High Performance Computing Platforms Research Articles

Scalable Data Processing Platform for Earth Observation Data Repositories

Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

Framework for Performance Enhancement of MPI based Application on Cloud

Cloud technology is a major revolution that has happened in the computing era that has changed the way applications and resources are used. Elasticity is the key characteristic of the cloud, wherein the required number of resources are provided as a service with a pay-as-you-go principle. This reduces the huge cost involved in buying, installing, and maintaining the resources. Cloud computing, with its highly scalable resources, can be a good platform for High-Performance Computing (HPC). HPC application performance highly depends on the quantity of the resources, which makes the cloud a suitable candidate. But the HPC community is not very happy with cloud technology, and most of the users still think cloud technology is not suitable for HPC applications. But virtualization technology, which is the foundation of the cloud, degrades the performance of applications in the urge to improve utilization. The hypervisor layer and resource sharing by the virtual machines (VMs) hosted on the same node are the main reasons for performance degradation in the cloud. The majority of the HPC applications belong to the message passing (MPI) category, and for these applications, communication cost is the major stakeholder in deciding performance. If these applications are hosted on the cloud, it leads to further performance degradation as the process on a VM communicates through the virtual network interface, which in turn shares the network interface of the host machine with other VMs. MPI-based applications hosted on MPPs work in a bandwidth shared environment as multiple processes communicate over the same network. But in the cloud, as the number of VMs increases, per node bandwidth availability per communication reduces. To address the above issues, we have built a framework to enhance the performance of MPI-based HPC applications on VMs by considering proper VM placement strategy and resource reservation policies, with knowledge of resource availability and process communication patterns. A VM placement strategy for dynamic clustering of VMs with high priority for shared memory-based communication is proposed and tested. Results show that with a medium number of processes, there is an improvement of around 70% with our placement strategy for high data communicating processes. If there are fewer processes, and the single physical node can hold all the VMs, then the performance improvement is up to 500%.

射电脉冲星搜索的优化方法

The connection between astronomy and high-performance computing is becoming stronger with the development of innovative observing facilities such as the Square Kilometre Array (SKA) and the proposed innovative platform for big data and high-performance computing. Astronomical computation is characterized by a large data volume and massive parallelism, particularly for pulsar search, a leading scientific direction of the SKA. In this study, we present an approach for accelerating the pulsar search pipeline based on OpenMP and multiprocessing techniques. We propose a method for solving the load imbalance problem and have successfully installed the pipeline on x86 and ARM compute nodes on the China SKA Regional Centre prototype (CSRC-P). The performance evaluation from tests on the Murchison Widefield Array (MWA) VCS observations shows that our optimization method works well on the x86 and ARM nodes, improving the relative speedup by factors of 10.4–12.2 and 24.5–25.8, respectively, compared with the original single-thread approach. The ARM platform was 1.1–1.3 times faster than the x86 platform in the tested cases, showing its great potential for SKA data processing. This optimized pulsar search pipeline deployed on the CSRC-P will be used for the pulsar survey of the southern-sky MWA rapid two-meter program for various scientific goals, including pulsar timing arrays for gravitational wave detection.

Adaptive elasticity policies for staging-based in situ visualization

In situ processing aims to alleviate the growing gap between computation and I/O capabilities by performing data processing close to the data source. In situ processing is widely used to process data generated by multiple data sources, including observation data from edge devices or scientific observational facilities and the simulation data generated by scientific computation on a high-performance computing (HPC) platform. For a scientific workflow that is run on an HPC platform and composed of a simulation program and an in situ data analytics or visualization (abbreviated as ana/vis) task, there is an implicit assumption that the computing resources assigned to the workflow keep static during the workflow execution. However, with the converging trend between the HPC and cloud computing platform, running the in situ ana/vis task in an elastic way is promising to decrease its overhead and improve its resource utilization rate. Resource elasticity represents the ability to change resource configurations such as the number of computing nodes/processes during workflow execution. An elastic job may dynamically adjust resource configurations; it may use a few resources at the beginning and more resources toward the end of the job when interesting data appear. However, it is hard to predict a priori how many computing nodes/processes need to be added/removed during the workflow execution to adapt to changing workflow needs. How to efficiently guide elasticity operations, such as growing or shrinking the number of processes used for in situ analysis during workflow execution, is an open-ended research question. In this article, we present adaptive elasticity policies that adopt workflow runtime information collected during workflow execution to predict how to trigger the addition/removal of processes in order to minimize in situ processing overhead. Taking in situ visualization tasks as an example, we integrate the presented elasticity policies into a staging-based elastic workflow and evaluate its efficiency in multiple elasticity scenarios. Compared with the situation without elasticity or with a static elasticity policy that uses a fixed number of processes for each rescaling operation, the adaptive elasticity policy can save overhead in finding a proper resource configuration and improve resource utilization efficiency. One experiment illustrates that the adaptive elasticity policy saves 41% of core-hours compared with the situation without the resource elasticity.

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Towards operational phytoplankton recognition with automated high-throughput imaging, near-real-time data processing, and convolutional neural networks

Plankton communities form the basis of aquatic ecosystems and elucidating their role in increasingly important environmental issues is a persistent research question. Recent technological advances in automated microscopic imaging, together with cloud platforms for high-performance computing, have created possibilities for collecting and processing detailed high-frequency data on planktonic communities, opening new horizons for testing core hypotheses in aquatic ecosystems. Analyzing continuous streams of big data calls for development and deployment of novel computer vision and machine learning systems. The implementation of these analysis systems is not always straightforward with regards to operationality, and issues regarding data flows, computing and data treatment need to be considered. We created a data pipeline for automated near-real-time classification of phytoplankton during remote deployment of imaging flow cytometer (Imaging FlowCytobot, IFCB). Convolutional neural network (CNN) is used to classify continuous imaging data with probability thresholds used to filter out images not belonging to our existing classes. The automated data flow and classification system were used to monitor dominating species of filamentous cyanobacteria on the coast of Finland during summer 2021. We demonstrate that good phytoplankton recognition can be achieved with transfer learning utilizing a relatively shallow, publicly available, pre-trained CNN model and fine-tuning it with community-specific phytoplankton images (overall F1-score of 0.95 for test set of our labeled image data complemented with a 50% unclassifiable image portion). This enables both fast training and low computing resource requirements for model deployment making it easy to modify and applicable in wide range of situations. The system performed well when used to classify a natural phytoplankton community over different seasons (overall F1-score 0.82 for our evaluation data set). Furthermore, we address the key challenges of image classification for varying planktonic communities and analyze the practical implications of confused classes. We published our labeled image data set of Baltic Sea phytoplankton community for the training of image recognition models (~63000 images in 50 classes) to accelerate implementation of imaging systems for other brackish and freshwater communities. Our evaluation data set, 59 fully annotated samples of natural communities throughout an annual cycle, is also available for model testing purposes (~150000 images).

Translating High-Performance Computing Tools From Research to Practice: Experiences With the TAU Performance System

The field of high-performance computing (HPC) has always challenged the research community to design and develop performance observation technology (based on instrumentation, measurement, and analysis methods), keeping pace with the rapid and aggressive evolution of HPC systems’ hardware and software. While the scope of observational concerns is broad and complex, it is the HPC innovation flux that poses difficult translation issues, even for performance tools of limited functionality. Both the complexity of HPC performance observation and the HPC translational pressures have kept the performance tools community mostly research oriented, with only a few open source toolkits widely used. The TAU Performance System is a performance toolkit for HPC with more than 30 years of continuous research and development. This project at the University of Oregon has attempted to keep TAU at the forefront of performance observation capabilities, ported to the latest HPC platforms available, and supported by a dedicated core research team. This article briefly describes the project’s research work and the challenges encountered, with a particular emphasis on the translation process necessary to make TAU the leading performance technology it is today.

Network Interactive Application of High-Performance Computing Platform for Japanese Teaching

With the development of network technology, Japanese teaching is increasingly showing the characteristics of network, and information interaction is becoming more and more frequent. Interactive teaching is a new teaching mode under the network environment. However, traditional interactive teaching cannot meet the flexibility and dynamics of resource allocation in the network environment. In the daily Japanese teaching process, the contradiction between the students’ growing need for learning resources and the unbalanced and insufficient distribution of resources is becoming more and more acute. High-performance computing is a model of swarm computing, which can solve the contradiction between complex student needs and resource allocation. Based on this, this paper proposes a Japanese interactive teaching method based on a high-performance computing platform. Experiments show that the interactive Japanese teaching method based on HPC can effectively improve students’ Japanese performance. Among them, the scores of high-level students increased by 23.3%, and the proportion of middle-level students accounted for more than 70.9%.

Resilient Scheduling of Moldable Parallel Jobs to Cope With Silent Errors

We study the resilient scheduling of moldable parallel jobs on high-performance computing (HPC) platforms. Moldable jobs allow for choosing a processor allocation before execution, and their execution time obeys various speedup models. The objective is to minimize the overall completion time or the makespan, when jobs can fail due to silent errors and hence may need to be re-executed after each failure until successful completion. Our work generalizes the classical scheduling framework for failure-free jobs. To cope with silent errors, we introduce two resilient scheduling algorithms, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lpa-List</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Batch-List</small> , both of which use the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">List</small> strategy to schedule the jobs. Without knowing a priori how many times each job will fail, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lpa-List</small> relies on a local strategy to allocate processors to the jobs, while <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Batch-List</small> schedules the jobs in batches and allows only a restricted number of failures per job in each batch. We prove approximation ratios for the two algorithms under several prominent speedup models (e.g., roofline, communication, Amdahl, power, monotonic, and a mix model). An extensive set of simulations is conducted to evaluate different variants of the two algorithms, and the results show that they consistently outperform some baseline heuristics. Overall, our best algorithm is within a factor of 1.6 of a lower bound on average over the entire set of experiments, and within a factor of 4.2 in the worst case.

R-STDP Spiking Neural Network Architecture for Motion Control on a Changing Friction Joint Robotic Arm.

Neuromorphic computing is a recent class of brain-inspired high-performance computer platforms and algorithms involving biologically-inspired models adopting hardware implementation in integrated circuits. The neuromorphic computing applications have provoked the rise of highly connected neurons and synapses in analog circuit systems that can be used to solve today's challenging machine learning problems. In conjunction with biologically plausible learning rules, such as the Hebbian learning and memristive devices, biologically-inspired spiking neural networks are considered the next-generation neuromorphic hardware construction blocks that will enable the deployment of new analog in situ learning capable and energetic efficient brain-like devices. These features are envisioned for modern mobile robotic implementations, currently challenging to overcome the pervasive von Neumann computer architecture. This study proposes a new neural architecture using the spike-time-dependent plasticity learning method and step-forward encoding algorithm for a self tuning neural control of motion in a joint robotic arm subjected to dynamic modifications. Simulations were conducted to demonstrate the proposed neural architecture's feasibility as the network successfully compensates for changing dynamics at each simulation run.

Operational Data-Driven Intelligent Modelling and Visualization System for Real-World, On-Road Vehicle Emissions—A Case Study in Hangzhou City, China

On-road vehicle emissions play a crucial role in affecting air quality and human exposure, particularly in megacities. In the absence of comprehensive traffic monitoring networks with the general lack of intelligent transportation systems (ITSs) and big-data-driven, high-performance-computing (HPC) platforms, it remains challenging to constrain on-road vehicle emissions and capture their hotspots. Here, we established an intelligent modelling and visualization system driven by ITS traffic data for real-world, on-road vehicle emissions. Based on the HPC platform (named “City Brain”) and an agile Web Geographic Information System (WebGISs), this system can map real-time (hourly), hyperfine (10~1000 m) vehicle emissions (e.g., PM2.5, NOx, CO, and HC) and associated traffic states (e.g., vehicle-specific categories and traffic fluxes) over the Xiaoshan District in Hangzhou. Our results show sharp variations in on-road vehicle emissions on small scales, which even fluctuated up to 31.2 times within adjacent road links. Frequent and widespread emission hotspots were also exposed. Over custom spatiotemporal scopes, we virtually investigated and visualized the impacts of traffic control policies on the traffic states and on-road vehicle emissions. Such results have important implications for how traffic control policies should be optimized. Integrating this system with chemical transport models and air quality measurements would bridge the technical gap between air pollutant emissions, concentrations, and human exposure.

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows; and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project.

Adaptively Periodic I/O Scheduling for Concurrent HPC Applications

With the convergence of big data and HPC (high-performance computing), various machine learning applications and traditional large-scale simulations with a stochastically iterative I/O periodicity are running concurrently on HPC platforms, which poses more challenges on the scarcely shared I/O resources due to the ever-growing data transfer demand. Currently the existing heuristic online and periodic offline I/O scheduling methods for traditional HPC applications with a fixed I/O periodicity are not suitable for the applications with stochastically iterative I/O periodicities, which are required to schedule the concurrent I/Os from different applications under I/O congestion. In this work, we propose an adaptively periodic I/O scheduling (APIO) method that optimizes the system efficiency and application dilation by taking the stochastically iterative I/O periodicity of the applications into account. We first build a periodic offline scheduling method within a specified duration to capture the iterative nature. After that, APIO adjusts the bandwidth allocation to resist stochasticity based on the actual length of the computing phrase. In the case where the specified duration does not satisfy the actual running requirements, the period length will be extended to adapt to the actual duration. Theoretical analysis and extensive simulations demonstrate the efficiency of our proposed I/O scheduling method over the existing online approach.

FgSpMSpV: A Fine-grained Parallel SpMSpV Framework on HPC Platforms

Sparse matrix-sparse vector (SpMSpV) multiplication is one of the fundamental and important operations in many high-performance scientific and engineering applications. The inherent irregularity and poor data locality lead to two main challenges to scaling SpMSpV over high-performance computing (HPC) systems: (i) a large amount of redundant data limits the utilization of bandwidth and parallel resources; (ii) the irregular access pattern limits the exploitation of computing resources. This paper proposes a fine-grained parallel SpMSpV (fgSpMSpV) framework on Sunway TaihuLight supercomputer to alleviate the challenges for large-scale real-world applications. First,fgSpMSpVadopts an MPI\( + \)OpenMP\( +X \)parallelization model to exploit the multi-stage and hybrid parallelism of heterogeneous HPC architectures and accelerate both pre-/post-processing and main SpMSpV computation. Second,fgSpMSpVutilizes an adaptive parallel execution to reduce the pre-processing, adapt to the parallelism and memory hierarchy of the Sunway system, while still tame redundant and random memory accesses in SpMSpV, including a set of techniques like the fine-grained partitioner, re-collection method, and Compressed Sparse Column Vector (CSCV) matrix format. Third,fgSpMSpVuses several optimization techniques to further utilize the computing resources.fgSpMSpVon the Sunway TaihuLight gains a noticeable performance improvement from the key optimization techniques with various sparsity of the input. Additionally,fgSpMSpVis implemented on an NVIDIA Tesal P100 GPU and applied to the breath-first-search (BFS) application.fgSpMSpVon a P100 GPU obtains the speedup of up to\( 134.38\times \)over the state-of-the-art SpMSpV algorithms, and the BFS application usingfgSpMSpVachieves the speedup of up to\( 21.68\times \)over the state-of-the-arts.

Fast Jitter Tolerance Testing for High-Speed Serial Links in Post-Silicon Validation

Post-silicon electrical validation of high-speed input/output (HSIO) links is a critical process for product qualification schedules of high-performance computer platforms under current aggressive time-to-market commitments. Improvements in signaling methods, circuits, and process technologies have allowed HSIO data rates to scale well beyond 10 Gb/s. Noise and EM effects can create multiple signal integrity problems, which are aggravated by continuously faster bus technologies. The goal of post-silicon validation for HSIO links is to ensure design robustness of both receiver (Rx) and transmitter circuitry in real system environments. One of the most common ways to evaluate the performance of an HSIO link is to characterize the Rx jitter tolerance (JTOL) performance by measuring the bit error rate (BER) of the link under worst stressing conditions. However, JTOL testing is extremely time-consuming when executed at specification BER considering manufacturing process, voltage, and temperature test coverage. In order to significantly accelerate this process, we propose a novel approach for JTOL testing based on an efficient direct search optimization methodology. Our approach exploits the fast execution of a modified golden section search with a high BER, while overcoming the lack of correlation between different BERs by performing a downward linear search at the actual target BER until no errors are found. Our proposed methodology is validated in a realistic industrial server post-silicon validation platform for three different computer HSIO links: SATA, USB3, and PCIe3.

Development and verification of Geant4-based parallel computing Monte Carlo simulations for nuclear logging applications

Monte Carlo is one of the most common methods to solve radiation transport problems. However, due to its statistical nature, Monte Carlo is time-consuming when applied to large-scale problems. Therefore, parallel computing, a powerful tool in accelerating Monte Carlo calculations, has been receiving increasing attention along with new developments of computing hardware in recent years. This work documents the application of parallel computing in Geant4 simulations pertaining to nuclear logging tools. A user-specific interface is developed to enable Geant4 multithreading functionality for two nuclear logging models and the parallel Monte Carlo performance is evaluated on a designated HPC (high performance computing) platform. Upon the validation of the parallel-mode simulation, the performance of the Geant4-based simulation in multi-threaded mode (G4-MT) and with the G4-MPI native interface to MPI are compared to obtain the best combination of threads and processes on each node. Strong and weak scalabilities on multiple threads are then investigated by computing the speedup and the parallel efficiency of the simulation. The results show that combining multi-threaded and MPI-based execution can significantly reduce execution time in parallel mode. And the paper documents the improvement of Monte Carlo simulation efficiency by optimizing the configuration of computational resources for parallel execution while keeping close-to-linear acceleration.

Neuromorphic scaling advantages for energy-efficient random walk computations

Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities. Most NMC research, which aims to replicate the brain's computational structure and architecture in man-made hardware, has focused on artificial intelligence; however, less explored is whether this brain-inspired hardware can provide value beyond cognitive tasks. We demonstrate that high-degree parallelism and configurability of spiking neuromorphic architectures makes them well-suited to implement random walks via discrete time Markov chains. Such random walks are useful in Monte Carlo methods, which represent a fundamental computational tool for solving a wide range of numerical computing tasks. Additionally, we show how the mathematical basis for a probabilistic solution involving a class of stochastic differential equations can leverage those simulations to provide solutions for a range of broadly applicable computational tasks. Despite being in an early development stage, we find that NMC platforms, at a sufficient scale, can drastically reduce the energy demands of high-performance computing (HPC) platforms.

3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms

We present a routine for 3D magnetotelluric (MT) modeling based upon high-order edge finite element method (HEFEM), tailored and unstructured tetrahedral meshes, and high-performance computing (HPC). This implementation extends the PETGEM modeller capabilities, initially developed for active-source electromagnetic methods in frequency-domain. We assess the accuracy, robustness, and performance of the code using a set of reference models developed by the MT community in well-known reported workshops. The scale and geological properties of these 3D MT setups are challenging, making them ideal for addressing a rigorous validation. Our numerical assessment proves that this new algorithm can produce the expected solutions for arbitrarily 3D MT models. Also, our extensive experimental results reveal four main insights: (1) high-order discretizations in conjunction with tailored meshes can offer excellent accuracy; (2) a rigorous mesh design based on the skin-depth principle can be beneficial for the solution of the 3D MT problem in terms of numerical accuracy and run-time; (3) high-order polynomial basis functions achieve better speed-up and parallel efficiency ratios than low-order polynomial basis functions on cutting-edge HPC platforms; (4) a triple helix approach based on HEFEM, tailored meshes, and HPC can be extremely competitive for the solution of realistic and complex 3D MT models and geophysical electromagnetics in general.

Action: A New Metric for Evaluating the Energy Efficiency on High Performance Computing Platforms (ranked on Green500 List)

The need for new and more reliable metrics is always in demand. In this paper, a new metric is proposed for the evaluation of high performance computing platforms in conjunction with their energy consumption. The aim of the new metric is to reliably compare different HPC systems concerning their energy efficiency. The metric provides a mean to rank supercomputers of similar capabilities, avoiding the misleading results of metrics like performance-per-watt, currently used for ranking systems, as in the Green500 list, where systems with totally different sizes and capabilities are ranked consecutively. An example of this misuse for two adjacent systems in the Green500 list, is discussed. A comparative study for the energy efficiency of three high performance computing platforms, with different architectures, using the proposed metric is presented.

Object Detection in Driving Datasets Using a High-Performance Computing Platform: A Benchmark Study

Nowadays, machine learning methods are increasingly used in different parts of autonomous driving and driving assistance systems. Yet, data and computational requirements can be enormous with these methods. Thus, providing several datasets containing many and diverse cases for the target problem and sufficient hardware for training and application of ML methods are too critical for achieving accurate results when applying them. Hence, we present an object detection benchmark study implementing the knowledge graph-based data integration framework to meet the data requirements and run the implementation on a big data and high-performance computing (HPC) platform, namely the EVOLVE. We applied different object detection methods to widely known open datasets, and compared the results on three different hardware setups, including EVOLVE. We also performed a small-scale transfer learning experiment. The results show that EVOLVE allowed the exploitation of much bigger data leading to a more efficient application of the object detection models with the help of the knowledge graph-based data integration framework. EVOLVE significantly improved the execution times compared to running them on a local laptop and a virtual machine and provided the easy-to-use and ready-to-use means to store large datasets and apply different models with its hardware and software stack.