By Ivan V. Sergienko
This paintings is dedicated to the past due Ukrainian laptop scientist V. M. Glushkov at the ninetieth anniversary of his birthday. Dr. Glushkov is understood for his contribution to the realm desktop technology and expertise and this quantity analyzes the guidelines and paths of improvement of informatics formulated by way of him and display their vital function in developing computing device applied sciences of easy examine within the fields of utilized arithmetic, theories of machine programming and computing systems.
A significant slice of the monograph is dedicated to the elucidation of latest effects bought within the box of mathematical modeling of complex procedures, construction of recent tools for fixing and investigating optimization difficulties in several statements and improvement of laptop applied sciences for investigations within the box of financial system, biology, drugs and knowledge safety in systems.
The monograph should be of specific curiosity to informatics experts and specialists utilizing tools of informatics and computing device applied sciences to enquire advanced procedures of other natures and constructing new details applied sciences. it could even be worthwhile for either graduate scholars and postgraduates focusing on computing device Science.
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Additional info for Topical Directions of Informatics: In Memory of V. M. Glushkov
Example text
The algebra and calculi like CCS, CSP, and algebra of processes created earlier revived. New models appeared, including applications oriented to bioinformatics (mobile ambients, for example). The model for interaction of agents and environments [193] emerged in the mid-1990s as a response to the topical (for that time) idea of shifting paradigms from computation to interaction. Another goal was to unify different approaches existing in the theory of interacting concurrent processes. The notion of insertion function and transformation of environment behavior, performed by recursive insertion of agents to environments of different levels, is at the heart of the model of interaction of agents and environments.
In terms of performance, the expediency of k times data compression with relative packing/unpacking performance v ¼ dN/zN is determined by the value of compression isoefficiency coefficient [199]: E¼ 1 NÀ k 0 1 Nþ À1 : v If E > 1 then the compression is efficient even for the same number of processing elements N* ¼ N. It is the best case. In particular, this condition is provided by v > k=ðk À 1Þ: However, if for a particular method of compression E < N/Nmax, where Nmax is the number of cluster cores, then the compression method application for the computer is not appropriate because it does not accelerate the parallel program.
1. Two algorithms Linear Splines and Linear Splines + LZW yield the compression isoefficiency E > 1, which means the best recommendation for use. Linear Splines, which is the second by isoefficiency but faster and easier to implement, was selected for implementation. In strict accordance with the theory, Linear Splines ensure stable acceleration of Duplex Wave Migration (Fig. 8), and the optimal number of processes (the point of run time inflection) is shifted to bigger values (24 instead of 20).