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Hello world,

Two technical reports on scientific visualization techniques for
multicomputers have been made available for anonymous ftp from the
Department of Computer Science at the Australian National University.
They are available from bellatrix.anu.edu.au in the directory
pub/techreports/tr-cs-92-10 and pub/techreports/tr-cs-92-11.
The details of the papers are enclosed below.

Enjoy,

	B


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Author   : Paul Mackerras
Title    : A Fast Parallel Marching-Cubes Implementation on the Fujitsu AP1000
Date     : August 1992
Source   : Technical Report TR-CS-92-10, Department of Computer Science,
           Australian National University, Canberra, ACT, Australia.
Contact  : Paul Mackerras (paulus@cs.anu.edu.au)
FTP      : anonymous ftp to bellatrix.anu.edu.au,
           directory pub/techreports/tr-cs-92-10
Abstract :

Parallel computers hold the promise of enabling interactive visualization of
very large data sets.  Fulfilling this promise depends on the development
of parallel algorithms and implementations which can efficiently utilize
the power of a parallel computer.  Fortunately, many visualization algorithms
involve performing independent computations on a large collection of data
items, making them particularly suitable for parallelization.

This paper describes a high-performance implementation of the Marching
Cubes isosurface algorithm on the Fujitsu AP1000, based on a fast
serial Marching Cubes implementation.  On a 128-processor AP1000,
our implementation can generate an isosurface for a volume of reasonable
size (e.g. 2.6 million data points) in typically less than 0.5 seconds
(depending on the number of polygons generated).

The Fujitsu AP1000 is an experimental large-scale MIMD (multiple-instruction,
multiple data) parallel computer, composed of between 64 and 1024 processing
cells connected by three high bandwidth, low latency communications networks.
Each processing cell is a SPARC processor with 16MB of memory.  The cell
processors do not share memory.

Our experience indicates that the Marching Cubes algorithm parallelizes well;
in fact the speedup we obtain is actually greater than the number of
processors (presumably due to cache effects).  However, it is necessary to
perform any further processing of the generated surface (such as rendering,
or evaluation of connected volumes) in parallel if massive slowdowns are to
be avoided.

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Authors  : Brian Corrie, Paul Mackerras
Title    : Parallel Volume Rendering and Data Coherence on the Fujitsu AP1000
Date     : August 1992
Source   : Technical Report TR-CS-92-11, Department of Computer Science,
           Australian National University, Canberra, ACT, Australia.
Contact  : Brian Corrie (bcorrie@cs.anu.edu.au)
FTP      : anonymous ftp to bellatrix.anu.edu.au,
           directory pub/techreports/tr-cs-92-11
Abstract :

Many scientific and engineering disciplines, through physical
measurements or computational simulations, generate large scale
three-dimensional data sets. Both the physical size 
and the computational resources needed to render these data sets
present a challenge to current rendering architectures and techniques.

The Fujitsu AP1000 has the memory capacity and the processing speed
to render large three-dimensional data sets at interactive or
near-interactive speeds. A parallel version of a volume renderer has been
implemented using a ray-casting technique on this architecture.
The two key issues in implementing this technique on a distributed
memory, MIMD machine such as the AP1000 are the work and data distribution.
To perform the data distribution, a distributed virtual memory for volume
data is used. The importance of utilizing the data coherence that is inherent
in volume data is demonstrated through the analysis of several case studies.

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-- 
                Brian Corrie (bcorrie@cs.anu.edu.au)
Under the most rigorously controlled conditions of pressure, temperature,
volume, humidity and other variables, the organism will do as it damn well
pleases. Sounds like some of the code I have written......  8-)

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