Thanks Vincent for both your answers. I am planning on using just one machine with up to 24 cores. (If I am not able to get far enough this way then I will think about using the cluster. Using 24 cores will already be a big step up compared with what I am currently doing.)
If the processes can share memory that would probably be good, but it is not essential. As this seems to require extra effort I'll try first to see how it goes without sharing. Andrew On Wednesday, 11 May 2016 14:56:27 UTC+10, vdelecroix wrote: > > On 10/05/16 23:32, Andrew wrote: > > On Wednesday, 11 May 2016 14:00:20 UTC+10, William wrote: > >> > >> > >> It depends on what you are trying to do. For some computations, > >> @parallel is the best possible way to do them, and for others it is > >> the worst possible way. > >> > >> Is there a good to learn about how best to do this (in > sage/python/...)? > > > > The first example that I care about looks something like this: > > > > mat=[[0 for s in xrange(tabs)] for t in xrange(tabs)] > > for s in xrange(tabs): > > for t in xrange(s,tabs): > > mat[s][t]=self._inner_product_st(s,t) > > mat[t][s]=mat[s][t] > > > > The `_inner_product_st` method computes certain structure constants in a > > module. This method is time consuming and slightly recursive with > "basic" > > cases being cached. Parallelising this loop seemed like the right place > to > > me but, to be honest, I have no idea what I am doing! > > At this point it is not clear to me whether you need shared memory or > not between the processes. This can be an important overhead of > parallelization. > -- You received this message because you are subscribed to the Google Groups "sage-support" group. To unsubscribe from this group and stop receiving emails from it, send an email to sage-support+unsubscr...@googlegroups.com. To post to this group, send email to sage-support@googlegroups.com. Visit this group at https://groups.google.com/group/sage-support. For more options, visit https://groups.google.com/d/optout.
