Re: Parallelization

2016-07-17 Thread Tony Kelman
Eliot Moss cs.umass.edu> writes: > True ... it also made me think of Python, which is designed to use > parallelized numpy (etc.) libraries, optimized for your platform. > Can use all the hardware threads on your machine, as well as make > good use of vector extensions such as AVX. A 64-bit (x86

Re: Parallelization

2016-07-17 Thread Eliot Moss
On 7/17/2016 7:56 AM, Tony Kelman wrote: Thomas Koenig netcologne.de> writes: This is more of a general numerical ODE or Sundials usage question rather than a cygwin specific one, but I would try openmp or mpi on your function evaluations first, if that's taking most of the time. Do you know

Re: Parallelization

2016-07-17 Thread Tony Kelman
Thomas Koenig netcologne.de> writes: > > I am working on a non-linear, boring system of ODEs, boring being > defined as non-stiff and without other numerical surprises. > So, CV_ADAMS works well. > > The only interesting part is that there are very many of the ODEs, > around 3 at the moment

Parallelization

2016-07-17 Thread Thomas Koenig
I am working on a non-linear, boring system of ODEs, boring being defined as non-stiff and without other numerical surprises. So, CV_ADAMS works well. The only interesting part is that there are very many of the ODEs, around 3 at the moment, and that each ODE depends on around half of the oth

Re: [ANNOUNCEMENT] Re: Updated: octave-4.0.3-1

2016-07-17 Thread Tatsuro MATSUOKA
- Original Message - > From: Marco Atzeri  > To: cygwin@cygwin.com > Cc: > Date: 2016/7/17, Sun 13:25 > Subject: Re: [ANNOUNCEMENT] Re: Updated: octave-4.0.3-1 > > On 17/07/2016 04:23, Tatsuro MATSUOKA wrote: >>> From: Marco Atzeri >>> To: cygwin@cygwin.com >>> Cc: >>> Date: 2016/7