Formal Analysis of Memory Contention in a Multiprocessor System

Multi-core processors along with multi-module memories are extensively being used in high performance computers these days. One of the main performance evaluation metrics in such configurations is the memory contention problem and its effect on the overall memory access time. Usually, this problem is analyzed using simulation or numerical methods. However, these methods either cannot guarantee accurate analysis or are not scalable due to the unacceptable computation times. As an alternative approach, this paper uses theorem proving to analyze the memory contention problem of a multiprocessor system. For this purpose, the paper presents the higher-order-logic formalization of the expectation of a discrete random variable and Discrete-time Markov Reward Models. These foundations are then utilized to analyze the memory contention problem of a multi-processor system configuration with two processors and two memory modules using the HOL theorem prover.

[1]  Sofiène Tahar,et al.  Formalization of Entropy Measures in HOL , 2011, ITP.

[2]  Rabi Bhattacharya,et al.  Stochastic processes with applications , 1990 .

[3]  Joe Hurd,et al.  Formal verification of probabilistic algorithms , 2003 .

[4]  Joost-Pieter Katoen,et al.  Model checking Markov reward models with impulse rewards , 2005, 2005 International Conference on Dependable Systems and Networks (DSN'05).

[5]  Sofiène Tahar,et al.  Formal Reasoning About Finite-State Discrete-Time Markov Chains in HOL , 2013, Journal of Computer Science and Technology.

[6]  Christel Baier,et al.  Principles of model checking , 2008 .

[7]  R. Goldberg Methods of Real Analysis , 1964 .

[8]  Gul A. Agha,et al.  A Markov Reward Model for Software Reliability , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[9]  David Parker,et al.  IMPLEMENTATION OF SYMBOLIC MODEL CHECKING FOR , 2002 .

[10]  Sofiène Tahar,et al.  Formal Reasoning about Classified Markov Chains in HOL , 2013, ITP.

[11]  Alvin W. Drake,et al.  Fundamentals of Applied Probability Theory , 1967 .

[12]  D. Vere-Jones Markov Chains , 1972, Nature.

[13]  Henk C. Tijms,et al.  A First Course in Stochastic Models: Tijms/Stochastic Models , 2003 .

[14]  Michael Sczittnick,et al.  MACOM - A Tool for Evaluating Communication Systems , 1997, MMB.

[15]  Kishor S. Trivedi Probability and Statistics with Reliability, Queuing, and Computer Science Applications , 1984 .

[16]  H. Tijms A First Course in Stochastic Models , 2003 .