parallel-processing

问题 I have 29 Simulink/Matlab Test. It has a lot of different reference models. Before running a 20 second simulation , it has to load all reference models and create a lot of simulation artifacts in a work folder. A lot of reference model are shared in-between test. When running one test at a time, I have no issue, all simulation artifact are created and used to run the various simulation. Everything Passes. When running it all via parallel processing. I have a issue.Some simulation artifact are

问题 OK, I hope this was not asked before, because this is a little tricky to find on the search. I have looked over the F95 manual, but still find this vague: For the simple case of: DO i=0,99 <some functionality> END DO I'm trying to figure out what is the difference between: !$OMP DO PRIVATE(i) DO i=0,99 <some functionality> END DO !$OMP END DO And: !$OMP PARALLEL DO PRIVATE(i) DO i=0,99 <some functionality> END DO !$OMP PARALLEL END DO (Just to point out the difference: the first one has OMP

问题 OK, I hope this was not asked before, because this is a little tricky to find on the search. I have looked over the F95 manual, but still find this vague: For the simple case of: DO i=0,99 <some functionality> END DO I'm trying to figure out what is the difference between: !$OMP DO PRIVATE(i) DO i=0,99 <some functionality> END DO !$OMP END DO And: !$OMP PARALLEL DO PRIVATE(i) DO i=0,99 <some functionality> END DO !$OMP PARALLEL END DO (Just to point out the difference: the first one has OMP

问题 This example from PYMOTW gives an example of using multiprocessing.Pool() where the processes argument (number of worker processes) passed is twice the number of cores on the machine. pool_size = multiprocessing.cpu_count() * 2 (The class will otherwise default to just cpu_count() .) Is there any validity to this? What is the effect of creating more workers than there are cores? Is there ever a case to be made for doing this, or will it perhaps impose additional overhead in the wrong

问题 This example from PYMOTW gives an example of using multiprocessing.Pool() where the processes argument (number of worker processes) passed is twice the number of cores on the machine. pool_size = multiprocessing.cpu_count() * 2 (The class will otherwise default to just cpu_count() .) Is there any validity to this? What is the effect of creating more workers than there are cores? Is there ever a case to be made for doing this, or will it perhaps impose additional overhead in the wrong

问题 We are facing problem that camel file component readLockCheckInterval allow single file to be processed at a time and for next file camel lock, it will wait for readLockCheckInterval time. We have 10000 or more files which we want to process in parallel. I want to use maxMessagesPerPoll attribute for accessing multiple file per poll but with readLockCheckInterval because camel release the file lock if the file is still being copied. It will be great help if there will be any other way to

问题 We are facing problem that camel file component readLockCheckInterval allow single file to be processed at a time and for next file camel lock, it will wait for readLockCheckInterval time. We have 10000 or more files which we want to process in parallel. I want to use maxMessagesPerPoll attribute for accessing multiple file per poll but with readLockCheckInterval because camel release the file lock if the file is still being copied. It will be great help if there will be any other way to

问题 I would like to see something like this working in Julia: using Distributed addprocs(4) @everywhere arr = Array{Int}(undef, 10) for i = 1:10 @spawn arr[i] = i end What is the proper way of doing this? 回答1: You have the following ways to parallelize the process. Threads (requires setting JULIA_NUM_THREADS system variable) arr = Array{Int}(undef, 10) Threads.@threads for i = 1:10 arr[i] = i end SharedArrays using Distributed, SharedArrays addprocs(4) arr = SharedVector{Int}(10) @sync

问题 I would like to see something like this working in Julia: using Distributed addprocs(4) @everywhere arr = Array{Int}(undef, 10) for i = 1:10 @spawn arr[i] = i end What is the proper way of doing this? 回答1: You have the following ways to parallelize the process. Threads (requires setting JULIA_NUM_THREADS system variable) arr = Array{Int}(undef, 10) Threads.@threads for i = 1:10 arr[i] = i end SharedArrays using Distributed, SharedArrays addprocs(4) arr = SharedVector{Int}(10) @sync

问题 I would like to see something like this working in Julia: using Distributed addprocs(4) @everywhere arr = Array{Int}(undef, 10) for i = 1:10 @spawn arr[i] = i end What is the proper way of doing this? 回答1: You have the following ways to parallelize the process. Threads (requires setting JULIA_NUM_THREADS system variable) arr = Array{Int}(undef, 10) Threads.@threads for i = 1:10 arr[i] = i end SharedArrays using Distributed, SharedArrays addprocs(4) arr = SharedVector{Int}(10) @sync