Nowadays, it is becoming increasingly important to use the most efficient and most suitable computational resources for algorithmic tools that extract meaningful information from big data and make smart decisions. In this paper, a comparative analysis is provided for performance measurements of various machine learning and bioinformatics software including scikit-learn, Tensorflow, WEKA, libSVM, ThunderSVM, GMTK, PSI-BLAST, and HHblits with big data applications on different high performance computer systems and workstations. The programs are executed in a wide range of conditions such as single-core central processing unit (CPU), multi-core CPU, and graphical processing unit (GPU) depending on the availability of implementation. The optimum number of CPU cores are obtained for selected software. It is found that the running times depend on many factors including the CPU/GPU version, available RAM, the number of CPU cores allocated, and the algorithm used. If parallel implementations are available for a given software, the best running times are typically obtained by GPU, followed by multi-core CPU, and single-core CPU. Though there is no best system that performs better than others in all applications studied, it is anticipated that the results obtained will help researchers and practitioners to select the most appropriate computational resources for their machine learning and bioinformatics projects.