cluster-computing

I have a big problem sice 1 week. I try to convert my node.JS project actually run on single core to multi core with cluster. With websockets, at this moment, i have no problems for events but, for xhr-polling or jsonp-polling, i have big problems with socket.io on cluster mode. this is my server configuration : 00-generic.js 'use strict'; var http = require('http'), os = require('os'), cluster = require('cluster'); module.exports = function(done) { var app = this.express, port = process.env.PORT || 3000, address = '0.0.0.0'; if(this.env == 'test'){ port = 3030; } var self = this; var size =

The makeCluster function for the SNOW package has the different cluster types of " SOCK ", " PVM ", " MPI ", and " NWS " but I'm not very clear on the differences among them, and more specifically which would be best for my program. Currently I have a queue of tasks of different length going into a load balancing cluster with clusterApplyLB and am using a 64bit 32-core Windows machine. I am looking for a brief description of the differences among the four cluster types, which would be best for my use and why. Welcome to parallel programming. You may want to peruse the vignette of the excellent

I have a qsub script as #####----submit_job.sh---##### #!/bin/sh #$ -N job1 #$ -t 1-100 #$ -cwd SEEDFILE=/home/user1/data1 SEED=$(sed -n -e "$SGE_TASK_ID p" $SEEDFILE) /home/user1/run.sh $SEED The problem is-- it puts all error and output files (job1.eJOBID & job1.oJOBID) to the same directory from where I am running qsub submit_job.sh while I want to save these file (output and error log file in same different place (specified as $SEED_output). I tried to change the line as /home/user1/run.sh $SEED -o $SEED_output But it didn't work. Any suggestion ?? How can I specify path and name of

I'm running an R program on a linux cluster because it is very demanding on my processor. My program is designed to output multiple (around 15) plots as PDF's into the folder from which the program gathers its input. I want my program to run in the background, and to continue running when I log out of the cluster. First, I tried this: cd /Users/The/Folder/With/My/RScript #changed working directory nohup ./BatchProgram.R & However, this didn't work because it appended the output to a file called nohup.out , and did not output any of the PDF's I need. Next I tried this: cd /Users/The/Folder/With

So I have an input that consists in a dataset and several ML algorithms (with parameter tuning) using scikit-learn. I have tried quite a few attempts on how to execute this as efficiently as possible but at this very moment I still don't have the proper infrastructure to assess my results. However, I lack some background on this area and I need help to get things cleared up. Basically I want to know how the tasks are distributed in a way that exploits as much as possible all the available resources, and what is actually done implicitly (for instance by Spark) and what isn't. This is my

I am looking for a solution that allows me to deploy multiple load balanced Grails instances that have shared cache (EhCache Server ?) and sessions, is this possible ? I can't find any documentation on this (connecting to a common EhCache server or using Distributed EhCache, and sharing sessions (using EhCache too ?))... I'm looking for something that will work like multiple Rails instances with a common memcached and sessions/caches stored in the memcached... I was recently listening to a talk by Dave Klein , author of the book "Grails: A Quick Start Guide", and Mike Allen, Product Managemer

My objective is to setup a high throughput cluster using Kafka as source & Flink as the stream processing engine. Here's what I have done. I have setup a 2-node cluster the following configuration on the master and the slave. Master flink-conf.yaml jobmanager.rpc.address: <MASTER_IP_ADDR> #localhost jobmanager.rpc.port: 6123 jobmanager.heap.mb: 256 taskmanager.heap.mb: 512 taskmanager.numberOfTaskSlots: 50 parallelism.default: 100 Slave flink-conf.yaml jobmanager.rpc.address: <MASTER_IP_ADDR> #localhost jobmanager.rpc.port: 6123 jobmanager.heap.mb: 512 #256 taskmanager.heap.mb: 1024 #512

I have a Java web app that I'm developing, using JBoss Seam as the application framework. I'd like to take advantage of Hibernate Search to provide entity searching capabilities. The integration has gone fine, and I'm getting closer to deployment. The setup for the app in production will be: 2 (or more) Tomcat app servers, load balanced PostgreSQL database on the backend My question is, how can I configure Hibernate Search so that the local Lucene indices are updated on App Server #2 when an entity is persisted on App Server #1, and vice versa? Does Hibernate Search have any sort of clustered