Titlebook: Euro-Par 2024: Parallel Processing; 30th European Confer Jesus Carretero,Sameer Shende,Martin Schreiber Conference proceedings 2024 The Edi

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Ramon Puigjaner,Dominique Potierx computations found in deep learning applications. Intel oneAPI’s Explicit SIMD (ESIMD) SYCL extension API allows for simpler vectorization of arithmetic and memory operations which is critical in achieving good performance. We explore sparse matrix operations relevant to deep learning applications

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https://doi.org/10.1007/978-3-319-33789-0orkloads. However, the benchmark’s representativeness of real-world HPC and AI workloads is unclear. In this paper, we discuss the HPL-MxP benchmark from a numerical perspective and propose new rules and data generation for numerically meaningful comparisons. We present experiments showing that the

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https://doi.org/10.1007/978-3-658-38618-4xible programmability. Coarse-grained Reconfigurable Arrays (CGRAs) show great potential with their regular parallel architectures and word-level spatio-temporal reconfigurability. However, the mapping of image processing applications on CGRAs faces two main challenges: 1) low-level CGRA programming

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Modeling Uncertainty with Fuzzy Logicjor issue for the High-performance computing (HPC) community. Including reliable energy management to a supercomputer’s resource and job management system (RJMS) is not an easy task. The energy consumption of jobs is rarely known in advance and the workload of every machine is unique and different f

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