On 31/05/16 12:01, Kouhei Kaigai wrote:
On 05/29/2016 04:55 PM, Kouhei Kaigai wrote:
For the closer integration, it may be valuable if PL/R and PL/CUDA can exchange
the data structure with no serialization/de-serialization when PL/R code tries
to call SQL functions.
I had been thinking about something similar. Maybe PL/R can create an
extension within the R environment that wraps PL/CUDA directly or at the
least provides a way to use a fast-path call. We should probably try to
start out with one common use case to see how it might work and how much
benefit there might be.

My thought is the second option above. If SPI interface supports fast-path
like 'F' protocol, it may become a natural way for other PLs also to
integrate SQL functions in other languages.

IIUC, pg.spi.exec("SELECT my_function(...)") is the only way to call SQL 
inside PL/R scripts.

Correct (currently).

BTW, this is starting to drift off topic I think -- perhaps we should
continue off list?

Some elements are common for PostgreSQL (matrix data type and fastpath SPI
interface). I like to keep the discussion on the list.
Regarding to the PoC on a particular use case, it might be an off-list

NEC Business Creation Division / PG-Strom Project
KaiGai Kohei <kai...@ak.jp.nec.com>

Possibly there should be two matrix types in Postgres: the first would be the default and optimized for small dense matrices, the second would store large sparse matrices efficiently in memory at the expensive of speed (possibly with one or more parameters relating to how sparse it is likely to be?) - for appropriate definitions 'small' & 'large', though memory savings for the latter type might not kick in unless the matrices are big enough (so a small sparse matrix might consume more memory than a nominally larger dense matrix type & a sparse matrix might have to be sufficiently sparse to make real memory savings at any size).

Probably good to think of 2 types at the start, even if the only the first is implemented initially.


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