Robustness of the Young/Daly formula for stochastic iterative applications

Du, Yishu; Marchal, Loris; Pallez, Guillaume; Robert, Yves

doi:10.1145/3404397.3404418

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…Alternative approach To understand the actual behavior of the system, some work [7] consider the bounded slowdown as a function of the size of the job. In this case, this objective is not one to optimize anymore, but more a qualitative way to measure and understand the performance of a solution.…”

Section: Mean (Bounded) Slowdownmentioning

confidence: 99%

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Optimization Metrics for the Evaluation of Batch Schedulers in HPC

Boëzennec,

Dufossé,

Pallez

2023

Lecture Notes in Computer Science

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Machine Learning techniques are taking a prominent position in the design of system softwares. In HPC, many work are proposing to use such techniques (specifically Reinforcement Learning) to improve the performance of batch schedulers. Their main limitation is the lack of transparency of their decision. This underlines the importance of choosing correctly the optimization criteria when evaluating these solutions. In this work, we discuss bias and limitations of the most frequent optimization metrics in the literature. We provide elements on how to evaluate performance when studying HPC batch scheduling. We also propose a new metric: the standard deviation of the utilization, which we believe can be used when the utilization reaches its limits. We then experimentally evaluate these limitations by focusing on the use-case of runtime estimates. One of the information that HPC batch schedulers use to schedule jobs on the available resources is user runtime estimates: an estimation provided by the user of how long their job will run on the machine. These estimates are known to be inaccurate, hence many work have focused on improving runtime prediction.

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Section: Mean (Bounded) Slowdownmentioning

confidence: 99%

“…Note that when jobs fail to complete fully (for instance because their walltime is underestimated), it is interesting to measure the "useful utilization", i.e. the volume of computation that lead to a successful execution [7] . Limits for HPC workloads One of the main limitation concerns machines with lower submission rate (i.e.…”

Section: Utilizationmentioning

confidence: 99%

Optimization Metrics for the Evaluation of Batch Schedulers in HPC

Boëzennec,

Dufossé,

Pallez

2023

Lecture Notes in Computer Science

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“…Recently, the results of [44] have been extended to deal with linear chains whose tasks do not have constant execution times but instead obey some probability distribution [13]. As pointed out above, for general workflows, deciding which tasks to checkpoint has been shown #P-complete [21], but the results of [3] show that if the graph is scheduled in a sequential manner (linearized), then one can derive an optimal checkpointing strategy.…”

Section: Checkpointingmentioning

confidence: 99%

Optimal Checkpointing Strategies for Iterative Applications

Marchal

Pallez

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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This work provides an optimal checkpointing strategy to protect iterative applications from fail-stop errors. We consider a very general framework, where the application repeats the same execution pattern by executing consecutive iterations, and where each iteration is composed of several tasks. These tasks have different execution lengths and different checkpoint costs. Assume that there are n tasks and that task a i , where 0 ≤ i < n, has execution time t i and checkpoint cost C i . A naive strategy would checkpoint after each task. A strategy inspired by the Young/Daly formula would select the task a min with smallest checkpoint cost C min and would checkpoint after every p th instance of that task, leading to a checkpointing period P Y D = pT where T = n−1 i=0 a i is the time per iteration. One would choose the period so that P Y D = pT ≈ √ 2µC min to obey the Young/Daly formula, where µ is the application MTBF. Both the naive and Young/Daly strategies are suboptimal. Our main contribution is to show that the optimal checkpoint strategy is globally periodic, and to design a dynamic programming algorithm that computes the optimal checkpointing pattern. This pattern may well checkpoint many different tasks, and this across many different iterations. We show through simulations, both from synthetic and real-life application scenarios, that the optimal strategy significantly outperforms the naive and Young/Daly strategies.

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Adaptively Periodic I/O Scheduling for Concurrent HPC Applications

Zha

Shen

2022

Electronics

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With the convergence of big data and HPC (high-performance computing), various machine learning applications and traditional large-scale simulations with a stochastically iterative I/O periodicity are running concurrently on HPC platforms, which poses more challenges on the scarcely shared I/O resources due to the ever-growing data transfer demand. Currently the existing heuristic online and periodic offline I/O scheduling methods for traditional HPC applications with a fixed I/O periodicity are not suitable for the applications with stochastically iterative I/O periodicities, which are required to schedule the concurrent I/Os from different applications under I/O congestion. In this work, we propose an adaptively periodic I/O scheduling (APIO) method that optimizes the system efficiency and application dilation by taking the stochastically iterative I/O periodicity of the applications into account. We first build a periodic offline scheduling method within a specified duration to capture the iterative nature. After that, APIO adjusts the bandwidth allocation to resist stochasticity based on the actual length of the computing phrase. In the case where the specified duration does not satisfy the actual running requirements, the period length will be extended to adapt to the actual duration. Theoretical analysis and extensive simulations demonstrate the efficiency of our proposed I/O scheduling method over the existing online approach.

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Robustness of the Young/Daly formula for stochastic iterative applications

Cited by 3 publications

References 30 publications

Optimization Metrics for the Evaluation of Batch Schedulers in HPC

Optimization Metrics for the Evaluation of Batch Schedulers in HPC

Optimal Checkpointing Strategies for Iterative Applications

Adaptively Periodic I/O Scheduling for Concurrent HPC Applications

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