parallel-processing

问题 I want to do block matrix-matrix multiplication with the following C code.In this approach, blocks of size BLOCK_SIZE is loaded into the fastest cache in order to reduce memory traffic during calculation. void bMMikj(double **A , double **B , double ** C , int m, int n , int p , int BLOCK_SIZE){ int i, j , jj, k , kk ; register double jjTempMin = 0.0 , kkTempMin = 0.0; for (jj=0; jj<n; jj+= BLOCK_SIZE) { jjTempMin = min(jj+ BLOCK_SIZE,n); for (kk=0; kk<n; kk+= BLOCK_SIZE) { kkTempMin = min(kk

问题 I want to do block matrix-matrix multiplication with the following C code.In this approach, blocks of size BLOCK_SIZE is loaded into the fastest cache in order to reduce memory traffic during calculation. void bMMikj(double **A , double **B , double ** C , int m, int n , int p , int BLOCK_SIZE){ int i, j , jj, k , kk ; register double jjTempMin = 0.0 , kkTempMin = 0.0; for (jj=0; jj<n; jj+= BLOCK_SIZE) { jjTempMin = min(jj+ BLOCK_SIZE,n); for (kk=0; kk<n; kk+= BLOCK_SIZE) { kkTempMin = min(kk

问题 we need to execute heavy calculation on simple but numerous data. Input data are rows in a BigQuery table, two columns: ID (Integer) and DATA (STRING). The DATA values are of the form "1#2#3#4#..." with 36 values. Ouput data are the same form, but DATA are just transformed by an algorithm. It's a "one for one" transformation. We have tried Apache Beam with Google Cloud Dataflow, but it does not work, there are errors as soon as several workers are instancied. For our POC we use only 18k input

问题 we need to execute heavy calculation on simple but numerous data. Input data are rows in a BigQuery table, two columns: ID (Integer) and DATA (STRING). The DATA values are of the form "1#2#3#4#..." with 36 values. Ouput data are the same form, but DATA are just transformed by an algorithm. It's a "one for one" transformation. We have tried Apache Beam with Google Cloud Dataflow, but it does not work, there are errors as soon as several workers are instancied. For our POC we use only 18k input

问题 An obligatory assurance that I have read the many posts on the topic before posting this. I'm aware that multiprocessing entails a fixed cost, but to the best of my knowledge this doesn't seem to be the problem here. I basically have a number of separate optimisation problems, and want to solve them in parallel. The following code is a simple example: import psutil import multiprocessing as mp import time from scipy.optimize import minimize import numpy as np pset = np.random.uniform(-10,10

问题 An obligatory assurance that I have read the many posts on the topic before posting this. I'm aware that multiprocessing entails a fixed cost, but to the best of my knowledge this doesn't seem to be the problem here. I basically have a number of separate optimisation problems, and want to solve them in parallel. The following code is a simple example: import psutil import multiprocessing as mp import time from scipy.optimize import minimize import numpy as np pset = np.random.uniform(-10,10

问题 I have written a code like following below: Parallel.ForEach(filteredList, (f) => { var conditionMatchCount = itm.AsParallel().Max(i => // One point if ID matches ((i.ItemID == f.ItemID) ? 1 : 0) + // One point if ID and QuantitySold match ((i.ItemID == f.ItemID && i.QuantitySold == f.QuantitySold) ? 1 : 0) ); // Item is missing if (conditionMatchCount == 0) { ListToUpdate.Add(f); missingList.Add(f); } // Item quantity is different else if (conditionMatchCount == 1) { ListToUpdate.Add(f); } }

问题 I have written a code like following below: Parallel.ForEach(filteredList, (f) => { var conditionMatchCount = itm.AsParallel().Max(i => // One point if ID matches ((i.ItemID == f.ItemID) ? 1 : 0) + // One point if ID and QuantitySold match ((i.ItemID == f.ItemID && i.QuantitySold == f.QuantitySold) ? 1 : 0) ); // Item is missing if (conditionMatchCount == 0) { ListToUpdate.Add(f); missingList.Add(f); } // Item quantity is different else if (conditionMatchCount == 1) { ListToUpdate.Add(f); } }

问题 All, I'm using a Java ExecutorService to perform tasks in parallel. Unfortunately, the list of tasks is now reaching the tens of millions. This means that submitting the tasks to the executor service ahead of time is infeasible due to memory constraints. I am able to generate an iterator which dynamically creates the tasks as they are needed, but I'm not sure how best to apply this to the ExecutorService. Should I create a task which pulls the next task from the iterator or is there some

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