Asynchronous and Parallel Programming

So far in this book, you've seen functions and objects that process their inputs immediately using a single thread of execution where the code runs to completion and produces useful results or state changes. In this chapter, you turn your attention to concurrent, parallel, asynchronous, and reactive programs. These represent substantially different approaches to programming from those you've seen so far. Some of the reasons for turning to these techniques are as follows: To achieve better responsiveness in a graphical user interface (GUI) To report progress results during a long-running computation and to support cancellation of these computations To achieve greater throughput in a reactive application or service To achieve faster processing rates on a multiprocessor machine or cluster To take advantage of the I/O parallelism available in modern disk drives or network connections To sustain processing while network and disk I/O operations are in process KeywordsGraphical User InterfaceParallel ProgramMain ThreadMessage QueueWork ThreadThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supporting asynchronization in OpenMP for event-driven programming

The following topics are dealt with: parallel processing; learning (artificial intelligence); graphics processing units; graph theory; parallel algorithms; scheduling; application program interfaces; parallel architectures; storage management; parallel machines.

The spatio-temporal evolution of plasma plumes is accompanied by a series of complex chemical reactions and gas–surface interactions that crucially affect the working characteristics and performance of plasma devices. The direct simulation Monte Carlo (DSMC) and particle-in-cell (PIC) hybrid parallel algorithm is often used to numerically simulate such phenomena. This article discusses the gas–surface interaction models of neutral molecules as elucidated by the parallel DSMC-PIC program. These models include the Maxwell model, the Cercignani–Lampis–Lord model, the complete diffuse reflection (CDR) gas–surface reflection model, and coupling with H combined reactions at the wall. Meanwhile, we consider the ion gas–surface interaction models, which include the CDR model and coupling of the four wall chemical reactions of ions. In addition, the dissociation reactions of H2 and recombination reactions of H are considered in the flow field. For various combinations of models, the simulations reveal the structural characteristics, number density, and coupling effects of gas–surface reflections, gas–surface chemical reactions, chemical reactions in the flow field and electric field, and spatio-temporal evolution of H2, H, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}_{2}^{+}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}^{+}$ </tex-math></inline-formula> , which are shown by the structural characteristics of the number-density contours for different model combinations and analyzed in detail. The gas–surface interaction critically influences the number-density distribution near the wall and in the main flow. It also strongly affects the number-density distribution of light neutral molecules but not heavy neutral molecules and ions and produces the wall stacking effect. Apart from the stacking area, it has an indirect influence on the number-density distribution. The chemical reactions of ions on the wall affect the number-density distribution of all particles in the flow field and decisively affect the ions near the wall. Acceleration by the electric field intensifies the ion diffusion. The effect of coupling of gas–surface interaction and acceleration by the electric field is obvious. Via the number density and temperature, the chemical reactions in the flow field strongly affect the distribution of neutral molecules at the inlet; further, from the inlet, this effect weakens. Neutral particles and ions interact mainly through collisions.