11 papers · ranked by Valyu relevance
Authors not listed
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Mohammed Alaa Ala’anzy, Nurdaulet Tolendi, Baizhan Baubek, Abdulmohsen Algarni + 1 more
Sorting can be approached in two main ways: sequentially and in parallel. In sequential sorting, data is processed in a single-threaded manner, which can be slow for large datasets. However, parallel sorting divides the task across multiple processing units, enabling faster results by processing data simultaneously.…
Amir Hossein Salehi Shayegan
In this work, we present a solution to the critical limitation of qubit capacity in near-term quantum hardware by giving a hybrid framework that integrates the spectral element method (SEM) with distributed quantum computing. Using domain decomposition techniques, the additive and multiplicative Schwarz methods, the…
Mohammad Abdur Rob, Md. Zakir Hossen, Md. Kamal Hossen, Md. Mithun Ali + 2 more
Sorting algorithms play a crucial role in computing, but most are designed with rigid structure that are only efficient under certain conditions. Although some sorting algorithms perform well in some circumstances, they do not perform well on some resistant platforms. This study introduces Wall-L Merge Sort, which…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
Lorién López-Villellas, Cristian Iñiguez, Albert Jiménez-Blanco, Quim Aguado-Puig + 5 more
Since each core performs the same workload as in the single-threaded benchmarks, total memory usage at a given thread count can be precisely computed by multiplying the per-thread memory reported in [btag183-T2] by the number of threads. The resulting multi-threaded memory usage is provided in the [sup1], available as…
Christoph Stelz, Lukas Hübner, Alexandros Stamatakis, Russell Schwartz
Given an input array of elements $E=[e_{0},e_{1},…,e_{n-1}]$, distributed across p processing elements (PEs; e.g. processes or threads), we desire to compute $r=e_{0}⊕e_{1}⊕…⊕e_{n-1}$, where $⊕$ denotes a binary, associative operation (e.g. summation or multiplication). In a distributed reduction, we return the result…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
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Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
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Current methods for predicting molecular porous materials typically exploit prior knowledge of similar systems, which biases the final outcome to a limited exploration space. To design novel structures and materials, the community must be able to evaluate and model all candidates without any bias. In this paper, we…
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This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…