Supercomputer Research Articles

Draft genome dataset of Streptomyces griseoincarnatus strain R-35 isolated from tidal pool sediments.

The marine isolate, Streptomyces griseoincarnatus strain R-35, was isolated from marine sediments collected from the Glencairn Tidal Pool, Table Mountain National Park, Cape Town, South Africa. The genomic DNA was sequenced using the Ion Torrent GeneStudio™ S5 platform, and the de novo assembly was performed using the SPAdes assembler on the Centre for High Performance Computing (CHPC) Lengau Cluster located at the CSIR, Rosebank, South Africa. The draft genome assembly consisted of 722 contigs totaling 7,625,174 base pairs and a G+C% content of 72.2 mol%. Genome completeness and genome contamination were determined as 99.12% and 0.92%, respectively. Genome annotations performed using the Rapid Annotation with Subsystem Technology (RAST) and the Bacterial and Viral Bioinformatics Resource Centre (BV-BRC) determined the presence of 7996 coding sequences (CDS), 63 transfer RNAs (tRNAs), and six ribosomal RNAs (rRNAs). A total of 2570 hypothetical proteins were assigned, and 5246 proteins were assigned to function. The phylogenomic positioning of S. griseoincarnatus strain R-35 was determined using the Type Strain Genome Server (TYGS) and was found to be related to S. griseoincarnatus JCM 4381T, with a digital DNA-DNA hybridisation (dDDH) value of 84.1%, and an OrthoANIu value of 98.22%. The CARD RGI algorithm on Proksee predicted the presence of 6,107 antimicrobial resistance (AMR) features, 27 biosynthetic gene clusters (BGCs) were predicted using antiSMASH, while 189 carbohydrate-active enzymes (CAZymes) were predicted using dbCAN3. The raw genome sequencing data has been submitted to the National Center for Biotechnology (NCBI) under the BioProject ID PRJNA1129156 (BioSample ID Accession Number: SAMN42145163; Short Read Archive (SRA) Accession: SRR29633055; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1129156).

An efficient Radix-4 butterfly structure based on the complex binary number system and distributed arithmetic

Complex number arithmetic is pivotal in various applications, requiring the selection of an efficient multiplier for high-performance computations. Fast Fourier transform (FFT)-based multipliers are widely employed for computing complex number products, but their reliance on using dedicated multipliers and treating the real and imaginary parts as two entities significantly add to the cost and complexity of the system. Distributed arithmetic (DA) is a technique that replaces complex multiplications with a bit-level shift and addition mechanism. The complex binary number system (CBNS) utilizes binary arithmetic, which treats the real and imaginary parts as a single entity, which can simplify complex number arithmetic and computations. This paper introduces an approach integrating the CBNS with DA in a Radix-4 decimation in time FFT 8-bit and 16-bit butterfly structure. The proposed design significantly reduces arithmetic computations and eliminates dedicated multipliers, demonstrating a reduction in power consumption, area size, and lookup tables, as well as increasing overall clock performance compared to the conventional FFT architecture on Artix-7, Kintex-7, and Virtex-7 field-programmable gate array chips.

Advances in Artificial Intelligence and Computational Methods: Enhancing Modeling, Prediction, and Optimization

bstract: This area of artificial intelligence and computational methods has witnessed tremendous development, especially with the solution of highly complex problems across scientific disciplines. This review brings together the latest advancements in the integration of AI with traditional computational techniques, focusing on their applications in numerical simulations, system optimization, and predictive modeling. Key contributions include AI-augmented finite difference methods for solving partial differential equations, advancements in material and energy systems optimization, and innovative approaches to highperformance computing. Future directions emphasize scalability, interdisciplinary applications, and the integration of emerging technologies such as quantum computing and reconfigurable hardware.

Optimal design of experiments in the context of machine-learning inter-atomic potentials: improving the efficiency and transferability of kernel based methods

Abstract Data-driven machine learning (ML) models of atomistic interactions are often based on flexible and non-physical functions that can relate nuanced aspects of atomic arrangements to predictions of energies and forces. As a result, these potentials are only as good as the training data (usually the results of so-called ab initio simulations), and we need to ensure that we have enough information to make a model sufficiently accurate, reliable and transferable. The main challenge stems from the fact that descriptors of chemical environments are often sparse, high-dimensional objects without a well-defined continuous metric. Therefore, it is rather unlikely that any ad hoc method for selecting training examples will be indiscriminate, and it is easy to fall into the trap of confirmation bias, where the same narrow and biased sampling is used to generate training and test sets. We will show that an approach derived from classical concepts of statistical planning of experiments and optimal design can help to mitigate such problems at a relatively low computational cost. The key feature of the method we will investigate is that it allows us to assess the quality of the data without obtaining reference energies and forces—a so-called offline approach. In other words, we are focusing on an approach that is easy to implement and does not require sophisticated frameworks that involve automated access to high performance computing.

All-optical analog differential operation and information processing empowered by meta-devices

Abstract The burgeoning demand for high-performance computing, robust data processing, and rapid growth of big data necessitates the emergence of novel optical devices to efficiently execute demanding computational processes. The field of meta-devices, such as metamaterial or metasurface, has experienced unprecedented growth over the past two decades. By manipulating the amplitude, phase, polarization, and dispersion of light wavefronts in spatial, spectral, and temporal domains, viable solutions for the implementation of all-optical analog computation and information processing have been provided. In this review, we summarize the latest developments and emerging trends of computational meta-devices as innovative platforms for spatial optical analog differentiators and information processing. Based on the general concepts of spatial Fourier transform and Green’s function, we analyze the physical mechanisms of meta-devices in the application of amplitude differentiation, phase differentiation, and temporal differentiation and summarize their applications in image edge detection, image edge enhancement, and beam shaping. Finally, we explore the current challenges and potential solutions in optical analog differentiators and provide perspectives on future research directions and possible developments.

The Progress and Prospects of Data Capital for Zero-Shot Deep Brain–Computer Interfaces

The vigorous development of deep learning (DL) has been propelled by big data and high-performance computing. For brain–computer interfaces (BCIs) to benefit from DL in a reliable and scalable manner, the scale and quality of data are crucial. Special emphasis is placed on the zero-shot learning (ZSL) paradigm, which is essential for enhancing the flexibility and scalability of BCI systems. ZSL enables models to generalise from limited examples to new, unseen tasks, addressing data scarcity challenges and accelerating the development of robust, adaptable BCIs. Despite a growing number of BCI surveys in recent years, there is a notable gap in clearly presenting public data resources. This paper explores the fundamental data capital necessary for large-scale deep learning BCI (DBCI) models. Our key contributions include (1) a systematic review and comprehensive understanding of the current industrial landscape of DBCI datasets; (2) an in-depth analysis of research gaps and trends in DBCI devices, data and applications, offering insights into the progress and prospects for high-quality data foundation and developing large-scale DBCI models; (3) a focus on the paradigm shift brought by ZSL, which is pivotal for the technical potential and readiness of BCIs in the era of multimodal large AI models.

A Descriptive Study on Interconnection Networks for Parallel Computing and Algorithm Models in Parallel Computing

In parallel computing, Interconnection networks are very crucial for efficient communication among all processors within a similar system. Parallel computing has become a crucial topic in the concern of computer science and also it is revealed to be critical when researching in high performance. The evolution of computer architectures towards an improved number of nodes, where parallelism could be the approach to option for speeding up an algorithm within the last few decades. Efficient data transfer between processors is an essential component in any large scale parallel computation. Motivated by the growing interest in parallel computers, a significant amount of theoretical research has been devoted to the area of interconnection networks for parallel computers, most of it to the packet routing (or store-and-forward) model of communication. We survey some of the major developments in this field, and discuss several new alternative models of communication, In many large scale applications, communication time dominates the execution time of the whole parallel computation. Thus, the performance of a large scale parallel computer is highly correlated with the efficiency of its network and communication algorithm. The combination of processing units build a model of computation (circuits) has gained an essential place in the area of high performance computing (HPC) due to its configuration and considerable processing supremacy that is parallel, series, etc. The aim of the Presenting this paper is study on the idea of parallel computing and its programming models and also explore some theoretical and technical concepts which can be often needed to understand the Interconnection network. In particular, we show how this technology is new in assisting the field of computational physics, especially when the issue is data parallel. In the real-life example of parallel computing, there are two queues to get a ticket of anything; if two cashiers are giving tickets to 2 persons simultaneously, it helps to save time as well as reduce complexity

Intelligent energy pairing scheduler (InEPS) for heterogeneous HPC clusters

In recent years, energy consumption has become a limiting factor in the evolution of high-performance computing (HPC) clusters in terms of environmental concern and maintenance cost. The computing power of these clusters is increasing, together with the demands of the workloads they execute. A key component in HPC systems is the workload manager, whose operation has a substantial impact on the performance and energy consumption of the clusters. Recent research has employed machine learning techniques to optimise the operation of this component. However, these attempts have focused on homogeneous clusters where all the cores are pooled together and considered equal, disregarding the fact that they are contained in nodes and that they can have different performances. This work presents an intelligent job scheduler based on deep reinforcement learning that focuses on reducing energy consumption of heterogeneous HPC clusters. To this aim it leverages information provided by the users as well as the power consumption specifications of the compute resources of the cluster. The scheduler is evaluated against a set of heuristic algorithms showing that it has potential to give similar results, even in the face of the extra complexity of the heterogeneous cluster.

Economic value of HPC experience for new STEM professionals: Insights from STEM hiring managers

PurposeThe purpose of this article is to investigate particular aspects of the STEM job market in the US. In particular, we ask: could the possession of high performance computing (HPC) skills enhance the chances of a person getting a job and/or increase starting salaries for people receiving an undergraduate or graduate degree and entering the technical workforce (rather than academia)? We also estimate the value to the US economy of practical experience offered to US students through training about HPC and the opportunity to use HPC systems funded by the National Science Foundation (NSF) and accessible nationally.MethodsInterviews and surveys of employers of graduates in STEM fields were used to gauge demand for STEM graduates with practical HPC experience and the salary increase that can be associated with the possession of such skills. We used data from the XSEDE project to determine how many undergraduate and graduate students it enabled to acquire practical proficiency with HPC.ResultsPeople with such skills who had completed an undergraduate or graduate degree received an initial median hiring salary of approximately 7%–15% more than those with the same degrees who did not possess such skills. XSEDE added approximately $10 million or more per year to the US economy through the practical educational opportunities it offered.DiscussionPractical hands-on experience provided by the US federal government, as well as many universities and colleges in the US, holds value for students as they enter the workforce.ConclusionPractical training in HPC during the course of undergraduate and graduate programs has the potential to produce positive individual labor market outcomes (i.e., salary boosts, signing bonuses) as well as to help address the shortage of STEM workers in the private sector of the US.

Transformer models for quantum gate set tomography

Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces Ml4Qgst as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for 2 and 3 gates on single-qubit and two-qubit systems, with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing.

ScRICA: An R package for multiple-sample single-cell RNA-seq data integrative comparative analysis

Single-cell sequencing technologies offer unprecedented resolution to inspect transcriptomes and generate critical biological insights. As the number of cells and cell types increase in single-cell studies, the effort required to analyze the data surges dramatically, especially when comparative explorations need to be performed on large datasets with different cell types and various sample attributes, such as clinical samples from different age and ancestry groups. Due to the sequential nature of single-cell data analysis, many steps involving multiple method choices and parameter options need to be considered. The computational skills required for integrative and comparative analyses of large datasets with various sample attributes represent a substantial obstacle for many researchers. To address this challenge, we have developed scRICA, a systematic workflow tailored for integrative and comparative single-cell RNA sequencing (scRNA-seq) analysis. This approach streamlines the analytical process, ensuring efficient utilization of computational resources and facilitating scalability for large-scale datasets. With scRICA, researchers can conduct integrative and comparative scRNA-seq analyses with ease, empowering them to derive meaningful insights from their data in a timely manner. scRICA offers a versatile approach by allowing users to input various parameter options from a metadata table, which are inherited throughout the entire analysis workflow. This functionality greatly enhances the efficiency of programming for comparative analyses involving multiple sample attributes. As an R package, scRICA provides a user-friendly interface within the R environment, making it accessible to researchers familiar with R programming. Additionally, scRICA offers a command line execution option, allowing users to seamlessly integrate it into their computational pipelines or execute analyses on High-Performance Computing (HPC) systems. This combination of features ensures flexibility, ease of use, and scalability, making scRICA a valuable tool for comprehensive and efficient single-cell RNA sequencing analysis.

Advanced Custom Interconnects: A Paradigm Shift in High-Performance Computing Data Transfer Efficiency

Recent advancements in high-performance computing (HPC) systems have highlighted the critical role of custom interconnects in achieving optimal system performance and efficiency. This article comprehensively analyzes modern custom interconnect architectures, focusing on their capacity to enhance data transfer speeds while maintaining power efficiency. The article examines the integration mechanisms for heterogeneous computing elements, including CPUs, GPUs, and specialized accelerators, and evaluates their impact on system-wide performance. The article explores advanced routing algorithms and coherence protocols that enable seamless communication across multiple processing elements while ensuring data consistency. The findings demonstrate that custom interconnects significantly improve system throughput and reduce latency in large-scale HPC deployments. Furthermore, the article analyzes the implications of these advancements for cloud computing, artificial intelligence, and big data analytics applications. This article contributes to the understanding of next-generation computing infrastructure by highlighting the crucial relationship between interconnect design and overall system performance while addressing the escalating demands of modern computational workloads.

HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization.

Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment. The use of deep learning algorithms for predicting WSIs has opened up new avenues for advanced medical diagnostics. Additionally, stain normalization can reduce the color and intensity variations present in WSI from different hospitals. As a result, deep learning classification accuracy improves. However, WSI reading and color normalization are still largely performed by using CPUs, leading to sub-optimal performance. We proposed a High-Performance Heterogeneous Computing Toolkit for Tissue Image (HCTTI) that integrates multiple computer system-level optimizations and encompasses WSI reading, tile normalization, and tile saving. We explored the potential advantages and limitations of different WSI readers and color normalization techniques in WSI analysis and the performance of different tile serialization formats for saving tiles. We found that HCTTI is 7 faster than OpenSlide for reading WSIs, GPU implementation of the Macenko normalization algorithm is 9 faster than TIAToolbox implementation, and HDF5 is faster than png and Zarr for storing normalized images in both writing (13 acceleration compared to png) and reading (2 acceleration compared to png), Specifically, HDF5 provides superior performance in handling large, complex datasets due to its efficient chunking and compression capabilities, as well as its broad support for hierarchical data management, making it very suitable for workloads like deep learning training I/O pattern that involves randomly reading large amount of small files in each training epoch. We also achieved linear acceleration in our multi-node distributed GPU implementation. To our knowledge, HCTTI is the first comprehensive toolkit that comprises distributed WSI reading, normalization, and serialization. It is 13 speedup compared to TIAToolbox implementation for normalizing a single WSI. Our findings could help pave the way for more effective and efficient deep learning-based approaches to WSI analysis, with the potential to transform medical diagnosis and treatment for a wide range of conditions. The source code of HCTTI is available at https://github.com/wangbo00129/HCTTI .

Computational Approaches for Multitarget Drug Design in Alzheimer's Disease: A Comprehensive Review.

Alzheimer's disease (AD) is a chronic and progressive neurodegenerative brain disorder, primarily affecting the elderly. Its socio-economic impact and mortality rate are alarming, necessitating innovative approaches to drug discovery. Unlike single-target diseases, Alzheimer's multifactorial nature makes single-target approaches less effective. To address this challenge, researchers are turning to drug design strategies targeting multiple disease pathways simultaneously. This approach has led to the promising identification of dual or multiple-target inhibitors, offering new perspectives for improving disease management. Computer-Aided Drug Design (CADD) such as virtual screening, docking, QSAR, molecular dynamics, ADMET prediction, etc., are valuable tools for designing and identifying new multi target directed ligands (MTDLs). These methods enable efficient screening of extensive compound libraries and accurate prediction of pharmacokinetic profiles, optimizing development costs and time. Challenges such as model accuracy, simulation complexity, and data integration persist. Addressing these issues requires advances in in silico modeling, high-performance computing, and experimental validation. In this regard, this review highlights recent advances using various computational methods to screen and identify new candidate compounds containing different heterocyclic motifs that could serve as potential bases for designing ligands targeting multiple targets for Alzheimer's disease.

Monte Carlo Simulations in Nanomedicine: Advancing Cancer Imaging and Therapy.

Monte Carlo (MC) simulations have become important in advancing nanoparticle (NP)-based applications for cancer imaging and therapy. This review explores the critical role of MC simulations in modeling complex biological interactions, optimizing NP designs, and enhancing the precision of therapeutic and diagnostic strategies. Key findings highlight the ability of MC simulations to predict NP bio-distribution, radiation dosimetry, and treatment efficacy, providing a robust framework for addressing the stochastic nature of biological systems. Despite their contributions, MC simulations face challenges such as modeling biological complexity, computational demands, and the scarcity of reliable nanoscale data. However, emerging technologies, including hybrid modeling approaches, high-performance computing, and quantum simulation, are poised to overcome these limitations. Furthermore, novel advancements such as FLASH radiotherapy, multifunctional NPs, and patient-specific data integration are expanding the capabilities and clinical relevance of MC simulations. This topical review underscores the transformative potential of MC simulations in bridging fundamental research and clinical translation. By facilitating personalized nanomedicine and streamlining regulatory and clinical trial processes, MC simulations offer a pathway toward more effective, tailored, and accessible cancer treatments. The continued evolution of simulation techniques, driven by interdisciplinary collaboration and technological innovation, ensures that MC simulations will remain at the forefront of nanomedicine's progress.

Correction to: EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing

Correction to: EEG Signal Based Human Emotion Recognition Brain-computer Interface using Deep Learning and High-Performance Computing

FlowCron - Increasing access to HPC by wrapping Globus into a function-as-a-service

Despite significant investment in High-Performance Computing (HPC) clusters by funding councils, there are still many researchers whose workflows could not benefit from the computation speed that is provided by these clusters. Reducing barriers to entry for these researchers would accelerate their scientific throughput, since they will be able to respond to results in a timely fashion, improving either their protocols or correcting any problems that might have arisen. This improves the quality of science, and therefore the return on investment, in computationally-intensive areas such as Cryogenic Electron Microscopy (cryo-EM). This paper outlines a technique, FlowCron, for users to analyse their data on a HPC facility with minimal training, increasing accessibility. FlowCron transfers the responsibilities of installation and upkeep of data processing pipelines from users to HPC systems administrators, simplifies the set up of HPC pipelines, and makes pipelines as reliable as possible once set up. The work described here has software dependencies that are common to the majority of HPC clusters. We achieve this by linking Globus and cron to produce an open-source system that requires little administrative support but provides a very easy way of running an analysis on a HPC system. The user starts the analysis through the Globus website and, when started, the data will be encrypted, uploaded to the HPC, analysed, and returned to the originating machine, along with a record of the analysis.

Design of spike-timing-dependent plasticity synapses based on CoPt-SOT device and its application in all-spin spiking neural network

Spintronic could be used to simulate synapses or neurons due to its multistate storage characteristics. In this work, a reliable design of all-spin spiking neural networks (SNN) based on spin–orbit torque (SOT) devices has been proposed in A1 CoPt single layer. The CoPt-SOT devices exhibited field-free SOT switching, and the magnetization reversal mechanism was inferred to be a combination of domain nucleation and domain-wall propagation as observed through magneto-optical Kerr microscopy images. Moreover, the current-induced SOT switching process of the device exhibited stable multistate magnetic switching behavior, which can be controlled by varying the amplitude and pulse width of the current pulse. Meanwhile, the spike-timing-dependent plasticity (STDP) curve was inverted when the SOT switching polarity was reversed by different magnetic fields, and the change in anomalous Hall resistances (ΔRH) in the STDP curve was linearly related to the SOT switching ratio. In addition, at the zero magnetic field, we constructed an all-spin SNN using STDP synapses and leaky integrate-and-fire neurons of CoPt-SOT devices. The handwritten digits recognition rate of this all-spin SNN network was 89.9%. These results substantiate that the CoPt single layer represents a promising hardware solution for high-performance neuromorphic computing, with applicability in the domain of SNN.

EnrichRBP: an automated and interpretable computational platform for predicting and analyzing RNA-binding protein events.

Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA. EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins. The platform supports 70 deep learning algorithms, covering feature representation, selection, model training, comparison, optimization, and evaluation, all integrated within an automated pipeline. EnrichRBP is adept at providing comprehensive visualizations, enhancing model interpretability, and facilitating the discovery of functionally significant sequence regions crucial for RBP interactions. In addition, EnrichRBP supports base-level functional annotation tasks, offering explanations and graphical visualizations that confirm the reliability of the predicted RNA binding sites. Leveraging high-performance computing, EnrichRBP provides ultra-fast predictions ranging from seconds to hours, applicable to both pre-trained and custom model scenarios, thus proving its utility in real-world applications. Case studies highlight that EnrichRBP provides robust and interpretable predictions, demonstrating the power of deep learning in the functional analysis of RBP interactions. Finally, EnrichRBP aims to enhance the reproducibility of computational method analyses for RNA-binding protein sequences, as well as reduce the programming and hardware requirements for biologists, thereby offering meaningful functional insights. EnrichRBP is available at https://airbp.aibio-lab.com/. The source code is available at https://github.com/wangyb97/EnrichRBP, and detailed online documentation can be found at https://enrichrbp.readthedocs.io/en/latest/. Supplementary data are available at Bioinformatics online.

Preparing MPICH for exascale

The advent of exascale supercomputers heralds a new era of scientific discovery, yet it introduces significant architectural challenges that must be overcome for MPI applications to fully exploit its potential. Among these challenges is the adoption of heterogeneous architectures, particularly the integration of GPUs to accelerate computation. Additionally, the complexity of multithreaded programming models has also become a critical factor in achieving performance at scale. The efficient utilization of hardware acceleration for communication, provided by modern NICs, is also essential for achieving low latency and high throughput communication in such complex systems. In response to these challenges, the MPICH library, a high-performance and widely used Message Passing Interface (MPI) implementation, has undergone significant enhancements. This paper presents four major contributions that prepare MPICH for the exascale transition. First, we describe a lightweight communication stack that leverages the advanced features of modern NICs to maximize hardware acceleration. Second, our work showcases a highly scalable multithreaded communication model that addresses the complexities of concurrent environments. Third, we introduce GPU-aware communication capabilities that optimize data movement in GPU-integrated systems. Finally, we present a new datatype engine aimed at accelerating the use of MPI derived datatypes on GPUs. These improvements in the MPICH library not only address the immediate needs of exascale computing architectures but also set a foundation for exploiting future innovations in high-performance computing. By embracing these new designs and approaches, MPICH-derived libraries from HPE Cray and Intel were able to achieve real exascale performance on OLCF Frontier and ALCF Aurora respectively.