Without information about the particular images - the data set you're
working with - it's hard to provide any accurate advice. If the images are
small, maybe it's all overhead. If the images are of a particular form of
PNG, maybe a fast path is missing. And so on.

To get meaningful help for problems like this, the more information you can
provide, the better.

-rob


On Fri, Jun 26, 2020 at 10:59 PM Lee Armstrong <l...@pinkfroot.com> wrote:

> Thanks, I am already maxing out some servers but wondered if it could be
> sped up.
>
> I will give the bimg package a go!
>
> On Friday, June 26, 2020 at 1:55:40 PM UTC+1 ren...@ix.netcom.com wrote:
>
>> Just parallelize in the cloud. At a minimum parallelize locally with
>> multiple Go routines.
>>
>> On Jun 26, 2020, at 7:51 AM, howar...@gmail.com wrote:
>>
>> 
>>
>> I don't know if the CGo transitions for 16 million images will completely
>> swamp the speed gains, or how much post-processing you need to do to these
>> images, but you might try https://github.com/h2non/bimg and see if it
>> gives you any wins. It claims 'typically 4x faster' then the Go Image
>> package. It is a CGO interface to libvips, which is an experimental branch
>> of libspng, which is a performance focused alternative to libpng.
>>
>> Howard
>>
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