Temporal information also requires pattern mining, and implementation of periodic mining in these areas has unlocked potential for its use in various domains. Providing insights into trends anomalies and underlying principles in wide range of contexts by discovering recurrent patterns throughout time. Partial periodic pattern mining particularly deals with the observation of patterns that are not constant throughout the data which means there might be missing occurrences, leading to disposal of a potentially valuable pattern. This paper expands on the 3P-BitVectorMiner algorithm, which has shown considerable improvement over preceding implementations such as 3P-Growth in partial periodic pattern mining on temporal datasets. For applications like trend analysis, forecasting, or anomaly identification, faster pattern mining can produce insights at critical moments to make relevant decisions in dynamic contexts. An enhancement to these applications would be to integrate the process with MPI and boost performance in mining partial periodic patterns by distributing computational tasks across multiple processors. This work attempts to illustrate the advantages of parallel processing for partial periodic pattern mining by examining the differences between the integrated model and the sequential model by considering various datasets. In datasets with more than 3000 transactions and over 20,000 partial periodic patterns, MPI shows double the speed when using 3 processors and an increase by a factor of three when using 4 processors. It shows similar improvements in datasets of over 300,000 transactions having about 1000 partial periodic patterns showcasing notably consistent impact across different types of datasets.