🚀 go-pugleaf

In article <1993Jan27.024351.17902@news.arc.nasa.gov>, lamaster@pioneer.arc.nasa.gov (Hugh LaMaster) writes:

| Actually, though, there are plenty of problems that require the largest 
| available power, and, in the commercial world, there are an increasing
| number of really big applications (e.g. VISA-type credit cards) which,
| generally, need an increasing number of transactions-per-second.  Anyone
| tried to buy anything with a VISA card the last week before Christmas?
| The wait can be rather long.   Credit card companies, Airline Reservations, 
| banks with ATM applications, you-name-it.  All have increasing needs for
| systems with expanding transaction rates.  A system which can do 2000 
| transactions/sec may indeed be worth a *lot* more than 200 systems which 
| can do 10 trans./sec.

  So who's doing the research on this? Obviously there will always be
problems which need a lot of peak TPS, and I doubt that making bigger
machines will prove practical for long.

  Just as scientific calculation has gone parallel, I would expect some
distributed TPS to be coming, but I sure see a lot of bigger and bigger
iron rather than cheap solutions you can put on a network of smaller
systems. I count vector processors as parallel for this discussion,
because they bypass the limitation that N operations take N times
longer than one operation.

  Recently we have seen PVM running here, a program which allows a user
to spread a problem over many machines while flooding the network and
bringing all the machines in question to a grinding halt. Has someone
done a similar program for TPS which I've missed? 

  The solutions I've seen are not seamless, don't scale well, and fail
to provide database consistency able to meet the standards I see met
with single machine systems.


-- 
bill davidsen, GE Corp. R&D Center; Box 8; Schenectady NY 12345
	A terible poker player can have a great hand,
	A obnoxious fool can have a good idea.
	Both are winners, regardless of who has them.