Microsoft has flip-flopped on its scalability message, now touting "scale-up" rather than "scale-out" improvements. "If you look at what Microsoft said in 1997 at its Scalability Day event it was to scale out. They said, 'Don't use SMP (Symmetric Multiprocessors) -- applications will hit a knee in the curve, and you have to pay more to scale up.'

"Now they're back to SMP. Get out your disco ball and leisure suit--Microsoft has taken you back to 1985," Shimp quipped.

Oracle, with its roots in Unix databases, has offered 64-bit support for years.

Parellel Processing (PP)	vs	Symmetric Multiprocessing (SMP)
PP splits the problem domain into pieces and assigns each to its own processor with dedicated memory		SMP splits the range among available processors with shared memory.
PP runs a copy of the solver on each processor and requires message passing at internal boundaries using multiprocessing languages like PVM and MPI.		SMP uses the multithreading paradigm and requires "index independence," i.e., no dependencies between different ranges
PP is more scalable (two to thousands of processors)		SMP less scalable (two to tens of processors) because it is limited by shared memory bandwidth.
PP requires significantly more code modifications.		SMP requires fewer code modifications.