{'fraction_non_alphanumeric': 0.016, 'fraction_numerical': 0.0, 'mean_word_length': 5.072815533980583, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Impact of communication volume on the maximum speedup in Parallel computing based on graph partitioning

In the field of parallel computing, load balancing of parallel simulations is considered as a fundamental problem. The primary goal is to evenly distribute computing load across multiple processors while minimizing processor-to-processor communication. A prevalent approach to solving the problem of balancing is based on a graph model. To balance the load between k processors, we partition the graph into k parts, each one is assigned to a processor. This problem is difficult to solve, but effective heuristics have been proposed, including those based on a multilevel approach, used by most graph partitioning tools. All partitioning methods are only relevant if the number of processors is fixed. It is essential to choose the right number of processors for a simulation to get good performance and more efficiency.In this paper, we test several graphs partitioning using the metis framework then we try to find the optimal number of processors, named in this paper p* to execute a simulation and the max speed up that we can reach using parallelization. We also investigate the intrinsic graph characteristics that affect these parameters.