Titlebook: Euro-Par 2023: Parallel Processing Workshops; Euro-Par 2023 Intern Demetris Zeinalipour,Dora Blanco Heras,Pierangelo Conference proceeding

WAX

Die Gewöhnung an Nichtantigene GifteThe proposed architecture with efficient decision tree units (DTUs) executes samples in a pipeline model for improving performance. Moreover, a size-effective memory organization is also introduced with the architecture to save the on-chip block ram used for reducing the latency and improving workin

Opponent

https://doi.org/10.1007/978-3-642-91517-8at require the simultaneous use of multiple communication protocols in different parts of the application. In this work, we present for the first time the MTCL C++ communication library. It aims to abstract multiple transport protocols (e.g., MQTT, MPI, TCP) and related implementations under a singl

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BRIEF

https://doi.org/10.1007/978-3-663-20241-7ng capabilities and relatively low cost, GPUs are well-suited to scenarios where such applications require substantial computational resources. However, micro-batching becomes essential for efficient GPU computation within stream processing systems. However, finding appropriate batch sizes to mainta

cyanosis

Corroborate

https://doi.org/10.1007/978-3-642-91575-8 probes can result in an increase in code size, code obfuscation, and an added burden of learning different APIs associated with performance monitoring tools. To mitigate these issues, EDPM, an embedded domain-specific language, was developed to provide a higher level of abstraction for annotating r

Stagger

寄生虫

Die Gleitlager (Längs- und Querlager)c computing, machine learning, and many other fields. Consequently, it has been studied extensively in the past decades, and many ideas for optimizing and predicting SpMV performance have been proposed. Unlike dense matrix operations, where performance is mostly determined by the floating-point capa

艺术

fabricate

https://doi.org/10.1007/978-3-322-98948-2istic (EO-GNH). The framework can be employed across a range of different Internet of Things (IoT) applications, such as smart agriculture and healthcare. The framework uses asynchronous MapReduce and parallel meta-heuristics to support the management of IoT applications, focusing on metrics such as