Multiprocessing performance question
PythonList at DancesWithMice.info
Thu Feb 21 13:34:14 EST 2019
George: apologies for mis-identifying yourself as OP.
On 22/02/19 6:04 AM, Israel Brewster wrote:
> Actually not a ’toy example’ at all. It is simply the first step in
> gridding some data I am working with - a problem that is solved by tools
> like SatPy, but unfortunately I can’t use SatPy because it doesn’t
> recognize my file format, and you can’t load data directly. Writing a
> custom file importer for SatPy is probably my next step.
Not to focus on the word "toy", the governing issue is of setup cost cf
the acceleration afforded by the parallel processing. In this case, the
former is/was more-or-less as high as the latter, and your efforts were
That said, if the computer was concurrently performing this task and a
number of others, the number of cores available to you would decrease.
At which point, speeds start heading backwards!
This is largely speculation because only you know the task, objectives,
and circumstances - however, for those 'playing along at home' and
learning from your experiment...
> That said, the entire process took around 60 seconds to run. As this
> step was taking 10, I figured it would be low-hanging fruit for speeding
> up the process. Obviously I was wrong. For what it’s worth, I did manage
> to re-factor the code, so instead of generating the entire grid
> up-front, I generate the boxes as needed to calculate the overlap with
> the data grid. This brought the processing time down to around 40
> seconds, so a definite improvement there.
Doing it on-demand. Now you're talking! Plus, if you're able to 'fit'
the data into each box as it is created, that will help justify the
setup/tear-down overhead cost for each async process.
> Israel Brewster
> Software Engineer
> Alaska Volcano Observatory
> Geophysical Institute - UAF
> 2156 Koyukuk Drive
> Fairbanks AK 99775-7320
> Work: 907-474-5172
> cell: 907-328-9145
>> On Feb 20, 2019, at 4:30 PM, DL Neil <PythonList at DancesWithMice.info
>> <mailto:PythonList at DancesWithMice.info>> wrote:
>> On 21/02/19 1:15 PM, george trojan wrote:
>>> def create_box(x_y):
>>> return geometry.box(x_y - 1, x_y, x_y, x_y - 1)
>>> x_range = range(1, 1001)
>>> y_range = range(1, 801)
>>> x_y_range = list(itertools.product(x_range, y_range))
>>> grid = list(map(create_box, x_y_range))
>>> Which creates and populates an 800x1000 “grid” (represented as a flat
>>> at this point) of “boxes”, where a box is a shapely.geometry.box(). This
>>> takes about 10 seconds to run.
>>> Looking at this, I am thinking it would lend itself well to
>>> parallelization. Since the box at each “coordinate" is independent of all
>>> others, it seems I should be able to simply split the list up into chunks
>>> and process each chunk in parallel on a separate core. To that end, I
>>> created a multiprocessing pool:
>> I recall a similar discussion when folk were being encouraged to move
>> away from monolithic and straight-line processing to modular functions
>> - it is more (CPU-time) efficient to run in a straight line; than it
>> is to repeatedly call, set-up, execute, and return-from a function or
>> sub-routine! ie there is an over-head to many/all constructs!
>> Isn't the 'problem' that it is a 'toy example'? That the amount of
>> computing within each parallel process is small in relation to the
>> inherent 'overhead'.
>> Thus, if the code performed a reasonable analytical task within each
>> box after it had been defined (increased CPU load), would you then
>> notice the expected difference between the single- and multi-process
>> From AKL to AK
>> Regards =dn
More information about the Python-list