On the complexity of scheduling checkpoints for computational workflows

This paper deals with the complexity of scheduling computational workflows in the presence of Exponential failures. When such a failure occurs, rollback and recovery is used so that the execution can resume from the last checkpointed state. The goal is to minimize the expected execution time, and we have to decide in which order to execute the tasks, and whether to checkpoint or not after the completion of each given task. We show that this scheduling problem is strongly NP-complete, and propose a (polynomial-time) dynamic programming algorithm for the case where the application graph is a linear chain. These results lay the theoretical foundations of the problem, and constitute a prerequisite before discussing scheduling strategies for arbitrary DAGS of moldable tasks subject to general failure distributions

Scheduling Computational Workflows on Failure-Prone Platforms

International audienceWe study the scheduling of computational workflows on compute resources that experience exponentially distributed failures. When a failure occurs, roll-back and recovery is used to resume the execution from the last checkpointed state. The scheduling problem is to minimize the expected execution time by deciding in which order to execute the tasks in the workflow and whether to checkpoint or not checkpoint a task after it completes. We give a polynomial-time algorithm for fork graphs and show that the problem is NP-complete with join graphs. Our main result is a polynomial-time algorithm to compute the execution time of a workflow with specified to-be-checkpointed tasks. Using this algorithm as a basis, we propose efficient heuristics for solving the scheduling problem. We evaluate these heuristics for representative workflow configurations

Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Two-Level Checkpointing and Verifications for Linear Task Graphs

International audienceFail-stop and silent errors are omnipresent on large-scale platforms. Efficient resilience techniques must accommodate both error sources. To cope with the double challenge, a two-level checkpointing and rollback recovery approach can be used, with additional verifications for silent error detection. A fail-stop error leads to the loss of the whole memory content, hence the obligation to checkpoint on a stable storage (e.g., an external disk). On the contrary, it is possible to use in-memory checkpoints for silent errors, which provide a much smaller checkpointing and recovery overhead. Furthermore, recent detectors offer partial verification mechanisms that are less costly than the guaranteed ones but do not detect all silent errors. In this paper, we show how to combine all of these techniques for HPC applications whose dependency graph forms a linear chain. We present a sophisticated dynamic programming algorithm that returns the optimal solution in polynomial time. Simulation results demonstrate that the combined use of multi-level checkpointing and verifications leads to improved performance compared to the standard single-level checkpointing algorithm