Titlebook: High Performance Computing; 8th Latin American C Isidoro Gitler,Carlos Jaime Barrios Hernández,Este Conference proceedings 2022 Springer Na

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An Efficient Vectorized Auction Algorithm for Many-Core and Multicore Architectures nature simplifies the adoption of parallel implementations. With its various processing cores and 512-bit vectorized instructions, many-core and multicore machines have the potential to considerably increase the performance of this algorithm. The aim of this work is to efficiently execute the aucti

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Green Energy HPC Data Centers to Improve Processing Cost Efficiencyne of the most significant operating variables due to the high energy demand required by the different elements that make up a HPC data center. This research proposes the use of clean energy to operate HPC data centers, to allow optimization of the efficiency of the processing operation in these spa

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DICE: Generic Data Abstraction for Enhancing the Convergence of HPC and Big Datad in-memory data systems. In addition, many applications are demanding the processing of data streams. The goal is to develop mechanisms to integrate and hide the diversity of data sources from applications and improve data access performance..In this work, we propose the implementation of a data co

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OCFTL: An MPI Implementation-Independent Fault Tolerance Library for Task-Based Applications paramount importance), FT would be a solved problem. It turns out that the scenario for FT and MPI is intricate. While FT is effectively a reality in these environments, it is usually done by hand. The few exceptions available tie MPI users to specific MPI implementations. This work proposes OCFTL,

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Accelerating Smart City Simulationsand hard to scale. To speed up these simulations and to allow the execution of larger scenarios, this work presents a set of optimizations based on two complementary approaches. The first is an approach inspired by SimPoint to estimate the results of new simulations using previous simulations. This

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Distributed Artificial Intelligent Model Training and Evaluationer tasks. The goal of supervised NN is to classify raw input data according to the patterns learned from an input training set. Training and validation of NN is very computationally intensive. In this paper we present an NN infrastructure to accelerate model training, specifically tuning of hyper-pa

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