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- W4302912691 abstract "Spatial decomposition is a popular basis for parallelising code. Cast in the frame of task parallelism, calculations on a spatial domain can be treated as a task. If neighbouring domains interact and share results, access to the specific data needs to be synchronized to avoid race conditions. This is the case for a variety of applications, like most molecular dynamics and many computational fluid dynamics codes. Here we present an unexpected problem which can occur in dependency-driven task parallelization models like StarSs: the tasks accessing a specific spatial domain are treated as interdependent, as dependencies are detected automatically via memory addresses. Thus, the order in which tasks are generated will have a severe impact on the dependency tree. In the worst case, a complete serialization is reached and no two tasks can be calculated in parallel. We present the problem in detail based on an example from molecular dynamics, and introduce a theoretical framework to calculate the degree of serialization. Furthermore, we present strategies to avoid this unnecessary problem. We recommend treating these strategies as best practice when using dependency-driven task parallel programming models like StarSs on such scenarios." @default.
- W4302912691 created "2022-10-07" @default.
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- W4302912691 date "2014-01-17" @default.
- W4302912691 modified "2023-10-18" @default.
- W4302912691 title "Avoiding Serialization Effects in Data-Dependency aware Task Parallel Algorithms for Spatial Decomposition" @default.
- W4302912691 doi "https://doi.org/10.48550/arxiv.1401.4441" @default.
- W4302912691 hasPublicationYear "2014" @default.
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