Strategic Japanese-French Cooperative Program
Overview:
It is a reality that “contemporary society and science is faced with the challenge of dealing with increasing amounts of data.” Today, continuous developments occur in measurement technologies and computational resources in various fields of science and society. They have facilitated the collection as well as generation of petascale data. High-performance computational (HPC) resources need to be made remotely accessible through long-distance, high-performance networking for the efficient generation or processing of petascale data. The PetaFlow project enables the representation of these data as an interactive scientific visualization. Consequently, the emergence of adequate “information and communication technologies (ICTs)” has been beneficial for the generation and processing of petascale data with respect to high-performance computing-networking visualization and their mutual “awareness.” We organize our project into three functions; PetaFlow Application, PetaFlow Visualization, and PetaFlow Network and Middleware as follows. Figure 1: Overview of PetaFlow scheme. :109[019 wo]¬[019 /biouys lolleiedexijn ue spiswol 19ul91 tsoilqqs ວッ
PetaFlow Application:
Interactive super computing helps oral practitioners (dentists) to demonstrate a predictive orthodontic surgery or fabrication of dentures that enable patients to know the change of pronunciation before his operation, such as tooth movements. To do this, appropriate HPC packaging is necessary, so as to avoid strange movements due to the losing frames, because the package includes heavy simulations, CFD etc. Smooth and useful computational oral therapies required the profluent information flow by using the advancing internet Figure 2: Application example. technologies (see Fig. 2).
PetaFlow Visualization:
We utilize the FlowVR library to visualize computing results in world-wide large-scale visualization. The FlowVR library provides users with the necessary tools to develop and run high performance interactive applications on PC clusters and Grids. The main target applications include virtual reality and scientific visualization. FlowVR enforces a modular programming that leverages software engineering issues, while enabling high performance executions on distribued and parallel architectures. The FlowVR software suite has today three main components; FlowVR, FlowVR Render, and VTK FlowVR (see Fig. 3). We also utilize Particle-based volume rendering (PBVR). PBVR is one of the effective rendering techniques applicable to huge volumes of data. It is based on Sabella' s density emitter model, in which the scalar field is characterized as a cloud of opaque and self-emitting particles with single-level scattering. PBVR, being different from the ray-casting method, does not require sorting of elements, and thus, it facilitates handling of huge amounts of data, which could be of the magnitude of several gigabyte. We show an example in Fig. 4, where oral flow sound simulation results in higher-level complexities of 3D phenomena and huge volume datasets Figure 4: Particle-based volume rendering (PBVR) on tiled display wall (TDW). on Tiled Display Wall in Fig. 4.
Member of PetaFlow Project
Shinji Shimojo: Leader and Network (Osaka University, JAPAN) Ken-ichi Baba: Network (Osaka University, JAPAN) Julien Cisonni: Application (Osaka University, JAPAN) Kohji Koyamada: Visualization (Kyoto University, JAPAN) Kazunori Nozaki: Application (Osaka University, JAPAN) Hiroyuki Ohsaki: Network (Osaka University, JAPAN) Naohisa Sakamoto: Visualization (Kyoto University, JAPAN) Eisaku Sakane: Network (NII, JAPAN) Contact: PetaFlow Project E-mail: petaflow@ispl.jp
PetaFlow Network and Middleware:
The PetaFlow network testbed is a layer-2 virtual private network (VPN). It has been developed from the NAREGI-Grid5000 network testbed (2006–2009) and constructed through a collaboration among SINET, JGN-X, RENATER, GEANT, and MAN LAN. Figure 5 shows the topology of the PetaFlow network testbed. On the Japanese side, the network is composed of SINET and JGN-X networks, which are connected at Tokyo. The NII and Kyoto University connect with SINET, and Osaka University connects with JGN-X. The international network operated by SINET is used to connect the Japanese research foothold with Grid5000, and this network extends to MAN LAN (New York, USA) via GEANT (Europe). The Grid5000 backbone network is provided by RENATER. RESO (ENS Lyon, INRIA Grenoble) is focused on metrology equipment synchronization and Bulk Data Transfer software. The metrology equipment that will be connected at INRIA Grenoble and Osaka University, consist of two servers that each have Endace DAG7.5G2 network monitoring card installed over one 10 Gbps interconnection Grid5000 (France) and NAREGI (Japan) and one 1Gbps cluster at Osaka University. EndAce DAG card provides a synchronized sub-10 nanosecond timestamp capability over 2Gbps capture and inspection. For synchronization and accuracy of network traffic, we are using two Trimble Acutime Gold GPS smart antenna mounted at both locations. Acutime Gold GPS system is set with L1 frequency, C/A code (SPS), continuous tracking receiver, static overdetermined clock mode, Update Rate of 1Hz, Event Update maximum of 5 Hz/second and External Event Capture of 455 nanoseconds. For bandwidth reservation and co-ordination of bulk data transfer of large medical images, the RESO team is using FlowVR library at both servers to view these large data file images, while using jBDTS to set and handle bandwidth reservations and proper application scheduling to ensure the data images have enough bandwidth to be sent at an efficient and effective rate (see Fig. 6). This is accomplished by profiling the availability of the whole bandwidth available to the system so that fair sharing is incorporated amongst interactive applications set by jBDTS profile. Figure 6: Design of bulk data transfer scheduling (jBDTS). Annemie van Hirtum: Leader and Application (Gipsa-lab, CNRS, FRANCE) Ashley Chonka: Network (ENS Lyon, FRANCE) Yo Fujiso: Application (Gipsa-lab, CNRS, FRANCE) Paulo Goncalves: Network (ENS Lyon, INRIA, FRANCE) Matthieu Imbert: Network (ENS Lyon, INRIA, FRANCE) Pierre Neyron: Visualization (CNRS, Grenoble University, FRANCE) Anne-Cecile Orgerie: Network (ENS Lyon, FRANCE) Xavier Pelorson: Application (Gipsa-lab, CNRS, FRANCE) Bruno Raffin: Visualization (INRIA, Grenoble University, FRANCE)