Sean, What about accumulating the matrix to be solved directly into a special JBLAS based matrix?
On Fri, Apr 19, 2013 at 7:31 AM, Sean Owen <sro...@gmail.com> wrote: > Check out Mikio's presentation, which mentions JNI overhead, at > http://mikiobraun.github.io/jblas/ > > I agree and it's kind of unavoidable. But copying N^2 data for an > operation that's more like N^3 operation seems fine in theory. N still > needs to be large enough. And yes that point has been strangely high. > > Here I think the combination of more suitable LAPACK routine, and > largeish but reasonable N (>100) makes it a win. > > On Fri, Apr 19, 2013 at 3:21 PM, Dmitriy Lyubimov <dlie...@gmail.com> > wrote: > > On Apr 19, 2013 3:49 AM, "Sean Owen" <sro...@gmail.com> wrote: > >> > >> Hey Mikio I posted the stack trace on the jblas-users list FYI. It > >> happens on a random 100x100 matrix, for example -- but not every time. > > I suspect that is due to jni calls being incredibky costly compared to > fpu > > ops. I see very similar behavior i my calls from R to java and back. And > r > > calls lapack too for certain things. >
