An Efficient Large-Scale System LTL Model Checking Method for De-memory Jittering

Abstract

The invention discloses a method for removing memory thrashing through the efficient LTL ((Linear Temporal Logic) model detection of a large-scale system. A LHS (Linear Hash Storage) algorithm is adopted to mainly aim at quickly finding a Hash value stored in a Hash table in a disk; no matter whether the classification of the Hash table in the memory is empty or not, the Hash table can be stored in an external memory and is processed by a new technology after being stored in the external memory, and the complexity of I / O is a linear size which is the same with the Hash table; and a CDD (Cached Duplicate Detection) technology permits a duplicate in the memory to be detected through effective access, duplicate detection complexity can be lowered through the LHS and the CDD, the plan of DPM (Dynamic Path Management) enables two nested depth-first stacks to dynamically share a memory cell, and the memory thrashing problem can be solved through the effective management of the stack and the state, wherein the memory thrashing means that the frequent movement of the state in the memory may obviously increase I / O operation frequencies so as to lower the efficiency of the algorithm.

Description

technical field [0001] The invention relates to the field of model detection, in particular to an LTL model detection technology. Background technique [0002] Model checking algorithm is a good method for formal verification of hardware and software. It can automatically detect whether the state of the system is satisfied and can detect counterexamples. Model checking is widely used in formal methods of hardware. But this method faces the problem of state space explosion, because the method will face insufficient memory when using large-scale systems. [0003] In practice, there are mainly two ways of model checking: [0004] In-memory algorithms and out-of-memory algorithms; in order to solve the state explosion problem, in-memory algorithms are mainly aimed at reducing the size and performance of the system. So far, there are many methods for memory algorithms, such as partial order reduction, symmetric reduction, abstraction extraction, combination extraction, symbolic...

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Problems solved by technology

So far, there are many methods for memory algorithms, such as partial order reduction, symmetric reduction, abstraction extraction, combination extraction, symbolic model detection, symbolic path tracing, automata theory, and boundary model detection, etc. However, due to memory In the large-scale system verification, the memory algorithm is not very practical

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