DArray: New Blocks and correct returns #406
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Implements a "semantic" hashing algorithm which hashes Thunks based on the functional behavior of the code being executed. The intention is to have a hash which has an identical value across different Julia sessions for tasks which compute the same value. This is important for implementing a "headless" worker-worker cluster, where there is no coordinating head worker, and all workers can see the entire computational program. Hashes are computed automatically and can be queried with `get_task_hash()` while running in a task context, or directly as `get_task_hash(task)` for any Dagger task type. Hashes are also provided within `Dagger.move` calls, where the input task's hash is also available.
Building on Dagger's unified hashing framework, DaggerMPI.jl allows DAGs to execute efficiently under an MPI cluster. Per-task hashes are used to "color" the DAG, disabling execution of each task on all but one MPI worker. Data movement is typically peer-to-peer using MPI Send and Recv, and is coordinated by using tags computed from the same coloring scheme. This scheme allows Dagger's scheduler to remain unmodified and unaware of the existence of an MPI cluster, while still providing "exactly once" execution semantics for each task in the DAG.
…bute function, revising testing forthe darray
…bute function, revising testing forthe darray
Co-authored-by: Julian Samaroo <[email protected]>
This was referenced Jul 20, 2023
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Implemented in #408 |
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New AbstractBlocks to enhance further development and functions that are expected to return a DArray now do it correctly