multicore

This question already has an answer here: Threading in Python [closed] 7 answers Please consider a class as follow: class Foo: def __init__(self, data): self.data = data def do_task(self): #do something with data In my application I've a list containing several instances of Foo class. The aim is to execute do_task for all Foo objects. A first implementation is simply: #execute tasks of all Foo Object instantiated for f_obj in my_foo_obj_list: f_obj.do_task() I'd like to take advantage of multi-core architecture sharing the for cycle between 4 CPUs of my machine. What's the best way to do it?

I have spend all morning trying to set up multiple cores on a SOLR installation that runs under Apache Tomcat server without success. My solr.xml looks like this: <solr persistent="false" sharedLib="lib"> <cores adminPath="/admin/cores"> <core name="core0" instanceDir="/multicore/core0"> <property name="dataDir" value="/multicore/core0/data" /> </core> <core name="core1" instanceDir="/multicore/core1"> <property name="dataDir" value="/multicore/core1/data" /> </core> </cores> </solr> What is the correct directory structure? Do I need to do change something in the solrconfig.xml? Check that

I am writing a multi-threaded application in Java in order to improve performance over the sequential version. It is a parallel version of the dynamic programming solution to the 0/1 knapsack problem. I have an Intel Core 2 Duo with both Ubuntu and Windows 7 Professional on different partitions. I am running in Ubuntu. My problem is that the parallel version actually takes longer than the sequential version. I am thinking this may be because the threads are all being mapped to the same kernel thread or that they are being allocated to the same core. Is there a way I could ensure that each Java

Say I run a simple single-threaded process like the one below: public class SirCountALot { public static void main(String[] args) { int count = 0; while (true) { count++; } } } (This is Java because that's what I'm familiar with, but I suspect it doesn't really matter) I have an i7 processor (4 cores, or 8 counting hyperthreading), and I'm running Windows 7 64-bit so I fired up Sysinternals Process Explorer to look at the CPU usage, and as expected I see it is using around 20% of all available CPU. But when I toggle the option to show 1 graph per CPU, I see that instead of 1 of the 4 "cores"

On a single core computer, one thread is executing at a time. On each context switch the scheduler checks if the new thread to schedule is in the same process than the previous one. If so, nothing needs to be done regarding the MMU (pages table). In the other case, the pages table needs to be updated with the new process pages table. I am wondering how things happen on a multi-core computer. I guess there is a dedicated MMU on each core, and if two threads of the same process are running simultaneously on 2 cores, each of this core's MMU simply refer to the same page table. Is this true ? Can

Is there some substitute of map which evaluates the list in parallel? I don't need it to be lazy. Something like: pmap :: (a -> b) -> [a] -> [b] letting me pmap expensive_function big_list and have all my cores at 100%. Yes, see the parallel package : ls `using` parList rdeepseq will evaluate each element of the list in parallel via the rdeepseq strategy. Note the use of parListChunk with a good chunk value might give better performance if your elements are too cheap to get a benefit evaluating each one in parallel (because it saves on sparking for each element). EDIT: Based on your question I