Membrane computing is a computational paradigm inspired by the structure and behavior of a living cell. P Systems are the computing devices that are used to realize membrane computing models. Numerous theoretical studies on many variants of P Systems have shown them to be computationally universal. There is a wide range of applications of P Systems from modeling of biological processes to image processing. Among many variants of P Systems, one of the most important is Enzymatic Numerical P System (ENPS). ENPS is a class of P System in which membranes operate on numerical values. To realize the power of ENPS there are a few simulators developed. Each and every simulator has some advantages as well as some disadvantages. Here, a GPU based simulator using Python as a user interaction language is developed. This tool is a completely parallel variant, compatible with a Python based sequential simulator (PeP) which was the first Python based work for ENPS. The developed simulator uses CUDA to interact with GPU and gives the desired speed up, while processing the membranes. There are two important case studies which show the performance of the developed tool to be far better than the other serial simulators.

膜计算是一种受活细胞的结构和行为启发的计算范例。P Systems是用于实现膜计算模型的计算设备。对P Systems的许多变体进行的大量理论研究表明，它们在计算上是通用的。从生物过程的建模到图像处理，P Systems的应用广泛。在P系统的许多变体中，最重要的一种是酶促数字P系统（ENPS）。ENPS是一类P系统，其中膜根据数值运行。为了实现ENPS的强大功能，开发了一些模拟器。每个模拟器都有其优点和缺点。在此，开发了使用Python作为用户交互语言的基于GPU的模拟器。该工具是完全并行的变体，与基于Python的顺序模拟器（PeP）兼容，这是ENPS的第一个基于Python的工作。开发的模拟器使用CUDA与GPU进行交互，并在处理膜时提供所需的速度。有两个重要的案例研究表明，开发的工具的性能远远优于其他串行模拟器。