The 3rd International Workshop on Next Generation Climate
Models for Advanced High Performance Computing Facilities
(Registration 205)
Over the past decade, climate researchers and numerical weather forecasters have seen their computational needs increase at a warp speed. While many of these requirements have been satisfied by the emerging powerful workstation technology, the most sophisticated modeling experiments still require computing resources at the very high-end.
Scientists still maintain that the massively parallel designs have yet to become competitive with vector machines in many weather and climate applications with respect to efficiency and ease of programming. This is particularly remarkable when the movement of a large amount of information occurs across processors.
The USGCRP report titled “High-End Climate Science: Development of Modeling and
Related Computing Capabilities“ [1] issued in December 2000 indicated that shared-memory, vector computers give far more capability than distributed-memory computers, as well as usability, allowing climate-science algorithms to achieve high levels of performance on these computers. The use of distributed memory, commodity-based processor parallel computers increases the needed software investment arising from the challenging inter-processor communications requirements, which often hampers the enhanced performance of weather /climate applications.
The report also claims that the lack of impartial metrics for performance has resulted in an implicit pre-selection of applications that are likely to perform well on these metrics. One

of the apparent examples is the Top 500 list based on performance numbers of Linpack applications. Linpack performance numbers are frequently cited to justify claims of computer speed, but they do not always represent an actual application environment.
Vector computers having a higher single processor speed often achieve more than 30% of effective performance ratio to their theoretical performance peaks, while many US climate centers set a performance goal of around 10%, thus leading to a significant performance-usability gap.
In view of such a circumstance, NEC has been committed to pursue a long-term vision that views parallel vector computing as being most prospective for achieving high effective performance. In line with this policy, NEC started delivering the latest SX-5 Series supercomputers having the maximum performance of 5TFlops. The most prominent features of the SX Series [2] that makes parallel vector computing a viable HPC solution are shared memory architecture, higher peak and sustained performance of a single CPU, and performance scalability, as well as ease of use.
NEC will continue advancing parallel vector computing, as well as pursuing its possible combination with emerging scalar parallel computing to achieve better performance-to-price ratios. This innovative system will be capable of providing solutions to the most challenging computational problems, such as weather and climate modeling.
With the ever increasing demand for number crunching, the requirements for processing a large volume of data resulting from numerical simulations are also becoming enormous.
For a typical 1-year climate simulation based on a T213L50 global spectral model, more than 10Gbytes of data must be handled for visualization. Meanwhile, the amount of the data resulting from a more challenging high-resolution model that will run on the Earth
Simulator [3][4] is supposed to exceed 1-10TBytes. In weather and climate modeling, tools like Vis5D and GrADS have widely been used for conventional post-processing; however, they are no longer practical for coping with such a large volume of data. The lack of appropriate means of visualization becomes more serious with the increased popularity of data assimilation and ensemble forecasting. The former is a typical example of capability computing and the latter that of capacity computing, both of which are highly demanding in terms of efficient visualization to allow the users to grasp numerical results instantly.
To provide a better solution, NEC has been developing a server-driven visualization technique, as part of its commercial package called RVSLIB [5]. It was shown that the server-driven image-based visualization is highly tailored to a wide spectrum of large-scale simulations because of the reduced network load due to the transfer of compressed image data, as well as highly efficient graphical processing and image creation that exploit vector-parallel processing capabilities.

RVSLIB consists of two components, i.e., RVSLIB/Server running on high-performance computing servers such as the SX Series and RVSLIB/Client on workstations or PCs.
Two protocols are supported for the communication between them; one is the TCP/IP socket and the other is HTTP that enables the Web-based usage. The link to a
FORTRAN-written simulation program is conducted by calling specific subroutines of the visualization library provided as part of RVSLIB/Server.
In the interactive visualization mode, RVSLIB/Server performs mapping and rendering on the basis of the user specifications for graphical representations, such as contour lines, stream lines, and volume rendering. Then, RVSLIB/Server converts them into compressed image data, which are transferred to RVSLIB/Client via network.
RVSLIB/Client has a graphical user interface written in Java to specify the ways of visualization and manipulate visualized images. It also has a menu to conduct monitoring
(browsing of visualized image concurrently with the on-going simulation on the server side) and steering (interactive alteration of input parameters to a simulation code such as boundary conditions without suspending the simulation). Furthermore, the system supports script-driven visualization in the batch mode and the post-processing mode, thus facilitating adequate visualization depending on the size of a simulation and the computing environment.
In the presentation, the features and performance of RVSLIB will be shown. The server-driven image-based visualization was proven to be highly efficient, with the time increase due to the image creation on the server side less than 5% of the whole simulation time. A few applications of RVSLIB will also be presented for weather and climate simulations.
References
[1] http://www.usgcrp.gov/
[2] http://www.hpc.comp.nec.co.jp/sx-e/index.html
[3] K.
Tani, “Outline of the Earth Simulator Project in Japan”, the Computing in
Atmospheric Sciences Workshop 1998 (CAS98), http://www.gaia.jaeri.go.jp/public/e_publicconts.html
[4] K. Yoshida, “Latest Status of Research & Development of the Earth Simulator“, the 3rd
International Workshop on Next Generation Climate Models for Advanced High
Performance Computing Facilities (2001)
[5] http://www.sw.nec.co.jp/APSOFT/SX/rvslib_e