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task-Level Checkpointing and Localized Recovery to Tolerate Permanent Node Failures for Nested Fork–Join Programs in Clusters

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SN Computer Science 2024年第3期5卷 320页

作者： Reitz, Lukas Fohry, Claudia Research Group Programming Languages/Methodologies University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

Exascale supercomputers consist of millions of processing units, and this number is still growing. Therefore, hardware failures, such as permanent node failures, become increasingly frequent. They can be tolerated with system-level Checkpoint/Restart, which saves the whole application state transparently and, if needed, restarts the application from the saved state;or with application-level checkpointing, which saves only relevant data via explicit calls in the program. The former approach requires no additional programming expense, whereas the latter is more efficient and allows to continue program execution after failures on the intact resources (localized shrinking recovery). An increasingly popular programming paradigm is asynchronous many-task (AMT) programming. Here, programmers identify parallel tasks, and a runtime system assigns the tasks to worker threads. Since tasks have clearly defined interfaces, the runtime system can automatically extract and save their interface data. This approach, called task-level checkpointing (TC), combines the respective strengths of system-level and application-level checkpointing. AMTs come in many variants, and so far, TC has only been applied to a few, rather simple variants. This paper considers TC for a different AMT variant: nested fork–join (NFJ) programs that run on clusters of multicore nodes under work stealing. We present the first TC scheme for this setting. It performs a localized shrinking recovery and can handle multiple node failures. In experiments with four benchmarks, we observed execution time overheads of around 44 % at 1536 workers, and negligible recovery costs. Additionally, we developed and experimentally validated a prediction model for the running times of the scheme. © The Author(s) 2024.

关键词： asynchronous many-task programming Fault tolerance task-level checkpointing Work stealing

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task-Level Checkpointing for Nested Fork-Join Programs Using Work Stealing

Task-Level Checkpointing for Nested Fork-Join Programs Using...

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29th International Conference on Parallel and Distributed Computing (Euro-Par)

作者： Reitz, Lukas Fohry, Claudia Univ Kassel Res Grp Programming Language Kassel Germany

ISBN: (纸本)9783031488023;9783031488030

Recent Exascale supercomputers consist of millions of processing units, and this number is still growing. Therefore, hardware failures, such as permanent node failures, become increasingly apparent. They can be tolerated with techniques such as Checkpoint/Restart (C/R), which saves the whole application state transparently, and, in case of failure, restarts the application from the saved state;or application-level checkpointing, which saves only relevant data via explicit calls in the program. C/R has the advantage of requiring no additional programming expense, whereas application-level checkpointing is more efficient and allows to continue running the application on the intact resources (localized shrinking recovery). An increasingly popular approach to code parallel applications is asynchronous many-task (AMT) programming. Here, programmers identify parallel subcomputations, called tasks, and a runtime system assigns the tasks to worker threads. Since tasks have clearly defined interfaces, the runtime can automatically extract and save their interface data. This approach, called task-level checkpointing (TC), combines the respective strengths of C/R and application-level checkpointing. AMTs come in many variants, and so far TC has only been applied to a few, rather simple variants. This paper considers TC for a different AMT variant: nested fork-join programs (NFJ) that run on clusters of multicore nodes under work stealing. We present the first TC implementation for this setting and evaluate it with three benchmarks and up to 1280 workers. We observe an execution time overhead of around 28% and neglectable recovery costs. (A preliminary version of this work has been presented in reference [1]. The present paper extends the algorithm therein, and for the first time implements and experimentally evaluates it.)

关键词： asynchronous many-task programming Fault Tolerance task-Level Checkpointing Work Stealing

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An Evaluation of task-Parallel Frameworks for Sparse Solvers on Multicore and manycore CPU Architectures 21

An Evaluation of Task-Parallel Frameworks for Sparse Solvers...

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50th International Conference on Parallel Processing (ICPP)

作者： Alperen, Abdullah Afibuzzaman, Md Rabbi, Fazlay Ozkaya, M. Yusuf Catalyurek, Umit Aktulga, Hasan Metin Michigan State Univ E Lansing MI 48824 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450390682

Recently, several task-parallel programming models have emerged to address the high synchronization and load imbalance issues as well as data movement overheads in modern shared memory architectures. OpenMP, the most commonly used shared memory parallel programming model, has added task execution support with dataflow dependencies. HPX and Regent are two more recent runtime systems that also support the dataflow execution model and extend it to distributed memory environments. We focus on parallelization of sparse matrix computations on shared memory architectures. We evaluate the OpenMP, HPX and Regent runtime systems in terms of performance and ease of implementation, and compare them against the traditional BSP model for two popular eigensolvers, Lanczos and LOBPCG. We give a general outline in regards to achieving parallelism using these runtime systems, and present a heuristic for tuning their performance to balance tasking overheads with the degree of parallelism that can be exposed. We then demonstrate their merits on two architectures, Intel Broadwell (a multicore processor) and AMD EPYC (a modern manycore processor). We observe that these frameworks achieve up to 13.7x fewer cache misses over an efficient BSP implementation across L1, L2 and L3 cache layers. They also obtain up to 9.9x improvement in execution time over the same BSP implementation.

关键词： sparse solvers task parallelism asynchronous many-task programming performance optimization runtime systems OpenMP HPX Regent

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