Enrico Sirola wrote:
Hello,
I'd like to perform linear algebra operations on float4/8 arrays. These
tasks are tipically carried on using ad hoc optimized libraries (e.g.
BLAS). In order to do this, I studied a bit how arrays are stored
internally by the DB: from what I understood, arrays are basically a
vector of Datum, and floating point numbers are stored by reference into
Datums. At a first glance, this seem to close the discussion because in
order to perform fast linear algebra operations, you need to store array
items in consecutive memory cells.
What are the alternatives? Create a new specialized data type for
floating point vectors?
Basically, the use-case is to be able to rescale, add and multiply
(element-by-element)
vectors.
I'm not sure about the internals of PostgreSQL (eg. the Datum object(?)
you mention), but if you're just scaling vectors, consecutive memory
addresses shouldn't be absolutely necessary. Add and multiply
operations within a linked list (which is how I'm naively assuming Datum
storage for arrays in memory is implemented) will be "roughly" just as fast.
How many scaling operations are you planning to execute per second, and
how many elements do you scale per operation?
Colin
---------------------------(end of broadcast)---------------------------
TIP 2: Don't 'kill -9' the postmaster
