize the topology whichisolates the failed nodes-Multiple simultaneous
node failures can berecovered, when suchnodes isolated in one
topology

Ideal
Proposed method(1)Proposed method(2)No Recovery

69% to 99%, when 5% links
in the underlay network failsimultaneously.
Underlay networks

Contact: Go Hasegawa
E-mail: hasegawa@cmc.osaka-u.ac.jp

Management of overlay network performance: End-to-end network measurement strategy and quick failure recovery

Joint works of Osaka University and NEC Corporation, Japan,
supported in part by the National Institute of Information andCommunications Technology (NICT) of JapanA Handheld User Interface for GPU-Accelerated
Large-scale Volume Visualization
Cybermedia Center, Osaka University, Japan

Introduction
The ever-increasing sizes of volumetric data produced from avariety of scientific studies post a formidable challenge for thesubsequent real-time large-scale volume visualization and analysis.
While steady advances in graphics hardware enable faster
rendering, real-time rendering of large-scale volume data is still atough problem. Unlike polygonal data, volume data must take careof transparency of internal values within the volume that thus
requires time-consuming voxel sorting for conventional volume
rendering algorithms.
Such large-scale volume visualization must also take into accountinteractive transfer function control for data mining and scientificdiscovery. A high-resolution large screen is often used for
displaying large-scale volume data, in which conventional input
devices such as a mouse and a keyboard is not a practicalsolution.
In this poster, presented is an ongoing work on an interactive
large-scale volume visualization system using a handheld device
and a GPU-accelerated particle-based volume rendering algorithm(see Figure 1).

Particle-based Volume Rendering

Figure 2: System configuration
Table 1: Specification

Server
CPU：AMD Opteron 2350 2.0GHz
RAM：32GB
GPU:nVIDIA Quadro FX 4600
OS:Ubuntu 8.10 (64bit)

ClientMulti-touch Display: 9.7 in
Resolution: 768 x 1024

A set of graphical user interfaces (GUI) are under development (seeFigure 3) for the handheld device. A user will be able to control thevisualization parameters interactively. Example interactions
implemented are:
- Color curve editing. Mapping between a scalar value of voxel
data and its rendered color can be changed simply by deforming
the color curves.
- Particle filtering. Particles with specified values can be hidden or
highlighted.
Any change in visualization parameters will be immediatelyreflected to the rendering result.

To treat large-scale volume data, a particle-based volume
rendering algorithm is employed in our visualization system, which
has been proposed by Kyoto University. In the algorithm, a set oftiny opaque particles are generated from a given 3D scalar field.
The final image is then generated by projecting these particles onto
an image plane. A semi-transparency effect is realized by sub-pixel
processing and averaging and thus the necessity of voxel sorting iseliminated. This algorithm is further accelerated by GPU
programming.

Interactive Transfer Function Control
For assisting data mining and scientific discovery, a handheld
device is provided to control the transfer function in detail in
real-time. Our visualization system consists of a server machine
which renders volume data and a client handheld device (i.e. iPad)that are connected to each other via WiFi or Bluetooth (see Figure2). The hardware specification in our system is summarized inTable 1.
The GPU-based rendering algorithm has been carefully modified toaccept user input to be able to control a variety of visualization
parameters with negligible perform degradation.

Figure 3: User Interface

Conclusion
An interactive visualization system for large-scale volume data has
been proposed. Large-scale volume visualization has been realized
thanks to the GPU-accelerated particle-based volume rendering.
Interactive transfer function control has been realized by a
dedicated graphical user interface on a handheld device.
Kentaro Oita : Graduate School of Information Science and Technology, Osaka University, Japan
Kiyoshi Kiyokawa : Cybermedia Center, Osaka University, Japan
Haruo Takemura : Cybermedia Center, Osaka University, Japan

Contact: Haruo Takemura and Kiyoshi Kiyokawa
E-mail: kiyo@ime.cmc.osaka-u.ac.jp

A Handheld User Interface for GPU-Accelerated Large-scale Volume Visualization

Figure 1: Prototype System

A Virtual Cluster over Multiple Physical
Clusters Using P2P Overlay Network
Cybermedia Center, Osaka University, Japan
Recently, virtual cluster technology
has attracted attention in scientificresearch field. A virtual cluster
system allows scientists to build
their private computational
environment which is customized
as they like on one physicalcluster.
However a virtual cluster overmultiple physical clusters is still
difficult to realize. This is because
each physical cluster is commonly
isolated by Firewall or NAT and so
VMs deployed on different clusters
cannot connect directly with eachother. A virtual cluster over multiple
physical clusters needs tovirtualize network to hidedisconnect of network.
We develop such a cluster overmulti physical cluster leveragecluster install toolkit software with
P2P overlay network software.

Figure 1 : Concept of our virtual culster

Our Virtual Cluster
Utilizes Two key technologies
N2N : A P2P software which establishes an encrypted layer-two virtualprivate network based on a P2P protocol implemented by ntop.org.
Rocks : A cluster installing toolkit including virtual cluster implemented by UCSD.

The process of developing a virtual cluster
Our virtual cluster is set up on multiple Rocks physical clusters. N2N virtual private network are established between VMs which are deployed byRocks. The setup processes are performed by a admin of one Rocks physical cluster. Rocks clusters which allow admins to set up a virtual cluster are
known in advance. The process of developing a virtual cluster is shown in the following.
1.Register selected vm-containers to each Rocks DB.
2.Establish a P2P virtual private network among the selected vm-containers and make configuration for VMs.
3.Start a Rocks virtual Frontend on a physical frontend.
4.Start VMs on selected vm-containers.
P2P overlay network②establish a P2P overlay network
among the selected vm-containers

Admin④Start VMs on selectedvm-containers

vm-container

tap

tap

tap

eth0

eth0

eth0

Compute-0-1Compute-0-0

eth0③Start a Rocks virtual Frontend
on a physical frontend

eth0

Site A①Register selected vm-containers
to each Rocks DB
Rocks DBon Frontend

VirtualFrontend
eth1
eth0

eth1

Rocks
Frontend

eth1

eth0

Physical network

tap

tap

eth0

eth0

Compute-0-1Compute
-0-0

eth0

eth0

Site B④Start VMs on selectedvm-containers

Figure 2 : Detaildesign of our proposed virtual cluster

Contact: Kei Kokubo, Adrian Ho, Kohei Ichikawa, Date Susumu and Jason Haga
E-mail: kokubo.kei@ais.cmc.osaka-u.ac.jp

A Virtual Cluster over Multiple Physical Clusters Using P2P Overlay Network

Introduction

PETFLOWa project towards an ultra parallel synergy internet system
in scientific applications
Cybermedia Center, Osaka University, JapanPETAFLOW CLOUD

It is no falsehood to state that‘current society and scienceattempt to deal with increasing
amounts of data’ . Today,
peta-scale data are commonly
gathered as well as generated
thanks to the continuous
development of measurement
technologies and computational
Fig.1 (Top) :r e sour c es i n d i v er se f iel ds o f
The oral flow-sound simulation inpronunciation of “/s/”science and society. Efficient
processing or generation of peta-scale data requires highperformance computational (HPC) resources which should bemade remotely accessible through long-distance high performance
networking and might be represented thanks to thepetaflow-project, interactive scientific visualization. Consequently,
generation or processing of peta-scale data benefits from theemergence of adequate ‘Information and communication
technologies (ICT)’ with respect to high performance‘computing-networking-visualization’ and their mutual‘awareness’ . It is aimed to develop and validate such ICT
solutions using a transnational high speed research network
between Japan and France connecting ‘GRID5000’ (France) to the‘Naregi’ (Japan) testbed. Data-transfer protocols are aimed to bevalidated on data obtained for a real scientific problem involving
peta-scale data. Due to the medical relevance as well as basic
scientific interest, peta-scale data are obtained from HPC
Computational Fluid Dynamics (CFD) simulations on a vector
supercomputer (NEC SX9 Japan) aiming to predict the airflow
through the upper airways. In addition, CFD simulation outcome isused as an input for aero-acoustic computations (CAA) for
prediction of noise production. Besides the international transfer ofthe generated peta-scale data, scientific visualization of peta-scaledata is aimed on a single PC as well as on a tiled display wall for
3D interactive reconstruction of the flow and noise data. In
summary, the petaflow project aims to contribute to thestate-of-theart of HPC, networking, scientific visualization and theirmutual interactions for peta-scale data, while at the same time it isaimed to contribute to basic research in the fields of CFD and CAA
applied to flow through the upper airways.

There are many kinds of applications which perform numericalsimulations, for example, Virtual Physiological Human (VPH), Real
time Space Weather Simulater, Numerical Whether Simulation,
Nuclear Fusion Simulations, Aerodynamic Research for the Next
Generation Supersonic Transport and Numerical Simulation to
Tsunami Disaster Prevention. It is necessary to increase the total
amount of computational resources to perform those applications.
It has not been sufficient, however, to do them at all.
This problem arise from such a structure that each researchers orgroups own their own computational resources. This structure ofscientific society is notefficient in terms of several
aspects. We hereby propose“PetaFlow cloud”. PetaFlow
cloud enables researchers orgroups to obtain the solutions
for their concrete objects thatcan be required by several
numerical simulation codes.
PetaFlow cloud has threemain components, first“ P e t a - s c a l e
Networking-Storage”, second
Fig.6(c) : Vision of PetaFlow:“Peta-scale Computing” and・Peta-scale Networking – Storage
・Peta-scale Computing“Peta-scale Visualization”.

PETAFLOW NETWORK・Peta-scale Visualization

PETASCALE VISUALIZATION
Oral flow-sound simulation results in higher-level complexities of
3D phenomena and huge volume datasets. Visualizing huge data
often requires use of high-resolution display such that importantfine structures are not missed. It is because the number of
elements forming the huge volume data exceeds resolutions of thenormal LCD displays.
The particle-based volume rendering (PBVR) is one of the effective
rendering techniques applicable to huge volume data. It is basedon the Sabella’ s density emitter model, in which the scalar field ischaracterized as a cloud of opaque and self-emitting particles withthe single-level scattering. PBVR does not require sorting ofelements, being different from the ray-casting method, and soenables us to treat over giga-byte huge data easily.

PetaFlow network testbed is a Layer-2 Virtual Private Network. It
has been developed from NAREGI-Grid5000 network testbed(2006--2009) and constructed with the collaboration of SINET,
JGN2plus, RENATER, GEANT, and MAN LAN. Figure XX shows the
topology of the PetaFlow network testbed. Japanese-side network
is composed of SINET and JGN2plus networks connected at
Tokyo. NII and Kyoto University connect with SINET, and Osaka
University connects with JGN2plus. In order to connect Japanese
research foothold with Grid5000, the international network
operated by SINET is used and extends to MAN LAN. On the other
hand, Grid5000 backbone network is provided by RENATER. The
network from Grid5000 extends to MAN LAN via GEANT.
Paulo Goncalves : INRIA, ENS Lyon, Universit´e de Lyon
Xavier Grandchamp : Gipsa-lab, UMR CNRS 5216, Grenoble Universities
Xavier Pelorson : Gipsa-lab, UMR CNRS 5216, Grenoble Universities
Bruno Raffin : INRIA Grenoble, France
Annemie Van Hirtum : Gipsa-lab, UMR CNRS 5216, Grenoble Universities
Pascale Vicat-Blanc : INRIA, ENS Lyon, Universit´e de Lyon
Ken-ichi Baba : Vizlab, Kyoto University
Julien Cisonni : The Center for Advanced Medical Engineering and Informatics
Yasuo Ebara : Cybermedia Center, Osaka University
Kazunori Nozaki : The Center for Advanced Medical Engineering and Informatics
Hiroyuki Ohsaki : Graduate School of Information Science and Technology
Shigeo Wada : The Center for Advanced Medical Engineering and Informatics
Takuma Kawamura : Vizlab, Kyoto University
Kohji Koyamada : Vizlab, Kyoto University
Eisaku Sakane : National Information and Communication Technology, Japan
Naohisa Sakamoto : Vizlab, Kyoto University
Shinji Shimojo : National Institute of Informatics, Japan

Contact: Petaflow
E-mail: petaflow@ispj.jp

PETFLOW a project towards an ultra parallel synergy internet system in scientific applications

BACKGROUNDA Handheld User Interface for GPU-Accelerated
Large-scale Volume Visualization
Cybermedia Center, Osaka University, JapanThe Cybermedia Center at Osaka University was founded by merging theformer Computation Center, the former Education Center for Information
Processing and part of the university library in April 2000. Such reorganization
was conducted in order to comprehensively promote educational study in viewof rapid developments in the field of information technology.
The goal of our center is twofold: 1) to continue providing stable infrastructureservices as well as technical knowledge about supercomputers, information
education systems and networks used around the world, and 2) to pursue
research that enables the most advanced infrastructure services.

Advantages of Historical Vector Computing Maintained
At our center, we introduced 20-nodes of SX-8R in January, 2007. It replaced
SX-5/128M8 which had stunned the HPC community with its peak
performance of more than 1TFLOPS for the first time ever as a vector-type
supercomputer and the 8th rank on the TOP500 list in 2001. While we havewitnessed a phenomenal increase in the computational performance on the
TOP500 list after this SX-5, we now give first priority to the users’ benefits
gained through the continuous improvement in performance rather than mere
performance index. In line with such a policy, we decided to upgrade thissystem in a phased manner with an additional 10-node SX-9 system for July,2008.
The performance in running real application programs has been improved by
the sophisticated compiler technology. While the recent microprocessors canrealize the improved performance as long as their caches are effective, thesupremacy of vector machines is still remarkable. Especially, multi-threaded
performance with automatic parallelization of the SX-9 is outstanding.

1.0E+06

STREAM2 DAXPY

1.0E+07

256KB

1.0E+06MB/s

1.0E+07

1.0E+05

1.0E+04

Xeon‐5160 (Woodcrest/3.0GHz)
SX‐9 w/o ADB
SX‐9 w/ADB

1.0E+05

1.0E+0432KB
1.0E+03

4MB

Size of Each Array (8‐byte Elements)

In these ten years, the technology of past high-end
microprocessors has been inherited to budget-price products, andthe low power consumption technology has been spread
simultaneously. Although the large-scale cluster system based on
PC is getting popular, it has also left the issues surrounding
operation and maintenance. We built a cluster system that canclosely be co-operated with the vector machine in order to gain
benefits from both architectures. It became a good example which
demonstrates a synergistic effect by different architectures.

1.0E+07

1.0E+05

1.0E+04

1.0E+03

1.0E+02

1.0E+01

1.0E+08

1.0E+07

1.0E+06

1.0E+05

1.0E+03
1.0E+06

MB/s

STREAM Triad

SX‐9 1‐threead
SX‐9 2‐threead
SX‐9 4‐threead
SX‐9 8‐threead
SX‐9 16‐threead
SX‐8R 1‐threead
SX‐8R 2‐threead
SX‐8R 4‐threead
SX‐8R 8‐threead

Size of Each Array (8‐byte Elements)

In terms of the STREAM2 benchmark, the performance of vector
computers is rather excellent even for very short loop lengths partly
due to the automatic loop collapse by the compiler. For the SX-9,
256KB of ADB (Assignable Data Buffer) acts like a secondary
cache to increase the bandwidth for short loop lengths. The
efficiency of the cache of a microprocessor can now be grasped. In
fact, it became clear that the vector machine is superior toconventional scalar processors also for the case with short loop
lengths that was thought to be tailored to microprocessors: vector
operations can be effective even for the range of very short array
lengths where the L1 cache of the microprocessor is effective.

The HPCC benchmark is gaining popularity as a comprehensive measure on theperformance of HPC systems. While it is not realistic to make a straightforward
comparison among different systems based on this benchmark, unlike the Linpack
benchmark, the HPCC benchmark can give a certain insight into the performance
characteristics of HPC systems through careful consideration.
Let us use the Kiviat Diagram for the comparative performance among different
systems based on the results submitted to the HPCC benchmark site as of
October, 2008. At this time, we measured the performance by setting thenumber of MPI processes to be identical to the number of CPUs. For themultiple-node configuration, the number of MPI processes was set to the numberof nodes with non-OpenMP based parallelization within a node by utilizing theautomatic parallelizable BLAS library for the SX and multi-threaded GotoBLAS for
Xeon-5160.
These measurements show that the SX series has a excellent single-node
performance with balanced scores for many performance measures. In contrast,
microprocessor-based systems show relatively poor performance numbers
depending on performance index.
System ‐ Processor ‐ Speed ‐Processors Count – OpenMP Threads – MPI Processes

NEC SX‐9 (opt)
NEC SX‐8R
NEC Express 120Rg‐1Intel Xeon 5160

3.2GHz2.2GHz3.0GHzG‐HPL

G‐PTRANS

G‐RandomAccessG‐FFTE

EP‐STREAM
Sys

EP‐STREAM
Triad

Tﬂop/s

GB/s

Gup/s

Gﬂop/sGB/sGB/sRandomRing RandomRing
EP‐DGEMM
Bandwidth
LatencyGﬂop/s

GB/s

usec

16

1

16

1.38215334.561

0.402194241.7130

2,723.90

170.2488.73

62.8236

3.5318132

12

1.94299

128.3030.100641

55.3206

5,630.32

2,815.16

1,305.26

27.18935.4568781

80.2577835.1760.050399

3.6489373.924

46.74

34.21

11.41813.63782161

20.36489

29.9110.183769

0.7758721.54360.77278.197.76246.567542

1

40.04034

0.743

0.008019

1.2338

5.50

1.37

10.80

0.48980.9790232

1

160.61089

3.297

0.008736

6.4515

51.81

3.24

43.10

0.16859.67466

Here let us take a close look at each of the measures. While there is an increasingly narrower gap between the SX-8R and the Xeon systemwith respect to HPL (High Performance Linpack) and DGEMM (matrix-matrix multiply) due to the improved cache blocking and
SIMD-extension/multi-threading techniques getting pervasive for commodity processors, the SX-8R still maintains competitiveness overscalar systems on STREAM and RandomRing Bandwidth. The SX-9 shows an excellent performance even for FFT with vector optimization,
but the performance in HPL is limited due to the limitation resulting from 32-bit indexing in coding of HPL. Here we should be aware of thecontroversy over HPL as a neutral performance indicator, since the recent commodity processor-based systems can easily achieve a relatively
high HPL performance even with low-speed interconnects. On the other hand, there are also indexes subject to change depending on theactual number of nodes used for the measurement. It can be suggested that for certain application codes, the hybrid system of a vector
machine and a PC cluster might be appropriate.
The results of the HPCC benchmark shows that per-processor performance of SX-9 is the world fastest and the per-thread performance for asingle-node by the automatic parallelization is also the world fastest. It is proven that ease of use combined with high performance can greatly
improve the efficiency of user's research.

Of the total 20 nodes of the SX-8R system introduced at last time, 8 nodes areinterconnected by bidirectional 16 GB/s through the IXS (Internode X-bar Switch)equipment. According to the Intel MPI Benchmark 3.0, the latency time measured for
Ping-Pong is 3.79 microseconds, and the maximum bidirectional throughput for
Send-Recv is 29.6 GB/s. On the Linpack HPC benchmark, 2.056TFLOPS (N= 352,256)
with its peak performance ratio of 91.3% was achieved.

1E+03

1E+02

SX‐9 inter‐node (64GB/s)
SX‐9 inter‐node
SX‐8R inter‐node (16GB/s)
SX‐8R inter‐node
Xeon‐5160 inter‐node (1.25GB/s)
Xeon‐5160 inter‐node

1E+01

268,435,456

16,777,216

1,048,576#bytes

65,536

4,096

256

16

1E+00
1

All the PCs comprising our cluster system with more than 600 nodes have alreadybeen introduced and are now inter-connected with the Chelsio’ s T310-CX(10GBase-CX4, ToE-enabled). The latency time with Ping-Pong is 10.49 microseconds
and the maximum bidirectional throughput for Send-Recv is 1.39GB/s. It seems thatthe throughput is limited due to the overhead arising from the internal processing of
the sock driver of Intel’ s MPI at present. Priority is given to the flexibility of cluster
construction and utility on GridMPI, although it is also possible to transpose a stack to
RDMA and to improve the throughput.

1E+04

MB/s

Of the total 10 nodes of the SX-9 system introduced at this time, 8 nodes areinterconnected by bidirectional 64 GB/s through the IXS crossbar equipment. The
latency time measured for Ping-Pong is 4.38 microseconds, and the maximum
bidirectional throughput for Send-Recv is 98.0 GB/s.

1E+05

Contact: Manabu Higashida, Ph. D.
E-mail: manabu@cmc.osaka-u.ac.jp

Advantages of Vector Supercomputer Maintained in Japanese Joint-Use for Academia

HPC Challenge Benchmark: SX vs. PC Cluster

Development of Pedestrian Crowd Simulation on
GPU and Application to Evacuation from
Large-scale Underground Shopping Mall
Cybermedia Center, Osaka University, Japan
CASE STUDY AND PERFORMANCE

Pedestrian crowd simulations have been developed and applied toverification of the safety in buildings or urban environments. Agentbased simulation are particularly well suited to pedestrian crowd
behaviors from a set of simple individual rules. However,computing and visualizing crowd behaviors in real-time is acomputationally intensive task because this intensity mostly comesfrom the O (n2) complexity of the algorithm needed for theinteractions of all agents. By using special data structures such as
grids, relevant previous works reduces this complexity.
On the other hand, researchers demonstrated significantly
increased speed-up after adapting existing CPU-oriented
algorithms to parallel processing. Furthermore, modern GPUs have
many cores; they offer large performance benefits for parallel
processing at low cost. In 2007, NVIDIA released CUDA
parallel-processing architecture for next-generation GPUs, letting
programmers use C. It is becoming easier to develop agent based
simulation on GPU.
In this study, we present a GPU based implementation of
agent-based pedestrian crowd simulation and an application to theevacuation from a large-scale underground shopping mall.

As a case study of the pedestrian crowd simulation, we configured
an evacuation situation in a large-scale underground shopping mallin Osaka, Japan in Figure 3.
As a performance testing, we evaluated two versions of thesimulation. While the first was executed on the GPU, the second
was executed on the CPU. Both versions used the same algorithmand data structure. The average time to compute the social force
of all agents without rendering process is shown in Figure 4. Thesesimulations were executed for each implementation type varying
only the number of agents, ranging from 1,000 to 10,000. As aresult, the GPU version presents better scalability than the CPU
version. When the number of agent is 10,000, the GPU version isapproximately seven times faster than the CPU version. Moreover,
it is possible to see that the performance of the GPU
implementation sustained interactive frame rates with rendering ofcomplex models of agents and buildings.

IMPLEMENTATION
Agent-Based Modeling
We implemented the social force model in order to model
pedestrian crowd behavior. The social force model solves the
motion equation of an agent, which is represented by a moving
disc (Figure 1). An agent that has mass and a constant radius issubjected to a force exerted by other agents and obstacles andexit position. These steps perform all operations on a GPU using
CUDA (Figure 2). Parallel processing and the GPU’ s many coresproduce huge computational power, enough to update massive
pedestrian crowds.

Fig3. Application to Large-scale Underground Shopping Mall

Fig.1 Social Force Model

These tests were performed on an Intel Core i7 CPU 930@2.80GHz with a NVIDIA GeForce GTX 460 GPU

Fig. 4 Performance Result of CPU and GPU

Conclusion
We showed an implementation capable of running up to 10,000
abents at an interactive frame rate using current graphics
hardware and CUDA technology
Fig2. Operations on GPU

Contact: Kensuke Yasufuku, Ph.D.
E-mail: yasufuku@cmc.osaka-u.ac.jp

Development of Pedestrian Crowd Simulation on GPU and Application to Evacuation from Large-scale Underground Shopping MallINTRODUCTION