{'fraction_non_alphanumeric': 0.028037383177570093, 'fraction_numerical': 0.006394490900147565, 'mean_word_length': 5.58252427184466, '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}}
Parallel Programming Models in High‐Performance Cloud (ParaMo 2019)

The notion of cloud computing has changed the way how we utilize computing resources. Since high performance computing (HPC) has long been suffered from underor overutilization of resources, many researchers are trying to adapt HPC applications, such as AI, big data, and computational science, to the cloud environment. With proper adaptation, HPC applications are able to enhance their resource utilization ratio and scalability by using virtualized and on-demand resources on clouds. While we discuss HPC on clouds, we should discuss the parallel programming models as well. Various parallel programming models and their frameworks (e.g., TensorFlow, PyTorch, MapReduce, MPI, OpenMP, OpenCL, and CUDA) has been proposed for parallel computing. For example, the MapReduce programming model has been used for various big data processing applications since it helps to reduce the complexity of problem parallelization such as decomposition, communication, and scheduling. However, a parallel programming model should be carefully selected for HPC applications to achieve high performance and efficient resource usage because their target hardware architectures (e.g., accelerators, multicore CPUs, and memory hierarchy) are different as well as the abstraction levels. For example, MapReduce may not be a suitable selection of parallel programming model for a large-scale graph data processing problem. In addition, since traditional parallel programming models, such as MPI, are implemented for a single tenant cluster environment, applying these models to HPC applications on the cloud is a challenging in terms of resource management. This special issue is an outgrowth of the 1st International Workshop on Parallel Programming Models in High-Performance Cloud (ParaMo 2019) held in Göttingen, Germany. The ParaMo 2019 provided a venue for researchers to discuss recent results and the future challenges to parallel programming models in high-performance cloud.