Efficient algorithms for verifying memory consistency

Manovit, Chaiyasit; Hangal, Sudheendra

doi:10.1145/1073970.1074011

Cited by 15 publications

(19 citation statements)

References 22 publications

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“…The VSCconflict problem, which is the VSC-read problem augmented with the total write order per-location, however, is in P (and similarly, so is the conflict serializability problem in databases). Similar results have been shown for the corresponding problems for the TSO memory model; VTSO and VTSO-read are NP-complete, while the VTSO-conflict problem can be solved in linear time [8] [11]. The Verifying Memory Coherence (VMC) problem, which is like VSC, but involves only one memory location, is also NP-complete; however, VMCread is in P [4].…”

Section: Related Worksupporting

confidence: 67%

“…We also developed a simple heuristic to determine if an order satisfying all the axioms of the memory model exist [11]. However, in the many cases (up to 80% of 16 processor program runs) that the heuristic failed to determine this, we had to optimistically assume an order exists though it had not been found.…”

Section: Related Workmentioning

confidence: 99%

“…The baseline algorithm (reproduced from our previous work [11]) adds edges to the graph using following rules:…”

Section: Baseline Algorithm For Vtso-readmentioning

confidence: 99%

See 2 more Smart Citations

Completely Verifying Memory Consistency of Test Program Executions

Manovit

Hangal²

The Twelfth International Symposium on High-Performance Computer Architecture, 2006.

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Section: Related Worksupporting

confidence: 67%

Section: Related Workmentioning

confidence: 99%

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Completely Verifying Memory Consistency of Test Program Executions

Manovit

Hangal²

The Twelfth International Symposium on High-Performance Computer Architecture, 2006.

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“…Basically, a consistency criterion defines which value has to be returned when a read operation on a shared object is invoked by a process [11], [14], [18]. The strongest (i.e., most constraining) consistency criterion is atomic consistency [20], also called linearizability [13].…”

Section: Introductionmentioning

confidence: 99%

A Methodological Construction of an Efficient Sequentially Consistent Distributed Shared Memory

Cholvi

Fernández

Jiménez

et al. 2009

The Computer Journal

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The paper proposes a simple protocol that ensures sequential consistency. The protocol assumes that the shared memory abstraction is supported by the local memories of nodes, that can communicate only by exchanging messages through reliable channels. Unlike other sequential consistency protocols, the one proposed here does not rely on a strong synchronization mechanism, such as an atomic broadcast primitive or a central node managing a copy of every shared object. From a methodological point of view, the protocol is built incrementally starting from the very definition of sequential consistency. It has the noteworthy property that a process that issues a write operation never has to wait for other processes. Depending on the current local state, most read operations issued also have the same property.Keywords: Distributed Systems, Distributed Shared Memory, Sequential Consistency. I. INTRODUCTIONThe definition of a consistency criterion is crucial for the correctness of a multiprocess program [10], [23]. Basically, a consistency criterion defines which value has to be returned when a read operation on a shared object is invoked by a process [11], [14], [18]. The strongest (i.e., most constraining) consistency criterion is atomic consistency [20], also called linearizability [13]. It states that a read returns the value written by the latest preceding write, "latest" referring to real-time occurrence order (concurrent writes being totally ordered). Causal consistency [3], [5] is a weaker criterion, stating that a read does not get an overwritten value. Causal consistency allows concurrent writes; consequently, it is possible that concurrent read operations on the same object get different values. This occurs when those values have been produced by concurrent writes. Other consistency criteria weaker than causal consistency have also been proposed [1], [27]. This paper focuses on sequential consistency [16]. This criterion lies between atomic consistency and causal consistency. Informally, it states that a multiprocess program executes correctly if its results could have been produced by executing that program on a single processor system. This means that an execution is correct if we can totally order its operations in such a way that 1) The order of operations in each process is preserved, and 2) Each read operation obtains the latest previously written value, "latest" referring here to the total order. The difference between atomic consistency and sequential consistency lies in the meaning of the word "latest". This word refers to real-time when we consider atomic consistency, while it refers to a logical time notion when we consider sequential consistency. Namely, the logical time defined by the total order.

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“…Distributed shared memory (DSM) is a well-known mechanism for interprocess communication in a distributed environment [1,2]. Roughly speaking, it consists of using read and write operations for inter-process communication thus hiding from the programmer the particular communication technique employed so that they do not need to be involved in the management of messages.…”

Section: Introductionmentioning

confidence: 99%

A parametrized algorithm that implements sequential, causal, and cache memory consistency

Jiménez

Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-Based Processing

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In this paper we present an algorithm that can be used to implement sequential, causal, or cache consistency in distributed shared memory (DSM) systems. For this purpose it has a parameter that allows to choose the consistency model to be implemented. We can also use our algorithm such that not all processes have the same value in this parameter (we have shown the resulting consistency). This characteristic allows to choose a concrete consistency model but implementing it with the algorithm more efficient in each case (in function of the requirements of the applications). As far as we know, this is the first algorithm proposed that implements cache coherence.In our algorithm, when implementing causal and cache consistency all read and write operations are executed locally (i.e., are fast). It is known that no sequential algorithm has only fast memory operations. However, in our algorithm, when implementing sequential consistency all write operations and some read operations are fast. The algorithm uses propagation and full replication, where values written by a process are propagated to the rest of processes. It works in a cyclic turn fashion, with each process of the DSM system broadcasting one message in its turn. The values written by the process are sent in the message (instead of sending one message for each write operation), but unnecessary values are excluded. All this allows to control the amount of message traffic due to the algorithm.

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Efficient algorithms for verifying memory consistency

Cited by 15 publications

References 22 publications

Completely Verifying Memory Consistency of Test Program Executions

Completely Verifying Memory Consistency of Test Program Executions

A Methodological Construction of an Efficient Sequentially Consistent Distributed Shared Memory

A parametrized algorithm that implements sequential, causal, and cache memory consistency

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