We can hence utilize multithreading by launching each case as a separate thread. We prefer for the main program to parse these, and output a single file containing results from all the simulations. Often, we are also interested in doing some post-processing of the data – each simulation may correspond to just a single point on an XY plot. However, if the number of required cases is larger than the number of CPUs, such an approach will result in non-optimal performance. If the number of cases is small, this can be done by simply building several versions of the executable and launching them individually. Instead of making each case execute faster, we can obtain the final set in shorter time by simply running the multiple cases concurrently. Often, we need to run a single program multiple times to analyze the dependence of results on some input parameter. However, sometimes parallelization is not actually necessary. It requires significant code changes and time devoted to debugging. Parallelization of a serial code is a nontrivial task. Usually the serial (single processor) version is written first, and after it is shown to work for a smaller domain set, the program is parallelized, and eventually used to run simulation on a large domain. These three methods allow you to write your program in a specific way so that it can run faster. In the world of particle plasma simulations, this means you can for instance push hundreds of particles at the same time. They can execute single instruction on multiple data concurrently. GPUs are basically highly optimized vector computers. Cuda is a special interface language that allows you to write code that runs not on the CPU, but on a compatible NVIDIA GPU (graphics card). As such, it is suitable for fine-grade parallelization on machines containing multiple cores sharing the same memory (such as most modern PCs). It is a set of compiler constructs that allow incorporation of multithreading into C and Fortran codes. It is deigned for use on clusters, arrays of hundreds and even thousands of individual computers linked together by network cables. MPI is a widely used networked protocol that allows programs running on different computers to communicate with each other. Code parallelization is the process of modifying a simulation code to make it run faster by splitting the workload among multiple computers (well, in the very general sense).
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