parallel-processing

问题 I recently found out about Parallel Collection in Scala 2.9 and was excited to see that the degree of parallelism can be set using collection.parallel.ForkJoinTasks.defaultForkJoinPool.setParallelism. However when I tried an experiment of adding two vectors of size one million each , I find Using parallel collection with parallelism set to 64 is as fast as sequential (Shown in results). Increasing setParallelism seems to increase performance in a non-linear way. I would have atleast expected

问题 Im using GLPK with through Julia, and I need to optimize the same GLPK.Prob repeatedly. What changes between each optimization is that some combination of variables gets fixed to 0 simply put in pseudocode lp = GLPK.Prob() variables_to_block = [[1,2,3], [2,3], [6,7], [19,100,111]...] for i in variables_to_block block_vars(lp, i) simplex(lp) restore_vars(lp, i) end when I then run this, it looks like CPU1 acts as a scheduler, staying in the 9-11% range, and the load on CPU3 and CPU4 alternates

问题 I have the following test module (MyMod.jl) to store some functions in Julia. Some of the core functions are written in serial. Other functions call the core functions in parallel. module MyMod export Dummy,distribute_data,recombine_data,regular_test,parallel_test function Dummy(icol,model,data,A,B) nz,nx,nh = size(model) # = size(A) = size(B) for ih = 1:nh for ix = 1:nx for iz = 1:nz data[iz,icol] += A[iz,ix,ih]*B[iz,ix,ih]*model[iz,ix,ih] end end end end function distribute_data(X, obj_name

问题 The OpenMP specification document says that "When a thread encounters a task construct, it may choose to execute the task immediately or defer its execution until a later time". However, the specification does not say how this choice is made. How does the thread decide when to execute the task immediately and when to defer the execution? If this is implementation specific, how does compilers like gcc solve this? 回答1: There are two conditions coming from the standard: When an if clause is

问题 I'm trying to figure out how to pass functions and packages to the boot() function when running parallel computations. It seems very expensive to load a package or define functions inside a loop. The foreach() function that I often use for other parallel tasks has a .packages and .export arguments that handles this (see this SO question) in a nice way but I can't figure out how to do this with the boot package. Below is a meaningless example that shows what happens when switching to parallel:

问题 I'm trying to figure out how to pass functions and packages to the boot() function when running parallel computations. It seems very expensive to load a package or define functions inside a loop. The foreach() function that I often use for other parallel tasks has a .packages and .export arguments that handles this (see this SO question) in a nice way but I can't figure out how to do this with the boot package. Below is a meaningless example that shows what happens when switching to parallel:

问题 I've built OpenCV using Intel IPP, so I suppose that whenever possible it's used (e.g. matrix multiplication). I want to test the scalability of my parallel application by comparing it with a serial version. In order to do so, when it I do: omp_set_num_threads(1); cv::setNumThreads(1); However, by monitoring the CPU usage I see that multiple CPUs are still used. Why is that? And how can I force the program execution by using just one CPU? 回答1: Re-building OpenCV from source with following

问题 I am still in very early stage of my learning of Python. Apologize in advance if this question sounds stupid. I have this set of data (in table format) that I want to add few calculated columns to. Basically I have some location lon/lat and destination lon/lat, and the respective data time, and I'm calculating the average velocity between each pair. Sample data look like this: print(data_all.head(3)) id lon_evnt lat_evnt event_time \ 0 1 -179.942833 41.012467 2017-12-13 21:17:54 1 2 -177

问题 How can I export the global environment for the beginning of each parallel simulation in foreach? The following code is part of a function that is called to run the simulations. num.cores <- detectCores()-1 cluztrr <- makeCluster(num.cores) registerDoParallel(cl = cluztrr) sim.result.list <- foreach(r = 1:simulations, .combine = list, .multicombine = TRUE, ) %dopar% { #...tons of calculations using many variables... list(vals1, vals2, vals3) } stopCluster(cluztrr) Is it necessary to use

问题 I found some sources online that indicate that there are tools for Octave that allow one to parallelize algorithms, but couldn't find any details or how to get and use them. Any help/insight into this would be great. 回答1: http://octave.sourceforge.net/parallel/ Just install the package and the function reference may help. 来源： https://stackoverflow.com/questions/7047840/parallel-computing-in-octave