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

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https://doi.org/10.1007/978-981-99-2509-4g collective I/O techniques were proposed with the assumption that computer memory is volatile. However, their ability is limited by the size of collective I/O buffers and communication overhead. In this paper, we propose ., a novel collective I/O framework that employs node-local persistent memory

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https://doi.org/10.1007/978-1-4614-4596-8ke CNNs, CapsNets are robust to affine transformation and are able to learn spatial relationships between features of an image. Since CapsNets require significant computing horsepower due to intensive matrix operations, GPUs have become the primary hardware platforms for the execution of CapsNets. I

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ImSPU: Implicit Sharing of?Computation Resources Between Vector and?Scalar Processing Units architecture and is straightforward to implement in hardware. In comparison to traditional SIMD processors, the proposed implicit sharing architecture achieves substantial performance gains while incurring minor hardware overhead.