Path: utzoo!attcan!utgpu!jarvis.csri.toronto.edu!mailrus!uflorida!gatech!hubcap!"Orran
From: okrieg@eecg.toronto.edu (Orran Y. Krieger)
Newsgroups: comp.parallel
Subject: Looking for a message passing package to run on the Sun
Message-ID: <6473@hubcap.clemson.edu>
Date: 15 Sep 89 21:10:28 GMT
Sender: fpst@hubcap.clemson.edu
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Approved: parallel@hubcap.clemson.edu


   I'd like to tie together our local bevvy of suns into a multicomputer
   for solving scientific problems. I need simple facilties to
   fork off processes and to handle message passing.
   Are there any software packages out there in netland to do this?


	   Scott B. Baden

	   Lawrence Berkeley Laboratory
	   Berkeley, California
	   baden@csam.lbl.gov
	   ...!ucbvax!csam.lbl.gov!baden

Since you specified message passing this is probably not of
interest to you. However, I developed for my MASc an
implementation of distributed shared data on a network of Sun's. 
A brief abstract from a talk I am giving is included below (in
latex format). The code is free and (hopefully) mainly debugged. 

\centerline{\Large{\bf Distributed Shared Data:}}
\centerline{\Large{\bf An Alternative Approach to Interprocess Communication}}
\centerline{\Large{\bf for Distributed Systems}}
\vspace*{.1 in}
\centerline{Orran Krieger and Michael Stumm}
\centerline{University of Toronto}
\vspace*{.1 in}

In this talk we will describe algorithms that implement the {\em
shared data model} of interprocess communication for distributed
systems.

Traditionally, communication among processes in a distributed
system is based on the {\em data passing} model, which includes
message passing mechanisms.  Although the data passing model is a
logical extension of the underlying communication hardware, the
{\em shared data} model has a number of advantages, which have
made it the focus of recent study\footnote{For example, work by
Li, Bisiani and Forin, Cheriton, and Gelernter.}. The shared data
model provides a simpler abstraction to the application
programmer. Processes use shared data in the same way they use
normal local memory; that is, shared data is accessed through
{\tt read} and {\tt write} operations.  Since the access protocol
is consistent with the way sequential applications access data,
there is a more natural transition from sequential to distributed
applications.  Another advantage of the shared data model is that
it hides the remote communication mechanism from the processes,
again substantially simplifying the programming of distributed
applications.  We have found the size of programs\footnote{as
measured by lines of source code} using the shared data model
typically to be half the size of equivalent programs that use the
data passing model.  With a shared data facility, complex
structures can be passed by reference. In contrast, with the data
passing model complex data structures that are passed between
processes must be explicitly packed and unpacked.

In this talk we describe how the shared data model can be
realized and then focus our attention on a new algorithm we have
developed called TORiS. The most original feature of TORiS is
that it makes {\em optimistic} assumptions in order to reduce the
amount of communication required between processes.

All of the algorithms we describe, including TORiS, have been
implemented on a network of SUN workstations. We compare
performance results of the TORiS implementation with other
implementations of the shared data model.