Analysis of network and complex system requires too much computing resources. Although the learning curve is deep, the power of parallel computing must be utilized, otherwise, more time will be spent on waiting. Moreover, for exploratory academic research, we will not know what’s the next step until we finish the current analysis. So the research life-cycle becomes hypothesis -> operationalization -> LONG TIME coding and debugging -> LONG TIME waiting for result -> new hypothesis.
With IPython Notebook, parallel computing can be easily operated; however, like what I’ve said: We cannot understand the easiest programming skills unless we are able to operate them. I’ll not come to this post if I do not have to wait for a week only for one result. Playing parallel computing with IPython is easy, but for real jobs, it’s not. Scholars in social science area may be less skilled in programming – we are not trained to be. I’ve made great efforts and finally got some progress which may be laughed by CS guys.
While using IPython Notebook (now named Jupyter Notebook) for parallel computing, Jupyter will start several remote engines beside the local one we are using. These remote engines are blank which means that the variables and functions defined and modules imported on the local engine do not work on the remote ones. Specifically, the puzzle for me was (yes, was!): How to operate the variables, functions, and modules on the remote engines.
Continue reading “Parallel computing using IPython: Important notes for naive scholars without CS background”
