parallel-processing

问题 I have a method: invokList(List<Object> list); This method is inside a jar and I have no access to the source code of it. So for that, I need to execute the invokList in a parallel way, can someone help for this? The idea is to split the list to many lists and execute invokList in parallel. I have made this example: import java.util.Arrays; import java.util.Collections; import java.util.List; public class Test { public static void main(String[] args) { List<Integer> list = Arrays.asList(1,2,3

问题 In Flink, as my understanding, JobManager can assign a job to multiple TaskManagers with multiple slots if necessary. For example, one job can be assigned three TaskManagers, using five slots. Now, saying that I execute one TaskManager(TM) with three slots, which is assigned to 3G RAM and one CPU. Is this totally the same as executing three TaskManagers, sharing one CPU, and each of them is assigned to 1 G RAM? case 1 --------------- | 3G RAM | | one CPU | | three slots | | TM | -------------

问题 In Flink, as my understanding, JobManager can assign a job to multiple TaskManagers with multiple slots if necessary. For example, one job can be assigned three TaskManagers, using five slots. Now, saying that I execute one TaskManager(TM) with three slots, which is assigned to 3G RAM and one CPU. Is this totally the same as executing three TaskManagers, sharing one CPU, and each of them is assigned to 1 G RAM? case 1 --------------- | 3G RAM | | one CPU | | three slots | | TM | -------------

问题 I have a function that go over two lists: items and dates. The function return an updated list of items. For now it runs with apply which is not that efficent on million of rows. I want to make it more efficient by parallelizing it. Items in item list are on chronological order, as well as the corresponding date list (item_list and date_list are the same size). This is the df: Date item_list date_list 12/05/20 [I1,I3,I4] [10/05/20, 11/05/20, 12/05/20 ] 11/05/20 [I1,I3] [11/05/20 , 14/05/20]

问题 Collection class comes with a new method " parallelStream " in Java SDK 8. It is obvious that this new method provides a mechanism to consume collections in parallel. But, I wonder about how Java achieve this parallelism. What is the underlying mechanism? Is it simply a multithreaded execution? Or does fork/join framework (coming with Java SDK 7) step in? If the answer is neither then, how does it work and what are the advantages of it over the other two mechanisms? 回答1: Looking at the stream

问题 Collection class comes with a new method " parallelStream " in Java SDK 8. It is obvious that this new method provides a mechanism to consume collections in parallel. But, I wonder about how Java achieve this parallelism. What is the underlying mechanism? Is it simply a multithreaded execution? Or does fork/join framework (coming with Java SDK 7) step in? If the answer is neither then, how does it work and what are the advantages of it over the other two mechanisms? 回答1: Looking at the stream

问题 I am running a monte-carlo simulation in parallel using joblib . I noticed however although my seeds were fixed my results kept changing. However, when I ran the process in series it remained constant as I expect. Below I implement a small example, simulating the mean for a normal distribution with higher variance. Load Libraries and define function import numpy as np from joblib import Parallel, delayed def _estimate_mean(): np.random.seed(0) x = np.random.normal(0, 2, size=100) return np

问题 I am running a monte-carlo simulation in parallel using joblib . I noticed however although my seeds were fixed my results kept changing. However, when I ran the process in series it remained constant as I expect. Below I implement a small example, simulating the mean for a normal distribution with higher variance. Load Libraries and define function import numpy as np from joblib import Parallel, delayed def _estimate_mean(): np.random.seed(0) x = np.random.normal(0, 2, size=100) return np

问题 I am running a monte-carlo simulation in parallel using joblib . I noticed however although my seeds were fixed my results kept changing. However, when I ran the process in series it remained constant as I expect. Below I implement a small example, simulating the mean for a normal distribution with higher variance. Load Libraries and define function import numpy as np from joblib import Parallel, delayed def _estimate_mean(): np.random.seed(0) x = np.random.normal(0, 2, size=100) return np

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