> KaiGai Kohei:
> >It seems to me you are a little bit optimistic.
> >Unlike CPU code, GPU-Sorting logic has to reference device memory space,
> >so all the data to be compared needs to be transferred to GPU devices.
> >Any pointer on host address space is not valid on GPU calculation.
> >Amount of device memory is usually smaller than host memory, so your code
> >needs a capability to combined multiple chunks that is partially sorted...
> >Probably, it is not all here.
>
> Aren't there algorithms which help you if the device memory is limited and the
> data is massive? I have a rough memory because I did a course online, where I
> saw algorithms to deal with such problems I suppose.
>
What I took is a hybrid approach to process data set overs device memory
limitation. First, it split input data stream into multiple (= more than
or equal to 1) chunks. Second, it kicks kernel of bitonic-sorting with
key-comparison function generated on the fly. Third, it kicks dynamic
background worker to run merge-sorting logic by CPU.
It does not try to handle all the sorting stuff in GPU. The point we
should not forget is, CPU/GPU is a way to sorting but not a purpose.
Thanks,
--
NEC OSS Promotion Center / PG-Strom Project
KaiGai Kohei <kaigai@ak.jp.nec.com>