![]() ![]() But, as so often in life, you get what you pay for and so for professional results, you often need to use a professional tool. With many of these apps, there are many other ways – often free ways – of achieving the same results. I hope you’ll find that some of these apps could save you a lot of time, energy and effort when put to work in your edit suite. ![]() That said, I’ll still continue to use specific apps for specific parts of the workflow as they tend to do that one thing, very, very well. In the process I’ve even come to discover that apps I was using by default may no longer be needed, as some of the same functionality is now found in Edit Ready. In writing this post I was seeking to share some apps that I make use of on a regular basis and a little of the workflow in how I do. ![]() I think you’re better off not mixing the modes.Essential Apps Every Film Editor Should OwnĮvery now and then it’s helpful to reconsider your workflow and see if there’s a better way of doing things. ![]() If you don’t have those needs, you will usually be better off with the multi-processing approach.ĭoing multi-threading within multi-processing would be an advanced, somewhat odd approach. Threads are required for certain kinds of inter-task communication. Threads are required if you need extremely lightweight tasks, since there is some overhead to each process in the multi-process approach - each process is a separate Python interpreter. Each process can run on its own CPU, so your program can effectively use much more of the system’s resources. In multi-processing, multiple separate Python processes are used (with one thread per process) and each process has its own separate GIL. So when you use multi-threading (multiple threads in one process), you’ll see no performance boost from having 2, 4 or 8 CPUs / cores. This is a complex topic, but stated briefly the GIL prevents a single python process from taking full advantage of multiple CPUs. The performance and scalability of Python is greatly limited by a mechanism deep inside the Python engine called the global interpreter lock or GIL. Going from the above analysis is this the case that every time before writing code for multi-threading or multi-processing we need to check which perform faster and opt-in for that or is there any set of rules that provide concrete rules to select one over the other ?Īlso let me know if all these results are due to the lib concurrent.futures which I used. Multi Threading using double value ( Took 0.2660 Seconds) My_results = executor.map(double_value, a) Multi processing using double value ( Took 2.73 Seconds) My_results = executor.map(list_double_value, a)Ĭase 2 (Using function that takes a value and multiple it by 2) Multi-Threading using list_double_value function ( Took 28 seconds) My_results = executor.map(list_double_value, a) # takes a list and double its value Multiprocess using list_double_value function ( Took 145 Seconds) I am in search of methodology that helps me to pick one of these to use ?īelow is my analysis where I ran multi-process and multi-threaded code on my Intel i7, 8th Gen, 8-core, 16 GB machine using Python 3.7.2 (Also tested it on Python 3.8.2)ĭefining required functions and variablesĪ = np.arange(100000000).reshape(100, 1000000)Ĭase 1 (Using function that take list a input and multiply every elem of it by 2 I have observed that sometime even in CPU bound process multi-threading takes the lead in comparison to multi-processing. From most of the post, I come to know that Multi-threading is I/O and Multi-processing is CPU bound but when I used both in case of CPU bound process the results are not in favour of the hypothesis that one can blindly pick Multi-threading for I/O and Multi-processing for CPU bound.Īs in my case, as the process is CPU bound, the results are in favor of Multi-threading. I have already gone through various post regarding this but none of them clearly answered which to select over the other and not even the methods to check which one suits best for the need. I have already asked a question here regarding multi-threading inside multi-processing, results of which are hard to understand and generated one more popular question Multi-threading V/s Multi-Processing. ![]()
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