cluster-computing

I am trying to set up a RabbitMQ cluster on Windows servers, and this requires using shared Erlang cookie file. According to the documentation, all I need to do is to ensure that the root directories on different machines contain the same .erlang.cookie file. So what I did is found these files on both machines and overwrote them with the same shared version. After that all rabbitmqctl commands failed on the machine with new file version with "unable to connect to node..." error message. I tried to restart RabbitMQ Windows service, but still rabbitmqctl complained. I even reinstalled RabbitMQ

In Tomcat 5.0.x you had the ability to set useDirtyFlag="false" to force replication of the session after every request rather than checking for set/removeAttribute calls. <Cluster className="org.apache.catalina.cluster.tcp.SimpleTcpCluster" managerClassName="org.apache.catalina.cluster.session.SimpleTcpReplicationManager" expireSessionsOnShutdown="false" **useDirtyFlag="false"** doClusterLog="true" clusterLogName="clusterLog"> ... The comments in the server.xml stated this may be used to make the following work: <% HashMap map = (HashMap)session.getAttribute("map"); map.put("key","value"); %>

We are migrating to Windows 2008 R2 Standard and will be using a Microsoft Clustering (active-passive) configuration. Our application is heavily dependent on MSMQ private queues and our install creates well over 100 private queues using the following C# code. MessageQueue.Create(".\private$\myqueue", false); Since the install is not running inside the context of the cluster, the queues are created on the local node and not in the cluster. We then tried changing the code to: MessageQueue.Create("MYCLUSTERNAME\private$\myqueue", false); However, you can't create private queues on a different

I want to cluster a dataset (600000 observations), and for each cluster I want to get the principal components. My vectors are composed by one email and by 30 qualitative variables. Each quantitative variable has 4 classes: 0,1,2 and 3. So first thing I'm doing is to load the library FactoMineR and to load my data: library(FactoMineR) mydata = read.csv("/home/tom/Desktop/ACM/acm.csv") Then I'm setting my variables as qualitative (I'm excluding the variable 'email' though): for(n in 1:length(mydata)){mydata[[n]] <- factor(mydata[[n]])} I'm removing the emails from my vectors: mydata2 = mydata[2

In slide 25 of this talk by Twitter's Head of Open Source office, the presenter says that Mesos allows one to track and manage even GPU (I assume he meant GPGPU) resources. But I cant find any information on this anywhere else. Can someone please help? Besides Mesos, are there other cluster managers that support GPGPU? Mesos does not yet provide direct support for (GP)GPUs, but does support custom resource types. If you specify --resources="gpu(*):8" when starting the mesos-slave, then this will become part of the resource offer to frameworks, which can launch tasks that claim to use these