Section: Scientific Foundations

Data consistency

A shared virtual memory system provides a global address space for a system where each processor has only physical access to its local memory. Implementing such a concept relies on the use of complex cache coherence protocols to enforce data consistency. To allow the correct execution of a parallel program, it is required that a read access performed by one processor returns the value of the last write operation previously performed by any other processor. Within a distributed or parallel a system, the notion of the last memory access is sometimes only partially defined, since there is no global clock to provide a total order of the memory operation.

It has always been a challenge to design a shared virtual memory system for parallel or distributed computers with distributed physical memories, capable of providing comparable performance with other communication models such as message-passing. Sequential Consistency   [77] is an example of a memory model for which all memory operations are consistent with a total order. Sequential Consistency requires that a parallel system having a global address space appears to be a multiprogramming uniprocessor system to any program running on it. Such a strict definition impacts on the performance of shared virtual memory systems due to the large number of messages that are required (page access, invalidation, control, etc.). Moreover Sequential Consistency is not necessarily required to correctly run parallel programs, in which memory operations to the global address space are guarded by synchronization primitives.

Several other memory models have thus been proposed to relax the requirements imposed by sequential consistency. Among them, Release Consistency   [72] has been thoroughly studied since it is well adapted to programming parallel scientific applications. The principle behind Release Consistency is that memory accesses are (should be?) guarded by synchronization operations (locks, barriers, etc.), so that the shared memory system only needs to ensure consistency at synchronization points. Release Consistency requires the use of two new operations: acquire and release . The aim of these two operations is to specify when to propagate the modifications made to the shared memory systems. Several implementations of Release Consistency have been proposed  [75] : an eager one, for which modifications are propagated at the time of a release operation; and a lazy one, for which modifications are propagated at the time of an acquire operation. These alternative implementations differ in the number of messages that needs to be sent/received, and in the complexity of their implementation  [76] .

Implementations of Release Consistency rely on the use of a logical clock such as a vector clock  [80] . One of the drawback of such a logical clock is its lack of scalability when the number of processors increases, since the vector carries one entry per processor. In the context of computing systems that are both parallel and distributed, such as a grid infrastructure, the use of a vector clock is impossible in practice. It is thus necessary to find new approaches based on logical clocks that do not depend on the number of processors accessing the shared memory system. Moreover, these infrastructures are natively hierarchical , so that the consistency model should better take advantage of it.