@dabrowski: In my experience, J beats Java-server-mode. J gets C level performance. I've never gotten C-level performance via Java.
@bill: I'm happy to use "J restricted to floating points" -- I have no attachment to doubles. I guess no one has an argument for J+GPU beating wrappers-around-CUDA -- which makes sense, this is consistent with everything I know. On Tue, Dec 19, 2017 at 7:32 PM, bill lam <[email protected]> wrote: > One weakness of J for GPU is that J doesn't support single precision or > half precision float but which are what GPU being commonly used for. The > overhead of conversion from/to double precision may or may not be > significant, it depends on applications, ymmv. > > On Dec 20, 2017 4:39 AM, "TongKe Xue" <[email protected]> wrote: > > > Hi, > > > > In my experience, on the CPU, J beats Java. I suspect this is due to > > Java's GC and J's ability to via "higher representation of ranks/loops" > to > > run highly optimized code. > > > > Is there any reason to believe that GPU-backed-J would beat Tensorflow > on > > Tensor / Deep Learning work ? > > > > Given that much of said works reduces to cuBlas + cuDNN, it seems like > a > > GPU-backed-J, although more concise, would end up calling the same > > functions. > > > > Thanks, > > --TongKe > > ---------------------------------------------------------------------- > > For information about J forums see http://www.jsoftware.com/forums.htm > ---------------------------------------------------------------------- > For information about J forums see http://www.jsoftware.com/forums.htm > ---------------------------------------------------------------------- For information about J forums see http://www.jsoftware.com/forums.htm
