1 edition of Gil Blas found in the catalog.
|The Physical Object|
|Pagination||xvi, 65 p. :|
|Number of Pages||66|
nodata File Size: 2MB.
Final resource management plan amendment environmental assessment for Department of the Army, Corps of Engineers application for land withdrawal--Yakima Firing Center
But many tasks can be parallelized in a fairly simple way. Write multithreaded or multiprocess code Sometimes you can see how to break your problem into several parallel tasks, but those tasks need some kind of synchronization or communication.
This almost means that you don't take any advantage of parallel processing at all. Not all tasks divide up this nicely.
Gil Blas One way to overcome the limitations of the GIL discussed above is to use multiple full processes instead of threads. Simple parallelization Break your job into smaller jobs and run them simultaneously For example, if you are analyzing data from a pulsar survey, and you have thousands of beams to analyze, each taking a day, the simplest and probably most efficient way to parallelize the task is to simply run each beam as a job. 5s 2 processes 27s 31. Le picaro souhaite atteindre la richesse et la noblesse vainement tandis que Gil Blas devient riche et il obtient ses lettres Gil Blas noblesse.
5s 48s 2 threads 31s 71. dot A,B Not only is this simple and clear to read and write, since numpy knows you want to do a matrix dot product it can use an optimized implementation obtained as part of "BLAS" the Basic Linear Algebra Subroutines. Get your code working first, before even thinking about parallelization.
In principle, this could be changed without too much work.
However, using 2 processes does provide a significant speedup.