Operations¶
Specify the interface by which functional code units are accessible and executable by the runtime facilities.
Importable Python modules implement a few basic interfaces to obtain computational and data resources and publish results, mediated by the runtime executor.
Runtime¶
How are operations actually executed?
Data flow, checkpoint, and resumption¶
The executor should make sure the worker is provisioned with operation dependencies before transmitting a packet of work to be performed, but the worker still needs to resolve the dependencies for each node.
Note that the abstract work graph contains work that a given worker may not perform (such as array members), but workers need to be able to identify the outputs of operations they are not responsible for executing.
Implementation road map¶
Data staging¶
Fundamental data structures, like arrays or array slices, need to be fingerprinted and staged to the worker, and reference-able in concrete graph records.
Concrete task¶
First, consider a graph with no abstraction. Each operation depends on concrete data explicitly represented in the work record.
- Generate fingerprint for data and operations.
- Produce serialized data node.
- Produce serialized operation node.
- Identify data by fingerprint and localize/confirm availability.
- Deserialize work, (re)initializing local resources. Determine completion status.
- Identify data flow constraints, establish subscriptions and/or chase references.
- Execute and/or publish results to subscribers.
Generating concrete tasks¶
In the simplest abstraction, an operation input is expressed in terms of another operation. Demonstrate how this is translated to a concrete executable task, with implications for checkpointing and resumption.