parallel-processing

问题 I am looking at an open source C++ project which has the following code structure: while(true) { // Do something work if(some_condition_becomes_true) break; __asm volatile ("pause" ::: "memory"); } What does the last statement do? I understand that __asm means that it is an assembly instruction and I found some posts about pause instruction which say that the thread effectively hints the core to release resources and give other thread more resources (in context of hyper-threading). But what

问题 I have a shell script that contains the following loop. i=0 upperlimit=$verylargevariable do complexstuff RunManager file $i i= 'expr $i +1' done This script runs on a quad core machine, and according to top , uses about 15% of each core while executing one iteration of the loop. I'd like to distribute it across the four cores so that each iteration of the loop does complexstuff four times, one on each core, so the resources will be used more efficiently. We're talking about computation that

问题 I have Matlab R2012b for Ubuntu 64 bits. I have a Intel Core i3 CPU M 330 @ 2.13GHz × 4. I want to use parfor to parallelize 4 loops at same time. Because Intel Core i3 has 2 Cores and 4 Threads I use this code: if matlabpool('size') == 0 % checking to see if my pool is already open matlabpool(4) else matlabpool close matlabpool(4) end And I obtain the following error: Error: You requested a minimum of 4 workers, but the cluster "local" has the NumWorkers property set to allow a maximum of 2

问题 I am working with Python (IPython & Canopy) and a RESTful content API, on my local machine (Mac). I have an array of 3000 unique IDs to pull data for from the API and can only call the API with one ID at a time. I was hoping somehow to make 3 sets of 1000 calls in parallel to speed things up. What is the best way of doing this? Thanks in advance for any help! 回答1: Without more information about what you are doing in particular, it is hard to say for sure, but a simple threaded approach may

问题 I am working with Python (IPython & Canopy) and a RESTful content API, on my local machine (Mac). I have an array of 3000 unique IDs to pull data for from the API and can only call the API with one ID at a time. I was hoping somehow to make 3 sets of 1000 calls in parallel to speed things up. What is the best way of doing this? Thanks in advance for any help! 回答1: Without more information about what you are doing in particular, it is hard to say for sure, but a simple threaded approach may

问题 I have a loop which updates a matrix A and I want to make it openmp but I'm not sure what variables should be shared and private. I would have thought just ii and jj would work but it doesn't. I think I need an !$OMP ATOMIC UPDATE somewhere too... The loop just calculates the distance between N and N-1 particles and updates a matrix A. !$OMP PARALLEL DO PRIVATE(ii,jj) do ii=1,N-1 do jj=ii+1,N distance_vector=X(ii,:)-X(jj,:) distance2=sum(distance_vector*distance_vector) distance=DSQRT

问题 I have a loop which updates a matrix A and I want to make it openmp but I'm not sure what variables should be shared and private. I would have thought just ii and jj would work but it doesn't. I think I need an !$OMP ATOMIC UPDATE somewhere too... The loop just calculates the distance between N and N-1 particles and updates a matrix A. !$OMP PARALLEL DO PRIVATE(ii,jj) do ii=1,N-1 do jj=ii+1,N distance_vector=X(ii,:)-X(jj,:) distance2=sum(distance_vector*distance_vector) distance=DSQRT

问题 What are all the similarities and diferences between them, It looks like Java Parallel Stream has some of the element available in RXJava, is that right? 回答1: Rx is an API for creating and processing observable sequences. The Streams API is for processing iterable sequences. Rx sequences are push-based ; you are notified when an element is available. A Stream is pull-based ; it "asks" for items to process. They may appear similar because they both support similar operators/transforms, but the

问题 My foray into parallelization continues. I initially had difficulty installing Rmpi , but I got that going (I needed to sudo apt-get it). I should say that I'm running a machine with Ubuntu 10.10. I ran the same simulation as my previous question. Recall the system times for the unclustered and SNOW SOCK cluster respectively: > system.time(CltSim(nSims=10000, size=100)) user system elapsed 0.476 0.008 0.484 > system.time(ParCltSim(cluster=cl, nSims=10000, size=100)) user system elapsed 0.028

问题 My foray into parallelization continues. I initially had difficulty installing Rmpi , but I got that going (I needed to sudo apt-get it). I should say that I'm running a machine with Ubuntu 10.10. I ran the same simulation as my previous question. Recall the system times for the unclustered and SNOW SOCK cluster respectively: > system.time(CltSim(nSims=10000, size=100)) user system elapsed 0.476 0.008 0.484 > system.time(ParCltSim(cluster=cl, nSims=10000, size=100)) user system elapsed 0.028