Efficient checkpointing of distributed data structures periodically at key moments during runtime is a recurring fundamental pattern in a large number of uses cases: fault tolerance based on checkpoint-restart, in-situ or post-analytics, reproducibility, adjoint computations, etc. In this context, multilevel checkpointing is a popular technique: distributed processes can write their shard of the data independently to fast local storage tiers, then flush asynchronously to a shared, slower tier of higher capacity. However, given the limited capacity of fast tiers (e.g. GPU memory) and the increasing checkpoint frequency, the processes often run out of space and need to fall back to blocking writes to the slow tiers. To mitigate this problem, compression is often applied in order to reduce the checkpoint sizes. Unfortunately, this reduction is not uniform: some processes will have spare capacity left on the fast tiers, while others still run out of space. In this paper, we study the problem of how to leverage this imbalance in order to reduce I/O overheads for multi-level checkpointing. To this end, we solve an optimization problem of how much data to send from each process that runs out of space to the processes that have spare capacity in order to minimize the amount of time spent blocking in I/O. We propose two algorithms: one based on a greedy approach and the other based on modified minimum cost flows. We evaluate our proposal using synthetic and real-life application traces. Our evaluation shows that both algorithms achieve significant improvements in checkpoint performance over traditional multilevel checkpointing.

The increasing scale and complexity of scientific applications are rapidly transforming the ecosystem of tools, methods, and workflows adopted by the high-performance computing (HPC) community. Big data analytics and deep learning are gaining traction as essential components in this ecosystem in a variety of scenarios, such as, steering of experimental instruments, acceleration of high-fidelity simulations through surrogate computations, and guided ensemble searches. In this context, the batch job model traditionally adopted by the supercomputing infrastructures needs to be complemented with support to schedule opportunistic on-demand analytics jobs, leading to the problem of efficient preemption of batch jobs with minimum loss of progress. In this paper, we design and implement a simulator, CoSim, that enables on-the-fly analysis of the trade-offs arising between delaying the start of opportunistic on-demand jobs, which leads to longer analytics latency, and loss of progress due to preemption of batch jobs, which is necessary to make room for on-demand jobs. To this end, we propose an algorithm based on dynamic programming with predictable performance and scalability that enables supercomputing infrastructure schedulers to analyze the aforementioned trade-off and take decisions in near real-time. Compared with other state-of-art approaches using traces of the Theta pre-Exascale machine, our approach is capable of finding the optimal solution, while achieving high performance and scalability.

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