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        <title>Frontiers in High Performance Computing | HPC Applications section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/high-performance-computing/sections/hpc-applications</link>
        <description>RSS Feed for HPC Applications section in the Frontiers in High Performance Computing journal | New and Recent Articles</description>
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        <pubDate>2026-10-05T10:51:49.643+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2026.1923360</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2026.1923360</link>
        <title><![CDATA[Beyond the hype: an empirical assessment of quantum computing in bioinformatics]]></title>
        <pubdate>2026-09-24T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Manuel Arcieri</author><author>Paolo Zuliani</author><author>Giovanni Carotenuto</author><author>Meryam Carrus</author><author>Tiziana Castrignanò</author>
        <description><![CDATA[Quantum computing offers great potential for solving intractable computational challenges in bioinformatics, yet the existing literature remains dominated by speculative proposals rather than empirical evidence. To separate theoretical promise from practical reality, this review systematically assesses the empirical state of quantum computing applications across biological domains. We analyzed fifty-three experimental studies spanning sequence analysis, structural biology, phylogenetics, and biomedical machine learning, evaluating them based on their underlying computational paradigms, dataset scales, comparisons against classical baselines, and adherence to open-science reproducibility standards. Our investigation reveals that, while all comparative studies report performance that matches or exceeds that of classical methods, the vast majority rely on simulated environments or quantum-inspired classical heuristics rather than on physical quantum hardware. Moreover, nearly 77% of the evaluated implementations are restricted to heavily reduced toy datasets due to current technological constraints. Severe reproducibility issues plague the field, with just 30% of the studies providing both publicly accessible source code and experimental data. Although quantum bioinformatics has successfully produced viable proof-of-concept prototypes, the field is fundamentally limited by hardware scaling bottlenecks and a lack of standardized benchmarking. To move from conceptual demonstrations to practical scientific impact, we recommend that the community should prioritize rigorous reproducibility practices, establish transparent evaluation metrics, and clearly distinguish genuine quantum execution from classical simulation.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1638203</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1638203</link>
        <title><![CDATA[Toward a persistent event-streaming system for high-performance computing applications]]></title>
        <pubdate>2025-09-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Matthieu Dorier</author><author>Amal Gueroudji</author><author>Valérie Hayot-Sasson</author><author>Hai Duc Nguyen</author><author>Seth Ockerman</author><author>Renan Souza</author><author>Tekin Bicer</author><author>Haochen Pan</author><author>Philip Carns</author><author>Kyle Chard</author><author>Ryan Chard</author><author>Maxime Gonthier</author><author>Eliu Huerta</author><author>Ben Lenard</author><author>Bogdan Nicolae</author><author>Parth Patel</author><author>Justin Wozniak</author><author>Ian Foster</author><author>Nageswara S. Rao</author><author>Robert B. Ross</author>
        <description><![CDATA[High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1458674</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fhpcp.2024.1458674</link>
        <title><![CDATA[Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications]]></title>
        <pubdate>2024-09-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Claire Songhyun Lee</author><author>V. Hewes</author><author>Giuseppe Cerati</author><author>Kewei Wang</author><author>Adam Aurisano</author><author>Ankit Agrawal</author><author>Alok Choudhary</author><author>Wei-Keng Liao</author>
        <description><![CDATA[IntroductionReconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications.MethodsWe observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets.ResultsOur experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset.DiscussionBy assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes.]]></description>
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