[Jprogramming] gpu-backed J vs Tensorflow

[bill lam]

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" <tkxue at tkxue.org> 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
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