Also called: MutualExclusionPattern, MutexPattern or MonitorPattern.
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In some cases, independent instances of data structures
can be assigned to particular CPUs or processes, so that
each CPU or process accesses or modifies only its own
data.  Algorithms that can tolerate stale data, such
as stochastic relaxation methods used in numerical
analysis,
may allow CPUs/processes to access each other's data.

DataOwnership eliminates the need for locking, although
DisableInterrupts may be needed if the locus of ownership
is the CPU in a kernel or real-time system.

Another way of looking at DataOwnership is to pretend that
each CPU/process acquired a lock that is otherwise unused
upon entry and released it upon exit.  Then, since the
data is accessed and modified only by its owning
CPU/process, any locks that might be used to protect it
would be subsumed into the imaginary per-process/CPU lock.
From this viewpoint, DataOwnership is a special case
of SubsumeLock.

An example data-ownership algorithms is described in:

``Efficient Kernel Memory Allocation on Shared-Memory Multiprocessors'',
Paul E. Mc''''''Kenney and Jack Slingwine, Winter'93 USENIX.

This paper describes a partially-partitioned algorithm
that uses data ownership.  The effect is that explicit
locks need be required only a few percent of the time,
greatly increasing performance despite the fact that the
algorithm is not fully partitionable.

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CategoryConcurrency