System Formulation and APL Shared Variables

R. H. Lathwell

doi:10.1147/rd.174.0353

How to integrate shared variables in distributed simulation

ACM SIGSIM Simulation Digest ◽

10.1145/233498.233499 ◽

1995 ◽

Vol 25 (2) ◽

pp. 14-41 ◽

Cited By ~ 5

Author(s):

Horst Mehl ◽

Stefan Hammes

Keyword(s):

Distributed Simulation ◽

Shared Variables

Download Full-text

Online replication of shared variables

Proceedings of 17th International Conference on Distributed Computing Systems ◽

10.1109/icdcs.1997.603396 ◽

2002 ◽

Author(s):

A.S. Poulakidas ◽

A.K. Singh

Keyword(s):

Shared Variables

Download Full-text

Shared Variable Extraction and Hardware Implementation for Nonlinear Boolean Functions Based on Swarm Intelligence

Mathematical Problems in Engineering ◽

10.1155/2018/7104764 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Longmei Nan ◽

Xiaoyang Zeng ◽

Yiran Du ◽

Zibin Dai ◽

Lin Chen

Keyword(s):

Swarm Intelligence ◽

Association Rule ◽

Boolean Functions ◽

Association Rule Mining ◽

Operation Time ◽

Lookup Table ◽

Coarse Grained ◽

Rule Mining ◽

Shared Variable ◽

Shared Variables

To solve the problem of complex relationships among variables and the difficulty of extracting shared variables from nonlinear Boolean functions (NLBFs), an association logic model of variables is established using the classical Apriori rule mining algorithm and the association analysis launched during shared variable extraction (SVE). This work transforms the SVE problem into a traveling salesman problem (TSP) and proposes an SVE based on particle swarm optimization (SVE-PSO) method that combines the association rule mining method with swarm intelligence to improve the efficiency of SVE. Then, according to the shared variables extracted from various NLBFs, the distribution of the shared variables is created, and two corresponding hardware circuits, Element A and Element B, based on cascade lookup table (LUT) structures are proposed to process the various NLBFs. Experimental results show that the performance of SVE via SVE-PSO method is significantly more efficient than the classical association rule mining algorithms. The ratio of the rules is 80.41%, but the operation time is only 21.47% when compared to the Apriori method, which uses 200 iterations. In addition, the area utilizations of Element A and Element B expended by the NLBFs via different parallelisms are measured and compared with other methods. The results show that the integrative performances of Element A and Element B are significantly better than those of other methods. The proposed SVE-PSO method and two cascade LUT-structure circuits can be widely used in coarse-grained reconfigurable cryptogrammic processors, or in application-specific instruction-set cryptogrammic processors, to advance the performance of NLBF processing and mapping.

Download Full-text