Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
I am new to Python. I have 2000 files each about 100 MB. I have to read each of them and merge them into a big matrix (or table). Can I use parallel processing for this so that I can save some time? If yes, how? I tried searching and things seem very complicated. Currently, it takes about 8 hours to get this done serially. We have a really big server with one Tera Byte RAM and few hundred processors. How can I efficiently make use of this?
Thank you for your help.
You make be able to preprocess the files in separate processes using the subprocess module; however, if the final table is kept in memory, then that process will end up being you bottleneck.
There is another possible approach using shared memory with mmap objects. Each subprocess can be responsible for loading the files into a subsection of the mapped memory.