On Sun, May 01, 2005 at 06:40:45PM -0600, Marcus G. Daniels wrote:
I see the two worlds (ahead of time compilation and just in time
compiation) coming together, as the crucial issue for future performance
of complex codes will have as much to do with effective dynamic code
partitioning over independent compute engines (finding parallelism) and
identification of runtime bottlenecks like out-of-cache memory access.
Expensive ahead-of-time analysis and mapping of user code to the CPU
architecture(s) in a system will still be important, but since dynamics
of programs can change over time (e.g. as in an agent based simulation),
it will also be useful to have system support to do things like
adaptively arrange the access pattern over a working set to fit in cache.
This is a fair comment, and I hope this does come to pass. The current
"two cultures" situation is a bit of a pain.
1) I learnt C++ in 1993/4, a decade after the language came out. At
that time, it was completely unknown in computational science. Even
C was considered a novelty at the time, barely registering in a
world of Fortran77.
In contrast, Java took off relatively quickly..
In computational science, Java has not taken off at all. I know of not
one computational scientist using Java for serious production
work. There are some pedagogical examples written in Java, and
occasionally these might provide research outcome too, but that is
often incidental. Usually, the models need to be rewritten in C++ or
Fortran to be serious research tools.
Matlab stands in similar role to Java as well. It is used for
prototyping, and for some problems, the prototype will ultimately
provide the searched for answer. However, many times, the Matlab code
needs to be rewritten, usually in Fortran90.
A decent optimising, autoparallelising Matlab compiler could be a real
boon. I think the problem is that Matlab is basically a product, not a
language standard.
Cheers
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