I\'m writing a distributed Go/Gomoku bot. Basically the point is to distribute tree search onto many computers. With basic tree search algorithms like DFS this would be very simple, as I could just p
Now I have a serial solver in C++ for solving optimization problems and I am supposed to parallelize my solver with different parameters to see whether it can help improve the performance of the solve
I\'m fairly new to OpenMP and I\'m trying to start an individual thread to process each item in a 2D arra开发者_C百科y.
I have a massive, static dataset and I\'ve a function to apply to it. f is in the form reduce(map(f, dataset)), so I would use the MapReduce s开发者_开发知识库keleton. However, I don\'t want to scat
(Any One There) I am working on vehicle tracking system:- I have n number of buses say 开发者_开发百科b1t1(start at 7 am and stop at 7 pm)
Is it suitable parallelizing loops containing function call(s), or is it much more convenient parallelization of loops which are doing basic operation inside.
We are looking into the solution that facilitate massively parallel data processing. Our processing graphs are often rather complex, so well developed operator framework like one Pervasive DataRush pr
I am writing an application that deals with lots of data (gigabytes). I am considering splitting the data onto multiple hard drives and reading it in parallel. I am wondering what kind of limitations
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Currently I\'m in the process of designing the messaging system for my application (which uses AMQP on the backend via RabbitMQ). There are going to be multiple instances where a method can get data f
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