Summary Current assessments of supercomputing (high‐performance computing) primarily focus on system performance. Quantitative methods to measure the impact of supercomputing in a broad context have not been well developed. In this paper, the basic meaning of supercomputing development is analyzed. An evaluation index system for assessing the development status of supercomputing is constructed innovatively, and the SuperComputing Development Index (SCDI) is proposed to measure supercomputing development status. SCDI is a composite index combining various indicators into one benchmark measure that monitors and compares supercomputing development in the past years. This appears to be the first attempt to quantitatively measure the supercomputing ecosystem. As an example, the SCDI of the Chinese Academy of Sciences is obtained, which is based on the data collected from 130 research groups about and covers the period from 2006 to 2012. The results have demonstrated that the proposed evaluation index system is objectively reasonable. The constructed SCDI provides a scientific method to quantitatively evaluate the development status of supercomputing for institutions or organizations.

ABSTRACT Visual and interactive data exploration requires fast and reliable tools for embedding of an original data space in 3(2)‐dimensional Euclidean space. Multidimensional scaling (MDS) is a good candidate. However, owing to at least O(M2) memory and time complexity, MDS is computationally demanding for interactive visualization of data sets consisting of order of 104objects on computer systems, ranging from PC with multicore CPU processor, graphics processing unit (GPU) board to midrange MPI clusters. To explore interactively data sets of that size, we have developed novel efficient parallel algorithms for MDS mapping based on virtual particle dynamics. We demonstrate that the performance of our MDS algorithms implemented in compute unified device architecture environment on a PC equipped with a modern GPU board (Tesla M2090, GeForce GTX 480) is considerably faster than its MPI/OpenMP parallel implementation on the modern midrange professional cluster (10 nodes, each equipped with 2x Intel Xeon X5670 CPUs). We also show that the hybridized two‐level MPI/CUDA implementation, run on a cluster of GPU nodes, can additionally provide a linear speedup. Copyright 2013 John Wiley & Sons, Ltd.

Abstract We have designed a web‐based software system for real‐time interactive visualization of results taken from large‐scale simulations of 3‐D mantle convection and other large‐scale modeling efforts. This approach allows for intense visualization sessions for a couple of hours as opposed to storing massive amounts of data in a storage system. Our data sets consist of 3‐D data with over 10 million unknowns at each time‐step to be used for volume rendering. Large‐scale interactive visualization on a display wall holding 15 million pixels has already been accomplished. With the advent of the age of the mobile web, we have now extended this tactic to hand‐held devices, such as the OQO, Nokia N800, netbooks, iPHONE, and Android OS phones. We are developing web‐based software in Java to extend the ubiquitous use of this system across long distances. The software is aimed at creating an interactive and functional application capable of running on multiple browsers by taking advantage of two AJAX‐enabled web frameworks: Echo2 ( http://echo.nextapp.com/site/echo2 ) and Google Web Toolkit ( http://code.google.com/webtoolkit/ ). Modular building of the system allows for components to be swapped out so that other forms of visualization can be accommodated, such as molecular dynamics in mineral physics or 2‐D data sets from the regional convection models. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract The faster growth curves in the speed of graphics processing units (GPUs) relative to CPUs have spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence in broadcasting warnings. We employed an NVIDIA board on a MacPro to test the efficacy of the GPU on the set of shallow‐water equations, and compared the relative speeds between CPU and GPU for two types of spatial discretization based on second‐order finite differences and radial basis functions (RBFs). We found that the GPU produced a speedup by a factor of 8 in favor of the finite difference method and a factor of 7 for the RBF scheme. We also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speedup of 5.3 by the GPU. The time steps employed for the RBF method are larger than those used in finite differences, because of the fewer number of nodal points needed by RBF. Thus, RBF acting in concert with GPU would hold great promise for tsunami modeling because of the spectacular reduction in the computational time. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract We present the initial architecture and implementation of VLab, a Grid and Web‐Service‐based system for enabling distributed and collaborative computational chemistry and material science applications for the study of planetary materials. The requirements of VLab include job preparation and submission, job monitoring, data storage and analysis, and distributed collaboration. These components are divided into client entry (input file creation, visualization of data, task requests) and back‐end services (storage, analysis, computation). Clients and services communicate through NaradaBrokering, a publish/subscribe Grid middleware system that identifies specific hardware information with topics rather than IP addresses. We describe three aspects of VLab in this paper: (1) managing user interfaces and input data with JavaBeans and Java Server Faces; (2) integrating Java Server Faces with the Java CoG Kit; and (3) designing a middleware framework that supports collaboration. To prototype our collaboration and visualization infrastructure, we have developed a service that transforms a scalar data set into its wavelet representation. General adaptors are placed between the endpoints and NaradaBrokering, which serve to isolate the clients/services from the middleware. This permits client and service development independently of potential changes to the middleware. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Simulating natural phenomena at greater accuracy results in an explosive growth of data. Large‐scale simulations with particles currently involve ensembles consisting of between 106 and 109 particles, which cover 105–106 time steps. Thus, the data files produced in a single run can reach from tens of gigabytes to hundreds of terabytes. This data bank allows one to reconstruct the spatio‐temporal evolution of both the particle system as a whole and each particle separately. Realistically, for one to look at a large data set at full resolution at all times is not possible and, in fact, not necessary. We have developed an agglomerative clustering technique, based on the concept of a mutual nearest neighbor (MNN). This procedure can be easily adapted for efficient visualization of extremely large data sets from simulations with particles at various resolution levels. We present the parallel algorithm for MNN clustering and its timings on the IBM SP and SGI/Origin 3800 multiprocessor systems for up to 16 million fluid particles. The high efficiency obtained is mainly due to the similarity in the algorithmic structure of MNN clustering and particle methods. We show various examples drawn from MNN applications in visualization and analysis of the order of a few hundred gigabytes of data from discrete particle simulations, using dissipative particle dynamics and fluid particle models. Because data clustering is the first step in this concept extraction procedure, we may employ this clustering procedure to many other fields such as data mining, earthquake events and stellar populations in nebula clusters. Copyright © 2003 John Wiley & Sons, Ltd.

Data sets from large-scale simulations (up to 501/sup 3/ grid points) of mantle convection are analyzed with volume rendering of the temperature field and a new critical point analysis of the velocity field. As the Rayleigh number Ra is increased the thermal field develops increasingly thin plume-like structures along which heat is convected. These eventually break down and become turbulent. Visualization methods are used to distinguish between various models of heat conductivity and to develop an intuitive understanding of the structure of the flow.

Abstract Dissipative particle dynamics (DPD) and its generalization—the fluid particle model (FPM)—represent the ‘fluid particle’ approach for simulating fluid‐like behavior in the mesoscale. Unlike particles from the molecular dynamics (MD) method, the ‘fluid particle’ can be viewed as a ‘droplet’ consisting of liquid molecules. In the FPM, ‘fluid particles’ interact by both central and non‐central, short‐range forces with conservative, dissipative and Brownian character. In comparison to MD, the FPM method in three dimensions requires two to three times more memory load and a three times greater communication overhead. Computational load per step per particle is comparable to MD due to the shorter interaction range allowed between ‘fluid particles’ than between MD atoms. The classical linked‐cells technique and decomposing the computational box into strips allow for rapid modifications of the code and for implementing non‐cubic computational boxes. We show that the efficiency of the FPM code depends strongly on the number of particles simulated, the geometry of the box and the computer architecture. We give a few examples from long FPM simulations involving up to 8 million fluid particles and 32 processors. Results from FPM simulations in three dimensions of the phase separation in binary fluid and dispersion of the colloidal slab are presented. A scaling law for symmetric quench in phase separation has been properly reconstructed. We also show that the microstructure of dispersed fluid depends strongly on the contrast between the kinematic viscosities of this fluid phase and the bulk phase. This FPM code can be applied for simulating mesoscopic flow dynamics in capillary pipes or critical flow phenomena in narrow blood vessels. Copyright © 2002 John Wiley & Sons, Ltd.

Mixing of particles by chaotic flow fields was simulated on the Connection Machine. Each cell was assigned to the processor, and the coordinates of particles residing on the cell were kept in the local memory of the processor. This approach implies the exchange between the local memories, when a particle moves from one cell to another. Approximately 10/sup 5/ particles were injected into a time-dependent flow field obtained by solving the nonlinear system of partial differential equations describing turbulent thermal convection. The flow field was calculated on a CRAY, and data were transferred to a CM-200 through a high-speed HIPPI channel.>