Items tagged with multicore

As I understand it, Maple will detect and use the available cores in a system, if the calculation is suitable for multi-core use.

As I am installing Maple on a multi-user cluster, using a scheduler to run maple scripts, I want to ensure the maple jobs only use the number of cores allocated to the job.  

Is it possible to set the number of cores used ? 

If I have misunderstood how Maple works (I am new to it), or if there is a section in the documentation which explains this, please point me in the right direction.  I haven't found this info so far.

I use maple 12 on my dual core laptop and i plan to buy maple 2016 and an 8 core system to get advandage of multiple processors. 

Does maple 2016 functions get advandage of multiprocessor systems or it will be the same as having one processor?

I'm running calculations like this:

    f := (i,j)-> (some complicated procedure depending on i and j);
    M:= Matrix([Threads:-Seq([Threads:-Seq( f(i,j), j=1..N)], i=1..N)]);

I have a server with 20 cores, but each core has two threads, so this code should max out all 40 threads. But what I notice is only at most 20 threads being used at a time. 

I checked kernelopts(numcpus) returns 20. 

Does anyone have any advice on how to maximize my resource usage?

Dear All


I have a question about Maple 16.

Does Maple 16 is able to use several cores in multi-cores system?

Does Maple 16 is able to execute some common commands such as int, diff, dsolve and solve with capabilities of a multi-cores system?

Our previous article described the design of fast algorithms for multiplying and dividing sparse polynomials. We have integrated these algorithms into the expand and divide commands of Maple 14. In this post I want to talk a bit about what you might see when you try Maple 14. Keep in mind that the product isn't released yet and I don't work for Maplesoft, so general disclaimers apply. Nevertheless, one of the first things you may notice is this.

task manager with maple 14

For double-precision ("hardware") real and complex floating-point operations on Matrices, Vectors, and Arrays Maple makes use of its external-calling mechanism to get to compiled code. A great deal of such compiled code for array operations requires what are known as Basic Linear Algebra Subprograms (BLAS). The BLAS libraries provide support not only directly for Matrix-Vector arithmetic but also indirectly in other external compiled libraries used by Statistics, ArrayTools, LinearAlgebra[Modular], etc.

Maple 10 comes in both a 32bit and 64bit version for Linux. It's possible to run both versions, installed to the same base location, on a machine with the appropriate operating system runtime configuration. There are some interesting performance differences between the two versions.

I'll say a few words about the installation. I installed both under /usr/local/maple10 on an Athlon64 3200+ running the x86-64 version of the Fedora Core 2 operating system. I have the 32bit...

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