Each deferred task has the potential to spin up a new (parallel) instance, 
though there is some dependence on the scheduler and your Min/Max Pending 
Latency settings (in Application Settings). If $$ is not an object, you 
could increase the Min Idle Instances to ensure the instances are around. 
There is some instance start-up overhead, though that mostly depends on 
your own code/framework.

You'd need to use larger front-end instances to get the memory footprint 
you need. F2's have 256MB, so you'd likely need the big F4 (512MB).

For the channel API, you only need to know the channel ID to send the 
message. It is just a string, so you can include it in your task context 
easily.

Sounds like your plan will work out fine.

j

On Wednesday, 26 December 2012 20:46:21 UTC-6, J.Ganesan wrote:
>
> Each task runs a genetic program. In desktop, each task takes   5 mins 
> and consumes 250 MB ram. A user-level job consists of  10 such tasks. 
> In desktop ( a normal Windows PC ), It takes about 50 minutes to 
> complete one user-level job. 
>
> Can I use App Engine to run the tasks in parallel and achieve a 
> response of  ~5 mins for the user-level job ? 
>
> Specifically, a single http request triggers  10 DeferredTasks. The 
> DeferredTask need not do any datastore operation. Can taskqueue 
> process the DeferredTasks  in parallel and handle the 10 DeferredTasks 
> simultaneously? 
>
> As each DeferredTask runs, it has to notify the progress to the 
> client  using Channels API. Is it possible ? Channel being not 
> serializable, how can I keep a channel as a member variable  to a 
> DeferredTask? 
>
> Could you please share your  experience in CPU and memory intensive 
> DeferredTasks ? 
>
> J.Ganesan 
>
>
>

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