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This is a preparatory PR for #307 |
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This commit refactors `_readSelection` in such a manner that: 1. Canonical selections don't require post-read shuffling. 2. The reading of the dataset is moved to a separate function. As a consequence, there's no need for an optimization for `std::string`, since those only mattern when reading from the `"@library"`, which should only happen indirectly. Hence, we free to ensure that those are always canonical.
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This commit refactors `_readSelection` in such a manner that: 1. Canonical selections don't require post-read shuffling. 2. The reading of the dataset is moved to a separate function. As a consequence, there's no need for an optimization for `std::string`, since those only mattern when reading from the `"@library"`, which should only happen indirectly. Hence, we free to ensure that those are always canonical.
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## Context When using `WholeCell` load-balancing, the access pattern when reading parameters during synapse creation is extremely poor and is the main reason why we see long (10+ minutes) periods of severe performance degradation of our parallel filesystem when running slightly larger simulations on BB5. Using Darshan and several PoCs we established that the time required to read these parameters can be reduced by more than 8x and IOps can be reduced by over 1000x when using collective MPI-IO. Moreover, the "waiters" where reduced substantially as well. See BBPBGLIB-1070. Following those finding we concluded that neurodamus would need to use collective MPI-IO in the future. We've implemented most of the required changes directly in libsonata allowing others to benefit from the same optimizations should the need arise. See, BlueBrain/libsonata#309 BlueBrain/libsonata#307 and preparatory work: BlueBrain/libsonata#315 BlueBrain/libsonata#314 BlueBrain/libsonata#298 By instrumenting two simulations (SSCX and reduced MMB) we concluded that neurodamus was almost collective. However, certain attributes where read in different order on different MPI ranks. Maybe due to salting hashes differently on different MPI ranks. ## Scope This PR enables neurodamus to use collective IO for the simulation described above. ## Testing <!-- Please add a new test under `tests`. Consider the following cases: 1. If the change is in an independent component (e.g, a new container type, a parser, etc) a bare unit test should be sufficient. See e.g. `tests/test_coords.py` 2. If you are fixing or adding components supporting a scientific use case, affecting node or synapse creation, etc..., which typically rely on Neuron, tests should set up a simulation using that feature, instantiate neurodamus, **assess the state**, run the simulation and check the results are as expected. See an example at `tests/test_simulation.py#L66` --> We successfully ran the reduced MMB simulation, but since SSCX hasn't been converted to SONATA, we can't run that simulation. ## Review * [x] PR description is complete * [x] Coding style (imports, function length, New functions, classes or files) are good * [ ] Unit/Scientific test added * [ ] Updated Readme, in-code, developer documentation --------- Co-authored-by: Luc Grosheintz <luc.grosheintz@gmail.ch>
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## Context When using `WholeCell` load-balancing, the access pattern when reading parameters during synapse creation is extremely poor and is the main reason why we see long (10+ minutes) periods of severe performance degradation of our parallel filesystem when running slightly larger simulations on BB5. Using Darshan and several PoCs we established that the time required to read these parameters can be reduced by more than 8x and IOps can be reduced by over 1000x when using collective MPI-IO. Moreover, the "waiters" where reduced substantially as well. See BBPBGLIB-1070. Following those finding we concluded that neurodamus would need to use collective MPI-IO in the future. We've implemented most of the required changes directly in libsonata allowing others to benefit from the same optimizations should the need arise. See, BlueBrain/libsonata#309 BlueBrain/libsonata#307 and preparatory work: BlueBrain/libsonata#315 BlueBrain/libsonata#314 BlueBrain/libsonata#298 By instrumenting two simulations (SSCX and reduced MMB) we concluded that neurodamus was almost collective. However, certain attributes where read in different order on different MPI ranks. Maybe due to salting hashes differently on different MPI ranks. ## Scope This PR enables neurodamus to use collective IO for the simulation described above. ## Testing <!-- Please add a new test under `tests`. Consider the following cases: 1. If the change is in an independent component (e.g, a new container type, a parser, etc) a bare unit test should be sufficient. See e.g. `tests/test_coords.py` 2. If you are fixing or adding components supporting a scientific use case, affecting node or synapse creation, etc..., which typically rely on Neuron, tests should set up a simulation using that feature, instantiate neurodamus, **assess the state**, run the simulation and check the results are as expected. See an example at `tests/test_simulation.py#L66` --> We successfully ran the reduced MMB simulation, but since SSCX hasn't been converted to SONATA, we can't run that simulation. ## Review * [x] PR description is complete * [x] Coding style (imports, function length, New functions, classes or files) are good * [ ] Unit/Scientific test added * [ ] Updated Readme, in-code, developer documentation --------- Co-authored-by: Luc Grosheintz <luc.grosheintz@gmail.ch>
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This commit refactors
_readSelectionin such a manner that:As a consequence, there's no need for an optimization for
std::string, sincethose only matters when reading from the
"@library", which should only happenindirectly. Hence, we free to ensure that those are always canonical.