27 papers · ranked by Valyu relevance
Zhiwei Zhang, Shuwang Li, John Lowengrub, Steven M. Wise
We present a fast, unconditionally energy-stable numerical scheme for simulating vesicle deformation under osmotic pressure using a phase-field approach. The model couples an Allen–Cahn equation for the biomembrane interface with a variable-mobility Cahn–Hilliard equation governing mass exchange across the membrane.…
Yasir Afzal, Naila Nawaz, Abdullah Ayub Khan, Muhammad Jawad Yousaf + 6 more
Data center management, the foundation of contemporary cloud computing, has made energy saving a top priority. Among other difficulties, the placement of virtual machines (VMs) has a major impact on data center resource and energy usage. Assigning VMs to physical machines (PMs) is a challenging NP-hard problem…
Saurabhsingh Rajput, Tushar Sharma
Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance. In this paper, we replace…
Marco Savioli, Paolo Calligari, Ugo Locatelli, Gianfranco Bocchinfuso
We introduce GROMODEX, a novel tool designed to optimise GROMACS molecular dynamics (MD) simulations using a structured Design of Experiments (DoE) approach. GROMACS, though efficient, requires extensive tuning of parameters to perform optimally on different hardware and molecular systems. Manual tuning is tedious and…
William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington
The efficient coding hypothesis presents a compelling success story for theoretical and systems neuroscience. It marshals a unifying idea, that neural codes can be understood as efficient encodings of natural stimuli, to explain phenomena from across sensory systems, sometimes with exquisite precision. However, similar…
Renée A. Sirbu, Luciano Floridi
Biological computing (biocomputing) leverages biologically derived materials and processes, such as DNA and protein synthesis, to perform computational tasks. Biocomputing offers significant advantages over traditional silicon-based systems in terms of scalability, energy efficiency, computational flexibility, and…
ZiChuan He, Hui Zhong, XiaoHua Shi, ChangHai Zhao + 2 more
Deep neural networks (DNNs) are computationally intensive and optimized in different ways. Some compiler optimizations for DNNs could achieve performance almost the same as, or even better than, manual optimizations. However, the former mechanisms usually require an unbearably long optimization time in the tuning…
Parra-Royón, Manuel, Rodríguez-Gallardo, Álvaro + 14 more
—The Square Kilometre Array (SKA) is a nextgeneration radio astronomy-driven big data facility that will revolutionise our understanding of the Universe and the laws of fundamental physics, and needs innovative solutions for efficient data processing. The SKA Regional Centres Network (SRCNet) is a collaborative…
Pande, Sohan Kumar, Panda, Sanjaya Kumar + 2 more
—Recent trends of technology have explored a numerous applications of cloud services, which require a significant amount of energy. In the present scenario, most of the energy sources are limited and have a greenhouse effect on the environment. Therefore, it is the need of the hour that the energy consumed by the cloud…
Jiahao Li, Ming Xu, Heng Dong, Bin Lan + 5 more
The deployment of Spiking Neural Networks (SNNs) on resource-constrained edge devices is hindered by a critical algorithm-hardware mismatch: a fundamental trade-off between the accuracy degradation caused by aggressive quantization and the resource redundancy stemming from traditional decoupled hardware designs. To…
Fabio Cumbo, Kabir Dhillon, M. Hassan Najafi, Sercan Aygun + 1 more
The exponential growth of genomic databases necessitates alignment-free methods for comparing genomes. While MinHash-based tools have revolutionized this field by efficiently estimating the Average Nucleotide Identity based on k-mer sets, they inherently discard structural genomic information. We introduce HyperSketch…
Rafael Ravedutti Lucio Machado, Jan Eitzinger, Georg Hager, Gerhard Wellein
This paper discusses the challenges encountered when analyzing the energy efficiency of synthetic benchmarks and the Gromacs package on the Fritz and Alex HPC clusters. Experiments were conducted using MPI parallelism on full sockets of Intel Ice Lake and Sapphire Rapids CPUs, as well as Nvidia A40 and A100 GPUs. The…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Kajol Kulkarni, Samuel Kemmler, Anna Schwarz, Gedik + 5 more
Energy efficiency has emerged as a central challenge for modern high-performance computing (HPC) systems, where escalating computational demands and architectural complexity have led to significant energy footprints. This paper presents the collective experience of the EuroHPC JU Center of Excellence in Exascale CFD…
Ali Zahir, Ashiq Anjum, Mark Wilkinson, Jeyan Thiyagalingam
The growing complexity and scale of scientific workflows in high performance computing (HPC) environments have led to significant challenges in managing energy consumption without compromising computational performance. Traditional scheduling strategies often fail to account for the complex interplay between thermal…
Kun Ma, Lingyu Xu
Robot applications encompass a multitude of edge computing tasks, such as image processing, health monitoring, path planning, and infotainment. However, task scheduling within such environments remains a significant challenge due to the inherent limitations of edge computing resources and the dynamically fluctuating…
Kayson Fakhar, Danyal Akarca, Andrea I. Luppi, Stuart Oldham + 5 more
Brains are often described as cost-efficient communication networks, optimally balancing the cost of long connections with the benefits of fast communication. Here, inspired by the “use it or lose it” principle, we present a novel game-theoretic model of self-organizing neural units and show that the brain is, in fact…
Samar Awad, Marwa Gamal, Khaled Abd El Salam, Rehab F. Abdel-Kader
With the rapid advancement of fog-cloud computing, task offloading and workflow scheduling have become pivotal in determining system performance and cost efficiency. To address the inherent complexity of this heterogeneous environment, a novel hybrid optimization strategy is introduced, integrating the Improved…
Lütfiye Özlem Akkan, AbdElRahman Ahmed ElSaid
The rapid development and expansion of the Internet of Things (IoT) ecosystem require managing increasing computational demands with the aid of advanced hierarchical architectures. The integration of a complete four-tier hierarchy-including Mobile, Edge, Fog, and Cloud layers-and the priority requirements are being…
Wanbo Zheng, Qiuping Yang, Kerong Chen, Siqi Li + 2 more
With the growing demand for computation-intensive and latency-critical tasks in intelligent mining, the computing capabilities of terminal devices are becoming increasingly inadequate. Consequently, task offloading has emerged as a vital mechanism. However, existing approaches often depend on centralized resource…
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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…
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We present a comprehensive theoretical analysis of quantum subspace diagonalization methods for molecular electronic structure calculations, establishing rigorous complexity bounds and convergence guarantees. Building on recent developments in adaptive quantum algorithms for chemical systems, we formulate a general…
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This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
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This paper addresses the challenge of decarbonizing global energy systems by proposing the Shibah Integrated Bio-Electro-AI CCUS-H2 Framework, a multidisciplinary approach that combines hydrogen production, storage, and utilization with carbon capture, utilization, and storage (CCUS). The framework tackles high costs…
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Continuous manufacturing processes offer significant advantages over batch processes, including easier scalability, reduced costs, lower raw material and solvent consumption, and improved energy efficiency. A robust techno-economic assessment is therefore essential to evaluate and facilitate the adoption of such…
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We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
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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…