parallel-processing

问题 Background I have an image analysis pipeline with parallelised steps. The pipeline is in python and the parallelisation is controlled by dask.distributed . The minimum processing set up has 1 scheduler + 3 workers with 15 processes each. In the first short step of the analysis I use 1 process/worker but all RAM of the node then in all other analysis steps all nodes and processes are used. Issue The admin will install HTCondor as a scheduler for the cluster. Thought In order order to have my

问题 Here is what I am trying to do. Suppose I have a program called myprogram.exe , which I have to execute 1000 times. Under Windows, I could usually do something as simple as: for /L %n in (1,1,1000) do start /myfolder/myprogram.exe However, suppose I only have 5 CPU threads I can devote to running the 1000 instances of myprogram.exe , such that I launch only 5, then when one of these finishes another one is launched, etc until the whole 1000 end. Under Linux and using GNU Parallel, I could

问题 I just bought the Matlab Parallel Computing toolbox. The command matlabpool open opens parallel workers with the number of the cores in my CPU. But each of my CPU core has two threads. According to Windows Task Manager, each worker can only use half performance of one CPU core, which seems could be interpreted as one worker = one thread = "half core". Therefore, after all workers opened, still half of the total power of CPU could be utilized. Is there any other command could help with that?

问题 I was playing with tflite and observed on my multicore CPU that it is not heavily stressed during inference time. I eliminated the IO bottleneck by creating random input data with numpy beforehand (random matrices resembling images) but then tflite still doesn't utilze the full potential of the CPU. The documentation mentions the possibility to tweak the number of used threads. However I was not able to find out how to do that in the Python API. But since I have seen people using multiple

问题 I have a piece of code that loops over a collection and calls httpclient for each iteration. The api that the httpclient calls, takes on average 30-40ms to execute. Calling it sequentially, I get the expected outcome, however as soon as I use Parallel.foreach, it takes longer. Looking closely in the logs, I can see quite a few httpclient calls take more 1000ms to execute and then the time drops back to 30-40ms. Looking in the api logs, I can see it barely goes over 100ms. I am not sure why I

问题 I have a piece of code that loops over a collection and calls httpclient for each iteration. The api that the httpclient calls, takes on average 30-40ms to execute. Calling it sequentially, I get the expected outcome, however as soon as I use Parallel.foreach, it takes longer. Looking closely in the logs, I can see quite a few httpclient calls take more 1000ms to execute and then the time drops back to 30-40ms. Looking in the api logs, I can see it barely goes over 100ms. I am not sure why I

问题 Here's what's been puzzling me: When you schedule a sequence of tasks that are homogenous in terms of content but heterogenous in terms of processing time (not known ex ante) using foreach, how exactly does foreach process these embarrassingly parallel tasks sequentially? For instance, I registered 4 threads registerDoMC(cores=4) and I have 10 tasks and the 4th and the 5th each turned out to be longer than all others combine. Then the first batch is obviously the 1st, 2nd, 3rd and 4th. When

问题 I am attempting to create an all in one parallel processing caret function for training caret models with different inputs. I want the function to be its own process independant of all other calls. The function that I have developed so far seems to be reproducible with some models and not with others. For example, below I train a gbm on the iris data set = fail to reproduce. Then train a rpart model = reproduce (aside from time difference). Is my function sound? Is it okay to specify the

问题 Hello I'm trying to compare speeds between serial and parallel version of 'Game of Life'. I used SFML library to visualize game of life like this. SFML window Serial logic is simple like below. for (int i = 0; i < height; i++) { for (int j = 0; j < width; j++) { int neighbor = 0; // check 8 cells around. // 1 2 3 -1 // 4 5 0 // 6 7 8 +1 // (1) if (gamefieldSerial.isAvailableCell(UP(i), LEFT(j))) { if(gamefieldSerial[UP(i)][LEFT(j)] == LIVE) neighbor++; } // (2) if (gamefieldSerial

问题 Trying to understand how to correctly program with ray . The results below do not seem to agree with the performance improvement of ray as explained here. Environment: Python version: 3.6.10 ray version: 0.7.4 Here are the machine specs: >>> import psutil >>> psutil.cpu_count(logical=False) 4 >>> psutil.cpu_count(logical=True) 8 >>> mem = psutil.virtual_memory() >>> mem.total 33707012096 # 32 GB First, the traditional python multiprocessing with Queue (multiproc_function.py): import time from