High Performance Computing Techniques Research Articles

Ion-molecule collision cross-section calculations using trajectory parallelization in distributed systems

Ion Mobility coupled with Mass Spectrometry (IM-MS) stands as a strong analytical method for structurally characterizing complex molecules. In IM-MS, the sample under investigation is ionized and propelled by an electric field into a drift tube, which collides against a buffer gas. The separation of the ion gas phase is then measured through the differences in their rotationally averaged Collision Cross-Section (CCS) values. The effectiveness of the measured Collision Cross-Section (CCS) for structural characterization critically depends on the validation against theoretical calculations. This validation process relies on intensive molecular mechanics simulations, which can be computationally demanding, especially for large systems such as molecular assemblies and viruses. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS experimental data. This work presents the MassCCS software, which considerably increases the CCS simulation performance by implementing a linked-cell-based algorithm, incorporating High-Performance Computing (HPC) techniques. We performed extensive tests regarding the system size, shape, and number of CPU cores. Experimental results reveal speedups up to 3 orders of magnitude faster than Collision Simulator for Ion Mobility Spectrometry (CoSIMS) and High-Performance Collision Cross Section (HPCCS), optimized solutions for CCS simulations, for a single node execution. In addition, we extended MassCCS at the inter-node level by employing OpenMP Cluster (OMPC). OMPC is an innovative programming model designed for the development of HPC applications. It streamlines the development process and simplifies software maintenance using only OpenMP directives. Notably, OMPC delivers a performance level comparable to a pure MPI implementation. This enhancement enabled expensive CCS calculations using nitrogen buffer gas for large systems such as human adenovirus with ∼11 million atoms in just ∼4 min, making MassCCS the most performant software nowadays, to the best of our knowledge. MassCCS is available as free software for Academic use at https://github.com/cces-cepid/massccs.

Advanced Framework for Multi-Modal Healthcare Data Integration: Leveraging HPC with GPU Computing and CNN Architecture in CDSS

In this study, we shall be looking at the challenges involved in integrating multi-modal healthcare data in the clinical decision support systems (CDSS). We propose the Automated Multi-Modal Data Integration (AMMI-CDSS) algorithm, which will utilize the latest high-performance computing (HPC) techniques such as the Convolutional Neural Network (CNN) architecture and the Graphics Processing Unit (GPU) computing to provide precise and rapid analysis. Which features will be extracted, multi-modal data will be merged, data will be prepared and algorithms developed in a distributed computing environment. We illustrate how AMMI-CDSS through the use of real world datasets such as wearable sensors data, medical imaging, genetic data, and electronic health records (EHRs), can improve the clinical decision support. By performing harmonization of the diverse data sources into a unique dataset after thorough data preprocessing and complex calculations, AMMI-CDSS provides the analysis with better quality and coherence.
 Our study allow us to make conclusion about how HPC-based CDSS models can be compared to conventional machine learning ones using their scalability and performance as key metrics. We enrich CDSS with the methodical framework for one-by-one testing and evaluation of proposed models and multi-modal healthcare data analysis. Future research might explore novel methods for integrating diverse types of healthcare data, as well as enhancing the HPC-based CDSS models by keeping them up-to-date.

Unveiling the potential of recently FDA-approved drugs as quorum sensing inhibitors against P. Aeruginosa using high-performance computational techniques

Through cell-to-cell communication, activation of efflux pumps, formation of biofilms, and other mechanisms, pseudomonas aeruginosa’s quorum sensing systems (QSS), notably the lasl/las-r system, contribute a vital role in the development of anti-microbial resistance (AMR). Identifying potential drugs against these targets could have significant implications for combating pseudomonal infections. The current study aims to identify promising recently FDA-approved drugs against lasl/las-r proteins. The ligands were selected from the FDA-approved drug lists of the last 5 years. Out of 202, 78 drugs were checked for interaction with lasl/las-r protein and 4 drugs revealed top binding conformations characterized by favorable energetic profiles within the active site of the las-r protein which were further assigned for 250-ns molecular dynamics (MD) simulation. The MD analysis confirmed the dynamical stability of brexanolone and oteseconazole with las-r protein. The root mean square deviation (RMSD), radius of gyration (Rg) and solvent-accessible surface area (SASA) analysis have indicated less deviation, more compactness of protein and less exposure of protein ligand complex to its surroundings as compared to the reference ligand-protein complex. The hydroxyl group in the oteseconazole whereas hydroxyl and ketone group in the brexanolone were responsible for hydrogen bonds with the active site residue of las r ptotein as indicated by ligand-protein contacts diagram. The binding energies per residue analysis revealed TYR-47 as the most contributing amino acid residue for interaction with oteseconazole and brexanolone. The identified drugs may be potential repurposing candidates against pseudomonal infections through inhibition of las-r protein. Communicated by Ramaswamy H. Sarma

Abstract C153: Rare BRAF mutations in menlanoma and beyond: Rationalizing the afficacy of B-raf inhibitors via HPC-based in silico/in vitro investigations

Abstract Melanoma is a highly aggressive type of cancer that affects the integumentary system and for which a family history of the disease, a fair complexion, the presence of a large number of moles, and skin exposure to natural/artificial ultraviolet (UV) radiation constitute established risk factors for malignancy development. The v-RAF murine sarcoma viral oncogene homolog B1 (BRAF) is a serine/threonine protein kinase that plays a critical role in the RAS-RAF-MEK-ERK mitogen–activated protein kinase (MAPK) cell signaling pathway. Despite over 20 years of research into the regulation and function of the RAF proteins, it was only relatively recently that the BRAF isoform is mutated at a high frequency in different human cancers, identifying this kinase as another important oncogene along this pathway. FFocusing on melanoma, activating BRAF mutations were identified in 37%−53% of patients with the malignant form of this pathology, the most prevalent, missense kinase alteration being the replacement of the small, apolar residue valine (V) with the negatively-charged, phosphorylation mimicking glutamic acid (E) at position 600 (BRAFV600E) within the protein catalytic (i.e., ATP binding) site. It is interesting to note that BRAFV600E is not a UV-driven mutation; in fact, it may also be found in the molecular profile of atypical benign nevi; moreover, although BRAFV600E is the most frequent mutation (accounting for nearly 40% of all melanomas and in approximately 90% of BRAF-mutant melanomas), other more or less frequent activating variants in BRAF have also been reported, which include other aminoacidic substitutions at the same position (e.g., V600K/D/R), G469A/V/S, L597Q/R/S/V, A598V, and K601E/N/T, among others. Importantly, these mutations are also found in other cancers, e.g., thyroid cancer, colorectal cancer, and some brain malignancies including glioblastoma, pilocytic astrocytoma, and pediatric low-grade glioma. In this multidisciplinary work, we focused on some of these rare BRAF variants and studied their ability to interact with several, first-line FDA approved BRAF initbitors (BRAFis) from a structural, chemico-phisical and in vitro perspectives. To the purpose, we employed a combitation of in silico techniques, based on the use of high-performance computing (HPC), chemico-physical and spectroscopical techniques, and in vitro tests to verify the interactions between these mutated BRAF isoforms with the inhibitors. As a main point, we were able to determine the binding thermodinamics and kinetics of the selected BRAFis to the mutant BRAF proteins, and each BRAF/drug complex was characterized from a structural standpoint. All these information ultimately revelaed that some of the BRAF isoforms are able to effectively bind the corresponding BRAi, with consequent switch-off of the signaling patwhay, at least in vitro. The presented mutlidisciplinary protocol could be adoped in the future to predict the response of newly discovered BARF variants and/or variants of unknown significance towards current or newly-designed BRAFis. Citation Format: Sabrina Pricl, Erik Laurini, Domenico Marson, Gabriele Cavalieri. Rare BRAF mutations in menlanoma and beyond: Rationalizing the afficacy of B-raf inhibitors via HPC-based in silico/in vitro investigations [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C153.

Sensitivity of salt intrusion to estuary-scale changes: A systematic modelling study towards nature-based mitigation measures

Estuaries are among the most densely populated and heavily utilised regions in the world, where crucial functions – e.g., freshwater availability and water safety – strongly relate to the natural dynamics of the system. When developing nature-based solutions to safeguard these essential functions, a thorough understanding of estuarine dynamics is required. This study describes an elaborate sensitivity analysis on the salt intrusion length using an idealised estuary, which is parametrically designed using key estuary-scale parameters – e.g., river discharge and tidal flats – to cover a wide range of estuary classes. We were able to systematically investigate such a wide range of estuary classes due to the combination of (1) state-of-the-art hydrodynamic modelling software, (2) high performance computing, and (3) reduction and analysis techniques using machine learning. The results show that the extent of the estuarine salt intrusion length is largely determined by four estuarine features: (1) river discharge; (2) cross-sectional area (especially water depth); (3) tidal damping/amplification; and (4) tidal asymmetry. In general, the salt intrusion length shows clear correlations with (a combination of) estuary-scale parameters, which all put an upper limit on the salt intrusion length. These relations provide crucial insights for successful development of nature-based solutions to mitigate salt intrusion in estuarine environments.

Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach

One of the fundamental challenges in geophysics is the calculation of distribution models for physical properties in the subsurface that accurately reproduce the measurements obtained in the survey and are geologically plausible in the context of the study area. This is known as inverse modeling. Performing a 3D joint inversion of multimodal geophysical data is a computationally intensive task. Additionally, since it involves a modeling process, finding a solution that matches the desired characteristics requires iterative calculations, which can take days or even weeks to obtain final results. In this paper, we propose a robust numerical solution for 3D joint inversion of gravimetric and magnetic data with Gramian-based structural similarity and structural direction constraints using parallelization as a high-performance computing technique, which allows us to significantly reduce the total processing time based on the available Random-Access Memory (RAM) and Video Random-Access Memory (VRAM)and improve the efficiency of interpretation. The solution is implemented in the high-level programming languages Fortran and Compute Unified Device Architecture (CUDA) Fortran, capable of optimal resource management while being straightforward to implement. Through the analysis of performance and computational costs of serial, parallel, and hybrid implementations, we conclude that as the inversion domain expands, the processing speed could increase from 4× up to 100× times faster, rendering it particularly advantageous for applications in larger domains. We tested our algorithm with two synthetic data sets and field data, showing better results than standard separate inversion. The proposed method will be useful for joint geological and geophysical interpretation of gravimetric and magnetic data used in exploration geophysics for example minerals, ore, and petroleum search and prospecting. Its application will significantly increase the reliability of physical-geological models and accelerate the process of data processing.

Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions

This paper presents a comprehensive understanding of current digital twin (DT) implementations in the construction industry, along with providing an overview of technologies enabling the operation of DTs in the industry. To this end, 145 publications were identified using a systematic literature review. The results revealed eight key areas of DT implementation including (i) virtual design, (ii) project planning and management, (iii) asset management and maintenance, (iv) safety management, (v) energy efficiency and sustainability, (vi) quality control and management, (vii) supply chain management and logistics, and (viii) structural health monitoring. The findings demonstrate that DT technology has the capacity to revolutionise the construction industry across these areas, enabling optimised designs, improved collaboration, real-time monitoring, predictive maintenance, enhanced safety practices, energy performance optimisation, quality inspections, efficient supply chain management, and proactive maintenance. This study also identified several challenges that hinder the widespread implementation of DT in construction, including (i) data integration and interoperability, (ii) data accuracy and completeness, (iii) scalability and complexity, (iv) privacy and security, and (v) standards and governance. To address these challenges, this paper recommends prioritising standardised data formats, protocols, and APIs for seamless collaboration, exploring semantic data modelling and ontologies for data integration, implementing validation processes and robust data governance for accuracy and completeness, harnessing high-performance computing and advanced modelling techniques for scalability and complexity, establishing comprehensive data protection and access controls for privacy and security, and developing widely accepted standards and governance frameworks with industry-wide collaboration. By addressing these challenges, the construction industry can unlock the full potential of DT technology, thus enhancing safety, reliability, and efficiency in construction projects.

High-Performance Computing for Density Matrix Renormalization Group

Abstract: In the last decades, many algorithms have been developed to use high-performance computing (HPC) techniques to accelerate the density matrix renormalization group (DMRG) method, an effective method for solving large active space strong correlation problems. In this article, the previous DMRG parallelization algorithms at different levels of the parallelism are introduced. The heterogeneous computing acceleration methods and the mixed-precision implementation are also presented and discussed. This mini-review concludes with some summary and prospects for future works.

A CUDA Fortran GPU-parallelised hydrodynamic tool for high-resolution and long-term eco-hydraulic modelling

Eco-hydraulic models are wide extended tools to assess physical habitat suitability on aquatic environments. Currently, the application of these tools is limited to short river stretches and steady flow simulations. However, this limitation can be overcome with the application of a high-performance computing technique: graphics processing unit (GPU) computing. R-Iber is a GPU-based hydrodynamic code parallelised in CUDA Fortran that, with the integration of a biological module, performs as an eco-hydraulic numerical tool. R-Iber was validated and applied to real cases by using an optimised instream flow incremental methodology in long river reaches and long-term simulations. R-Iber reduces the computation time considerably, reaching speed-ups of two orders of magnitude compared to traditional computing. R-Iber allows for overcoming the current limitations of the eco-hydraulic tools with the simulation of high-resolution numerical models calculated in a reasonable computation timeframe, which provides a better representation of the hydrodynamics and the physical habitat.

G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era.

A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.

High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies. This article recounts the dawn of parallel computation in power system studies, providing a thorough history and paradigm background for the reader, leading to the most impactful recent contributions. The reviews are split into Central Processing Unit (CPU) based, Graphical Processing Unit (GPU) based, and Cloud-based studies and smart grid applications. The state-of-the-art is also discussed, highlighting the issue of standardization and the future of the field. The reviewed papers are predominantly focused on classical imperishable electrical system problems. This indicates the need for further research on parallel and HPC approaches applied to future smarter grid challenges, particularly to the integration of renewable energy into the smart grid.

Computing for power system operation and planning: Then, now, and the future

With the global trend of pursuing clean energy and decarbonization, power systems have been evolving in a fast pace that we have never seen in the history of electrification. This evolution makes the power system more dynamic and more distributed, with higher uncertainty. These new power system behaviors bring significant challenges in power system modeling and simulation as more data need to be analyzed for larger systems and more complex models to be solved in a shorter time period. The conventional computing approaches will not be sufficient for future power systems. This paper provides a historical review of computing for power system operation and planning, discusses technology advancements in high performance computing (HPC), and describes the drivers for employing HPC techniques. Some high performance computing application examples with different HPC techniques, including the latest quantum computing, are also presented to show how HPC techniques can help us be well prepared to meet the requirements of power system computing in a clean energy future.

Automated generation of High-Performance Computational Fluid Dynamics Codes

Domain-Specific Languages (DSLs) improve programmers productivity by decoupling problem descriptions from algorithmic implementations. However, DSLs for High-Performance Computing (HPC) have two additional critical requirements: performance and scalability. This paper presents the automated process of generating, from abstract mathematical specifications of Computational Fluid Dynamics (CFD) problems, optimised parallel codes that perform and scale as manually optimised ones. We consciously combine within Saiph, a DSL for solving CFD problems, low-level optimisations and parallelisation strategies, enabling high-performance single-core executions which effectively scale to multi-core and distributed environments. Our results demonstrate how high-level DSLs can offer competitive performance by transparently leveraging state-of-the-art HPC techniques.

Reduced Order Modeling of Deformable Tire-Soil Interaction With Proper Orthogonal Decomposition

AbstractIn this study, model order reduction of high-fidelity off-road mobility models is explored to address the computational intensity of nonlinear finite element deformable tire–soil interaction models. To this end, a model order reduction procedure for the tire–soil interaction model is developed with the proper orthogonal decomposition (POD), and it is integrated into the off-road mobility simulation framework, leveraging high-performance computing. The POD is, however, limited in that the modes are dependent on snapshot data collected during the running of a full order model, limiting the modes to being accurate only for the specific scenario from which they were collected. Due to this limitation, a method of mode adaptation through interpolation on a tangent space of the Grassmann manifold is investigated to allow modes to be predicted for cases in which a full order model has not been run. It is demonstrated by several numerical examples that the POD modes are effective at retaining predictive accuracy while reducing computational time. The results show that adapted POD modes are more capable of characterizing the behavior of the model than modes produced at a different value of the simulation parameter. The POD-based reduced order modeling approach is further extended to the full vehicle simulation on deformable terrain through the co-simulation coupling algorithm by leveraging the high-performance computing technique.

Applications of Machine Learning and High-Performance Computing in the Era of COVID-19

During the ongoing pandemic of the novel coronavirus disease 2019 (COVID-19), latest technologies such as artificial intelligence (AI), blockchain, learning paradigms (machine, deep, smart, few short, extreme learning, etc.), high-performance computing (HPC), Internet of Medical Things (IoMT), and Industry 4.0 have played a vital role. These technologies helped to contain the disease’s spread by predicting contaminated people/places, as well as forecasting future trends. In this article, we provide insights into the applications of machine learning (ML) and high-performance computing (HPC) in the era of COVID-19. We discuss the person-specific data that are being collected to lower the COVID-19 spread and highlight the remarkable opportunities it provides for knowledge extraction leveraging low-cost ML and HPC techniques. We demonstrate the role of ML and HPC in the context of the COVID-19 era with the successful implementation or proposition in three contexts: (i) ML and HPC use in the data life cycle, (ii) ML and HPC use in analytics on COVID-19 data, and (iii) the general-purpose applications of both techniques in COVID-19’s arena. In addition, we discuss the privacy and security issues and architecture of the prototype system to demonstrate the proposed research. Finally, we discuss the challenges of the available data and highlight the issues that hinder the applicability of ML and HPC solutions on it.

Fuzzy Rough Set Theory (FRST) Classifier for Pest Prediction in India

Total cotton production is affected by an important factor called cotton pests occurrence. During the growth of cotton, environmental factors having great dependence especially during climate change. In multi-disciplinary agri-technologies domain, for data intensive science, for creating new opportunities, machine learning with big data technologies is used as a high-performance computing technique and good results are produced using this. However, running time and accuracy are highly important task. Affected crop pests are only predicted using available machine learning techniques. But there wont be any accurate level of pest affection by cotton. In this paper, proposed a Fuzzy Rough Set Theory (FRST) for handling this pest prediction level. There are four major steps in this proposed work, namely, results evaluation, prediction, pre-processing and dataset collection. From 2010-2020, from All India Coordinated Research Project (AICRP) collected the dataset in the first stage. Irrelevant features are removed by performing pre-processing in the second stage and for prediction, needed features only considered. However, for prediction, weather parameters like Rainfall (RF), Relative Humidity-Evening(RH-E), Relative Humidity-Morning(RH-M), Minimum Temperature(Min-Temp) and Maximum Temperature(Max-Temp) are used. For pest prediction, only these weather parameters are used and from original dataset removed the remaining parameters. This produces a pre-processed dataset. For pest prediction, proposed a Fuzzy Rough Set Theory (FRST) classifier in third step. Cotton pest pre-processed dataset is used for training FRST. Activation corresponds to high approximation and lower approximation functions. Weather factors belonging to higher cotton pests are predicted in this result. For future pest, FRST network can be used as a better predictor according to pest records. Moreover, better performance is shown by proposed FRST network, when compared with traditional machine learning techniques like Random Forest (RF) and Multi-Layer Perceptron (MLP). At last, these pest prediction techniques effectiveness is measured using metrics like F-measure, Area Under the Curve (AUC), and Accuracy (ACC).

Full-scale simulation of self-propulsion for a free-running submarine

It is important to predict the self-propulsion performance of full-scale marine vessels during the design stage. With the development of high-performance computational techniques, full-scale ship-free running simulations focused on self-propulsion performance are receiving increased attention. This study presents the results of computational fluid dynamics (CFD) simulations for a full-scale submarine propelled by a high-skew propeller. An in-house CFD code with the dynamic overset grid approach is used to simulate the rotational motion of the propeller. First, model- and full-scale simulations focused on submarine resistance and propeller open-water performance are conducted, enabling a systematic convergence study of the model. The self-propulsion performance is then predicted at the model scale, and comparisons with other available results show only small discrepancies. Finally, full-scale submarine self-propulsion simulations are conducted and the results are compared with those from the model-scale simulations with the addition of skin friction correction. Discussions on the differences between model- and full-scale self-propulsion results are presented including propeller performance, pressure distribution, boundary layer, and wake flow.

Commercial hypervisor-based task sandboxing mechanisms are unsecured? But we can fix it!

Cyber–Physical–Social Systems are frequently prescribed for providing valuable information on personalized services. The foundation of these services is big data which must be trustily collected and efficiently processed. Though High Performance Computing and Communication technique makes great contributions to addressing the issue of data processing, its effectiveness still relies on the veracity of data generated from Internet of Things (IoT) devices. Nevertheless, IoT devices, as basic production facilities to ensure data’s security, are unable to deploy expensive security extensions. Consequently, it causes the implementation of the task sandboxing, the fundamental security mechanism in Real-Time Operating Systems (RTOSs), much simpler and more vulnerable. In this paper, we take ARM Mbed uVisor as an example system, utilizing hypervisor-based task sandboxing mechanisms, and presents three new findings: First, we discover vulnerabilities against Mbed task sandboxing, which can be exploited to compromise system-maintained data structure to manipulate any tasks’ data. Second, we present LIPS (Lightweight Intra-Mode Privilege Separation), building a special protection domain to isolate particular system-maintained data structures. Finally, thorough evaluation and experimental tests show the efficiency of LIPS to defeat these attacks, with small runtime overheads and good portability.

Guiding Conventional Protein-Ligand Docking Software with Convolutional Neural Networks.

The high-performance computational techniques have brought significant benefits for drug discovery efforts in recent decades. One of the most challenging problems in drug discovery is the protein-ligand binding pose prediction. To predict the most stable structure of the complex, the performance of conventional structure-based molecular docking methods heavily depends on the accuracy of scoring or energy functions (as an approximation of affinity) for each pose of the protein-ligand docking complex to effectively guide the search in an exponentially large solution space. However, due to the heterogeneity of molecular structures, the existing scoring calculation methods are either tailored to a particular data set or fail to exhibit high accuracy. In this paper, we propose a convolutional neural network (CNN)-based model that learns to predict the stability factor of the protein-ligand complex and exhibits the ability of CNNs to improve the existing docking software. Evaluated results on PDBbind data set indicate that our approach reduces the execution time of the traditional docking-based method while improving the accuracy. Our code, experiment scripts, and pretrained models are available at https://github.com/j9650/MedusaNet.

Spark implementation of the enhanced Scatter Search metaheuristic: Methodology and assessment

Abstract Optimization problems arise nowadays in all disciplines, not only in the scientific area but also in the field of engineering or economics, and in many others. Currently, challenging optimization problems require solution methods that consume a significant amount of computational resources. The application of High-Performance Computing techniques is a common approach to obtain efficient implementations in traditional parallel computing systems. However, more recent approaches are exploring distributed programming frameworks developed in recent years to achieve efficient computations on clusters and cloud systems. In this paper we present a parallel implementation of the enhanced Scatter Search metaheuristic using Spark. The parallel program was obtained as a particularization of a general software framework we developed to support different realisations of the Scatter Search metaheuristic. The aim of this paper is to provide helpful guidance to readers interested in applying, or developing their own, parallel metaheuristics to solve challenging problems in the Cloud. With the twofold objective of demonstrating the potential of the parallelization with Spark and also of studying the factors that influence the performance of the solution, the proposal has been thoroughly evaluated on two different platforms, a cluster and a cloud platform, using a representative set of parameter estimation problems in the field of Computational Systems Biology.