Titlebook: Euro-Par 2021: Parallel Processing; 27th International C Leonel Sousa,Nuno Roma,Pedro Tomás Conference proceedings 2021 Springer Nature Swi

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A Fixed-Parameter Algorithm for Scheduling Unit Dependent Tasks with Unit Communication Delaysund of the makespan, release dates and deadlines of the tasks can be computed. Time windows are defined accordingly. We prove that our scheduling problem is fixed-parameter tractable; the parameter is the maximum number of tasks that are schedulable at the same time considering time windows..A fixed

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Plan-Based Job Scheduling for Supercomputers with Shared Burst Buffers to the emergence of the burst buffer concept—an intermediate persistent storage layer logically positioned between random-access main memory and a parallel file system. Despite the development of real-world architectures as well as research concepts, resource and job management systems, such as Slu

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Taming Tail Latency in Key-Value Stores: A Scheduling Perspectiveple replicas for each value, and read operations often exhibit high tail latencies. Various replica selection strategies have been proposed to address this problem, together with local request scheduling policies. It is difficult, however, to determine what is the absolute performance gain each of t

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A Log-Linear ,-Approximation Algorithm for Parallel Machine Scheduling with a Single Orthogonal Resotermediate persistent fast memory layer, called burst buffers. This is just one of many kinds of renewable resources which are orthogonal to the processors themselves, such as network bandwidth or software licenses. Ignoring orthogonal resources while making scheduling decisions just for processors

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An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical Architecturesbecomes all the more important when PEs have non-uniform communication costs or the input is highly irregular. Typically, mapping is addressed using partitioning, in a two-step approach or an integrated one. Parallel partitioning tools do exist; yet, corresponding mapping algorithms or their public

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A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural Networks use-cases, e.g., autonomous driving, are getting more pervasive and popular. While DNN workloads are executed on Graphics Processing Units (GPUs) in many cases, it is not trivial to improve the inference speed through the conventional DNN weight pruning techniques, due to the parallel architecture

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