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Preprint -> published article #70

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Aug 19, 2024
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Preprint -> published article #70

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Preprint -> published article
jrmaddison Aug 12, 2024
763b529
Correct docstring
jrmaddison Aug 19, 2024
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9 changes: 4 additions & 5 deletions checkpoint_schedules/mixed.py
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Expand Up @@ -42,14 +42,13 @@ class MixedCheckpointSchedule(CheckpointSchedule):
Notes
-----
Assumes that the data required to restart the forward has the same size as
the data required to advance the adjoint over a step. An updated version of
the algorithm described in [1].
the data required to advance the adjoint over a step. Described in [1].
This is a offline checkpointing strategy, one adjoint calculation
permitted.

[1] Maddison, J. R. (2023). On the implementation of checkpointing with
high-level algorithmic differentiation. arXiv preprint arXiv:2305.09568.
DOI: 10.48550/arXiv.2305.09568
[1] Maddison, J. R. (2024). Step-based checkpointing with high-level
algorithmic differentiation, Journal of Computational Science 82, 102405,
DOI: https://doi.org/10.1016/j.jocs.2024.102405
"""

def __init__(self, max_n, snapshots, *, storage=StorageType.DISK):
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2 changes: 1 addition & 1 deletion docs/notebooks/tutorial.ipynb
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Expand Up @@ -903,7 +903,7 @@
"\n",
"[5] Herrmann, J. and Pallez (Aupy), G. (2020). H-Revolve: a framework for adjoint computation on synchronous hierarchical platforms. ACM Transactions on Mathematical Software (TOMS), 46(2), 1-25. DOI: https://doi.org/10.1145/3378672.\n",
"\n",
"[6] Maddison, J. R. (2023). On the implementation of checkpointing with high-level algorithmic differentiation. arXiv preprint arXiv:2305.09568. https://doi.org/10.48550/arXiv.2305.09568.\n",
"[6] Maddison, J. R. (2024). Step-based checkpointing with high-level algorithmic differentiation, Journal of Computational Science 82, 102405, DOI: https://doi.org/10.1016/j.jocs.2024.102405.\n",
"\n",
"[7] Pringle, G. C., Jones, D. C., Goswami, S., Narayanan, S. H. K., and Goldberg, D. (2016). Providing the ARCHER community with adjoint modelling tools for high-performance oceanographic and cryospheric computation. https://nora.nerc.ac.uk/id/eprint/516314.\n",
"\n",
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2 changes: 1 addition & 1 deletion docs/source/intro.rst
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Expand Up @@ -51,7 +51,7 @@ References

[5] Herrmann, J. and Pallez (Aupy), G. (2020). H-Revolve: a framework for adjoint computation on synchronous hierarchical platforms. ACM Transactions on Mathematical Software (TOMS), 46(2), 1-25. DOI: 10.1145/3378672.

[6] Maddison, J. R. (2023). On the implementation of checkpointing with high-level algorithmic differentiation. arXiv preprint arXiv:2305.09568. 10.48550/arXiv.2305.09568.
[6] Maddison, J. R. (2024). Step-based checkpointing with high-level algorithmic differentiation, Journal of Computational Science 82, 102405, DOI: https://doi.org/10.1016/j.jocs.2024.102405.

[7] Pringle, G. C., Jones, D. C., Goswami, S., Narayanan, S. H. K., and Goldberg, D. (2016). Providing the ARCHER community with adjoint modelling tools for high-performance oceanographic and cryospheric computation. https://nora.nerc.ac.uk/id/eprint/516314.

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