25 papers · ranked by Valyu relevance
Pierrick Pochelu, Hyacinthe Cartiaux, Julien Schleich
High-performance computing (HPC) centers consume substantial power, incurring environmental and operational costs. This review assesses how artificial intelligence (AI), including machine learning (ML) and optimization, improves the efficiency of operational HPC systems. Approximately 1,800 publications from 2019 to…
Villalobos, Johansell, Ruzicka, Josef + 2 more
—Scientific computing in the exascale era demands increased computational power to solve complex problems across various domains. With the rise of heterogeneous computing architectures the need for vendor-agnostic, performance portability frameworks has been highlighted. Libraries like Kokkos have become essential for…
Anna-Lena Roth, Jonas Posner
The growing importance of High-Performance Computing (HPC) requires the systematic integration of parallel programming and performance-oriented competencies into computational science curricula. Effective HPC education combines theoretical foundations with practical experience on real cluster infrastructures, enabling…
John Kruper, Ariel Rokem
Tractography based on diffusion-weighted MRI (dMRI) is the predominant in vivo method for mapping the brain’s white matter. However, it is also one of the most computationally demanding steps in neuroimaging data analysis-requiring the generation and filtering of millions of streamlines per subject. Over the past…
Dragana Grbic
As exascale systems reach unprecedented concurrency, traditional performance analysis tools struggle with the overhead of massive-scale telemetry. We present an accelerated infrastructure for the hpcanalysis framework that leverages a high-performance C++ API and GPU parallelism to enable high-throughput diagnostics.…
Rafael Terra, Diego Carvalho, Denis Jacob Machado, Carla Osthoff + 1 more
Advances in High-Performance Computing (HPC) have enabled increasingly complex genomic analyses, including those in phylogenomics. These analyses contribute to understanding the evolution of viruses and pathogens, improving our knowledge of disease transmission, and supporting targeted public health strategies.…
Zheng, Haoyu, Gao, Shouwei + 2 more
Memory disaggregation is promising to scale memory capacity and improves utilization in HPC systems. However, the performance overhead of accessing remote memory poses a significant challenge, particularly for compute-intensive HPC applications where execution times are highly sensitive to data locality. In this work…
A. Murat Maga, Jean-Christophe Fillion-Robin
The digitization of biological specimens has revolutionized the field of morphology, creating large collections of 3D data, and microCT in particular. This revolution was initially supported by the development of open-source software tools, specifically the development of SlicerMorph extension to the open-source image…
Gerasimos Ntoukas, Gonzalo Rubio, Abbas Ballout, Stefano Colombo + 12 more
We present the GPU acceleration and large-scale performance assessment of HORSES3D, an open-source high-order discontinuous Galerkin solver for computational fluid dynamics. The solver is ported to NVIDIA GPU architectures using OpenACC directives, preserving the original Fortran code structure while enabling…
Ruiyong Zhao, Yibo Hu, Jing Chen, Viktor Sverdlov + 2 more
DRAM-based in-memory computing integrates computational regions into the main memory, enabling local data processing within the memory, thereby achieving faster and more efficient data computation. However, enhancing system performance requires addressing a critical challenge: achieving more general and sufficiently…
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…
Jacob Bradford, Divya Joy, Mattias Winsen, Nicholas Meurant + 5 more
Organisations are challenged when meeting the computational requirements of large-scale bioinformatics analyses using their own resources. Cloud computing has democratised large-scale resources, and to reduce the barriers of working with large-scale compute, leading cloud vendors offer serverless computing, a…
Chun Gong, Dong Yuan, Zijian Zhao, Yuxin Chen + 4 more
The advent of population-scale genomics has created significant bottlenecks in computational infrastructure and storage. Traditional BWA-GATK Best Practices require massive resources, while most existing acceleration solutions depend on specialized hardware like GPUs or FPGAs, increasing costs and limiting deployment.…
Spencer Starr, Yannik Feldner, Patrick Kopper, Marcel Blind + 5 more
With the recent proliferation of heterogeneous, GPU-accelerated supercomputers, high-order computational fluid dynamics (CFD) simulations of complex, turbulent flows are more accessible than ever. To leverage the computing power of these machines, CFD software must adapt. However, complicating the situation is the…
Noam Teyssier, Alexander Dobin
Single-cell genomics is rapidly scaling toward billion-cell atlases, but computational analysis has become a critical bottleneck. Processing multiplexed datasets with existing tools requires substantial computational resources and runtime that become prohibitive at scale. Here we present cyto, an ultra highthroughput…
Mingjing Li, Huihui Zhou, Xiaofeng Xu, Zhiwei Zhong + 15 more
There is a growing necessity for edge training to adapt to dynamically changing environments. Neuromorphic computing represents a significant pathway for highly efficient intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural…
Authors not listed
This research investigates predicting the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO; short HL) gap of natural compounds, a crucial property for understanding molecular electronic behavior relevant to cheminformatics and materials science. To address the high computational…
Páll Melsted, Elís Mar Guðnýjarson, Jóhannes Nordal
We present a GPU implementation of kallisto for RNA-seq transcript quantification. By redesigning the core algorithms: pseudoalignment, equivalence class intersection, and the EM algorithm; for massively parallel execution on GPUs, we achieve a 30–50× speedup over multithreaded CPU kallisto. On a benchmark of 100…
Bonson Wong, Gagandeep Singh, Haris Javaid, Kristof Denolf + 4 more
Nanopore sequencing technologies are used widely in genomics research and their adoption continues to accelerate. ‘Basecalling’ is an essential step in the nanopore sequencing workflow, during which raw electrical signals are translated into nucleotide sequences. The current state-of-the-art basecaller, Oxford Nanopore…
Authors not listed
We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…
Authors not listed
High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Krista Pipho, Greg A. Wray
The PacBio Revio simplifies genome assembly by generating very long reads with very few errors at an affordable price point. Comparative ease of assembly is democratizing access, leading to a larger niche for assembly workflows. HiFi-Helper is a user-friendly snakemake workflow designed to facilitate genome assembly…
Authors not listed
Identifying molecular catalysts that simultaneously achieve high selectivity, fast turnover, and robust stability remains a central challenge in homogeneous catalysis. Traditional discovery pipelines rely heavily on labor-intensive experimental screening, while existing automated computational workflows typically focus…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…