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Munin implements a variety of consistency protocols, which are applied at the granularity of individual data items. The protocols are parameterized according to the following options:
whether to use a write-update or write-invalidate protocol;
whether several replicas of a modifiable data item may exist simultaneously;
whether or not to delay updates or invalidations (for example, under release consistency);
whether the item has a fixed owner, to which all updates must be sent;
whether the same data item may be modified concurrently by several writers;
whether the data item is shared by a fixed set of processes;
whether the data item may be modified.
Read-only: No updates may be made after initialization and the item may be freely copied.
Migratory: Processes typically take turns in making several accesses to the item, at least one of which is an update. For example, the item might be accessed within a critical section. Munin always gives both read and write access together to such an object, even when a process takes a read fault. This saves subsequent write-fault processing.
Write-shared: Several processes update the same data item (for example, an array) concurrently, but this annotation is a declaration from the programmer that the processes do not update the same parts of it. This means that Munin can avoid false sharing but must propagate only those words in the data item that are actually updated at each process. To do this, Munin makes a copy of a page (inside a write-fault handler) just before it is updated locally. Only the differences between the two versions are sent in an update.
Producer-consumer: The data object is shared by a fixed set of processes, only one of which updates it. As we explained when discussing thrashing above, a writeupdate protocol is most suitable here. Moreover, updates may be delayed under the model of release consistency, assuming that the processes use locks to synchronize their accesses.
Reduction: The data item is always modified by being locked, read, updated and unlocked. An example of this is a global minimum in a parallel computation, which must be fetched and modified atomically if it is greater than the local minimum. These items are stored at a fixed owner. Updates are sent to the owner, which propagates them.
Result: Several processes update different words within the data item; a single process reads the whole item. For example, different ‘worker’ processes might fill in different elements of an array, which is then processed by a ‘master’ process. The point here is that the updates need only be propagated to the master and not to the workers (as would occur under the ‘write-shared’ annotation just described).
Conventional: The data item is managed under an invalidation protocol similar to that described in the previous section. No process may therefore read a stale version of the data item.
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