We study the problem of checkpointing strategies for adjoint computation on synchronous hierarchical platforms, specifically computational platforms with several levels of storage with different writing and reading costs. When reversing a large adjoint chain, choosing which data to checkpoint and where is a critical decision for the overall performance of the computation. We introduce H-R evolve , an optimal algorithm for this problem. We make it available in a public Python library along with the implementation of several state-of-the-art algorithms for the variant of the problem with two levels of storage. We provide a detailed description of how one can use this library in an adjoint computation software in the field of automatic differentiation or backpropagation. Finally, we evaluate the performance of H-R evolve and other checkpointing heuristics though an extensive campaign of simulation.

中文翻译：

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人力资源发展

我们研究了同步分层平台上伴随计算的检查点策略问题，特别是具有不同写入和读取成本的多级存储的计算平台。在反转大型伴随链时，选择要检查点的数据和位置是计算整体性能的关键决定。我们介绍人力资源发展，这个问题的最优算法。我们在公共 Python 库中提供了它，并为具有两个存储级别的问题的变体实施了几种最先进的算法。我们详细描述了如何在自动微分或反向传播领域的伴随计算软件中使用该库。最后，我们评估HR的表现发展通过广泛的模拟活动和其他检查点启发式方法。