A statically typed version of the data driven functional parallel computing model is proposed. It enables a representation of dynamically changing parallelism by means of asynchronous serial data ows. We consider the features of the syntax and semantics of the statically typed data driven functional parallel programming language Smile that supports asynchronous sequential ows. Our main idea is to apply the Hoar concept of communicating sequential processes to the computation control on the data readiness. It is assumed that on the data readiness a control signal is emi ed to inform the processes about the occurrence of certain events. e special feature of our approach is that the model is extended with the special asynchronous containers that can generate events on their partial lling. ese containers are a stream and a swarm, each of which has its own speci cs. A stream is used to process data which have identical type.e data comes sequentially and asynchronously at arbitrary time moments.e number of the incoming data elements is initially unknown, so the processing completes on the signal of the end of the stream. A swarm is used to contain independent data of the same type and may be used for the massive parallel operations performing. Unlike a stream, the swarm's size is xed and known in advance. General principles of the operations with the asynchronous sequential ows with an arbitrary order of data arrival are described. e use of the streams and the swarms in various situations is considered. We propose the language constructions which allow us to operate the swarms and streams and describe the speci cs of their application. We provide the sample functions to illustrate the use of the di erent approaches to description of the parallelism: recursive processing of the asynchronous ows, processing of the ows in an arbitrary or prede ned order of operations, direct access and access by the reference to the elements of the streams and swarms, pipelining of calculations. We give a preliminary parallelism assessment which depends on the ratio of the rates of data arrival and their processing. e proposed methods can be used in the development of the future languages and tool-kits of architecture-independent parallel programming.

We perceive, interpret, and remember ongoing experiences through the lens of our prior experiences. Inferring that we are in one type of situation versus another can lead us to interpret the same physical experience differently. In turn, this can affect how we focus our attention, form expectations about what will happen next, remember what is happening now, draw on our prior related experiences, and so on. To study these phenomena, we asked participants to perform simple word list-learning tasks. Across different experimental conditions, we held the set of to-be-learned words constant, but we manipulated how incidental visual features changed across words and lists, along with the orders in which the words were studied. We found that these manipulations affected not only how the participants recalled the manipulated lists, but also how they recalled later (randomly ordered) lists. Our work shows how structure in our ongoing experiences can influence how we remember both our current experiences and unrelated subsequent experiences.

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The article presents a programming paradigm that defines a new style of program development called procedural-parametric programming (PPP). The paradigm is based on parametric polymorphism, which allows the procedures to accept and process variant data types without the algorithmic choice of alternatives within these procedures. In procedural programming languages, such types are described by unions (union in C, C++) or variant entries (in Pascal). Algorithmic processing of variants is carried out by means of conditional operators or switches. This approach is a development of procedural programming methods and acts as an alternative to object-oriented programming. The procedural-parametric paradigm of programming is an extension of the procedural approach. It makes possible to increase the capabilities of the latter by supporting data polymorphism. The application of the proposed approach will allow to increase the functional capabilities of the procedures without making any internal algorithmic changes. Procedural-parametric programming can be used both independently and in combination with other programming paradigms

Today, due to problems in improving computing performance, parallel programming continues to evolve. There are many different languages in which you can write parallel programs. One of them is the functional-threading parallel programming language Pifagor, which in turn is very specific and allows you to write a program with maximum parallelism, as well as it is designed to solve the portability problem of parallel programs. Tools and a library of functions continue to be developed for this language. This study is devoted to the development of elements of the mathematical library and the search for the most effective mathematical parallel algorithms. The following methods are considered and used in the work: sequential, recursive (left and right recursion), factorization, and pairwise comparisons. As a result of the study, a number of mathematical functions were developed, and a study was made of the possibility of using these functions in the development of programs for multiplying large-dimensional matrices. The work demonstrates the effectiveness of using the developed simple functions implemented by different methods in matrix multiplication programs. The prospects of further work in this direction are noted, having in mind the analysis of the possibility of using artificial intelligence methods to increase efficiency and facilitate the development of parallel programs with large-sized matrices.