Heterogeneous High Performance Computing Research Articles

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

Unified Programming Models for Heterogeneous High-Performance Computers

Unified Programming Models for Heterogeneous High-Performance Computers

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, fgSpMSpV adopts 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, fgSpMSpV utilizes 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, fgSpMSpV uses several optimization techniques to further utilize the computing resources. fgSpMSpV on the Sunway TaihuLight gains a noticeable performance improvement from the key optimization techniques with various sparsity of the input. Additionally, fgSpMSpV is implemented on an NVIDIA Tesal P100 GPU and applied to the breath-first-search (BFS) application. fgSpMSpV on a P100 GPU obtains the speedup of up to \( 134.38\times \) over the state-of-the-art SpMSpV algorithms, and the BFS application using fgSpMSpV achieves the speedup of up to \( 21.68\times \) over the state-of-the-arts.

An Optimal GeoAI Workflow for Pan-Arctic Permafrost Feature Detection from High-Resolution Satellite Imagery

High-spatial-resolution satellite imagery enables transformational opportunities to observe, map, and document the micro-topographic transitions occurring in Arctic polygonal tundra at multiple spatial and temporal frequencies. Knowledge discovery through artificial intelligence, big imagery, and high-performance computing (HPC) resources is just starting to be realized in Arctic permafrost science. We have developed a novel high-performance image-analysis framework—Mapping Application for Arctic Permafrost Land Environment (MAPLE)—that enables the integration of operational-scale GeoAI capabilities into Arctic permafrost modeling. Interoperability across heterogeneous HPC systems and optimal usage of computational resources are key design goals of MAPLE. We systematically compared the performances of four different MAPLE workflow designs on two HPC systems. Our experimental results on resource utilization, total time to completion, and overhead of the candidate designs suggest that the design of an optimal workflow largely depends on the HPC system architecture and underlying service-unit accounting model.

HPCGCN: A Predictive Framework on High Performance Computing Cluster Log Data Using Graph Convolutional Networks.

This paper presents a novel use case of Graph Convolutional Network (GCN) learning representations for predictive data mining, specifically from user/task data in the domain of high-performance computing (HPC). It outlines an approach based on a coalesced data set: logs from the Slurm workload manager, joined with user experience survey data from computational cluster users. We introduce a new method of constructing a heterogeneous unweighted HPC graph consisting of multiple typed nodes after revealing the manifold relations between the nodes. The GCN structure used here supports two tasks: i) determining whether a job will complete or fail and ii) predicting memory and CPU requirements by training the GCN semi-supervised classification model and regression models on the generated graph. The graph is partitioned into partitions using graph clustering. We conducted classification and regression experiments using the proposed framework on our HPC log dataset and evaluated predictions by our trained models against baselines using test_score, F1-score, precision, recall for classification, and R1 score for regression, showing that our framework achieves significant improvements.

High-Performance Computing Probabilistic Fracture Mechanics Implementation for Gas Turbine Rotor Disks on Distributed Architectures Including Graphics Processing Units

Abstract We present an efficient Monte Carlo-based probabilistic fracture mechanics simulation implementation for heterogeneous high-performance computing (HPC) architectures including central processing units (CPUs) and graphics processing units (GPUs). The specific application focuses on large heavy-duty gas turbine rotor components for the energy sector. A reliable probabilistic risk quantification requires the simulation of millions to billions of Monte Carlo (MC) samples. We apply a modified Runge–Kutta algorithm in order to solve numerically the fatigue crack growth for this large number of cracks for varying initial crack sizes, locations, material, and service conditions. This compute intensive simulation has already been demonstrated to perform efficiently and scalable on parallel and distributed HPC architectures including hundreds of CPUs utilizing the message passing interface (MPI) paradigm. In this work, we go a step further and include GPUs in our parallelization strategy. We develop a load distribution scheme to share one or more GPUs on compute nodes distributed over a network. We detail technical challenges and solution strategies in performing the simulations on GPUs efficiently. We show that the key computation of the modified Runge–Kutta integration step speeds up over two orders of magnitude on a typical GPU compared to a single threaded CPU. This is supported by our use of GPU textures for efficient interpolation of multidimensional tables utilized in the implementation. We demonstrate weak and strong scaling of our GPU implementation, i.e., that we can efficiently utilize a large number of GPUs/CPUs in order to solve for more MC samples, or reduce the computational turn-around time, respectively. On seven different GPUs spanning four generations, the presented probabilistic fracture mechanics simulation tool ProbFM achieves a speed-up ranging from 16.4× to 47.4× compared to single threaded CPU implementation.

A Proposed Data Partitioning Approach on Heterogeneous HPC Platforms: Data Locality Perspective

We propose a new data partitioning approach to improve the performance of heterogeneous parallel applications in modern high-performance computing (HPC) systems. Existing approaches do not consider an important aspect that has a critical impact on the performance of parallel applications: the method of assigning partitions to each processor so as to minimize the communication cost and hence minimize data movement, which dominates energy and performance cost. Such an aspect for managing data locality is important for a large range of applications. Therefore, to achieve efficient data partitioning, we propose a method for distribution considering this aspect. Our algorithm seeks to minimize execution time by using two models. The first is a fine-grained computational model of heterogeneous processors, which is sufficiently adequate and accurate to guarantee efficient partitioning results that maximize utilization. The second is a communication model of heterogeneous processors to minimize data motion and hide communication overheads. The correctness of our algorithm was analyzed and validated. The complexity of our algorithm is approximately of order <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ( <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> × <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log</b> <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> + <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> × <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>2</b></sup> ), where s is problem size/steps (where steps is the step size between data points in the computational model of each processor), and p is the number of heterogeneous processors. The experiments were performed on AZIZ supercomputer using two types of applications: an application with no dependency between its partitions, i.e., matrix multiplication, and another one with high dependency between its partitions, i.e., the Jacobi method. The results show the efficiency of our algorithm in improving performance.

GPU Acceleration of the HemeLB Code for Lattice Boltzmann Simulations in Sparse Complex Geometries

We present an implementation and scaling analysis of a GPU-accelerated kernel for HemeLB, a high-performance Lattice Boltzmann code for sparse complex geometries. We describe the structure of the GPU implementation and we study the scalability of HemeLB on a GPU cluster under normal operating conditions and with real-world application cases. We investigate the effect of CUDA block size and GPU over-subscription on the single-GPU performance, and we present a strong-scaling analysis of multi-GPU parallel simulations using two different hardware models (P100 and V100) and a variety of large cerebral aneurysm geometries. We find that HemeLB-GPU achieves single-GPU speedups of $50\times$ (P100) and $100\times$ (V100) compared to a single CPU core, with good scalability up to 32 GPUs. We also discuss strategies to improve both the kernel performance as well as the scalability of HemeLB-GPU to a larger number of GPUs. The GPU implementation supports the LBGK collision kernel, boundary conditions for walls and inlets/outlets, and several lattice types (D3Q15, D3Q19, D3Q27), and it integrates seamlessly with the existing infrastructure in HemeLB for graph partitioning and parallelization via MPI. It is expected that the GPU implementation will enable users of the HemeLB code to make better utilization of heterogeneous high-performance computing systems for large-scale lattice Boltzmann simulations.

A novel data partitioning algorithm for dynamic energy optimization on heterogeneous high‐performance computing platforms

SummaryEnergy is one of the most important objectives for optimization on modern heterogeneous high‐performance computing (HPC) platforms. The tight integration of multicore CPUs with accelerators such as graphical processing units (GPUs) and Xeon Phi coprocessors in these platforms presents several challenges to the optimization of multithreaded data‐parallel applications for energy. In this work, the problem of optimization of data‐parallel applications on heterogeneous HPC platforms for dynamic energy through workload distribution is formulated. We propose a workload partitioning algorithm to solve this problem. It employs load‐imbalancing technique to determine the workload distribution minimizing the dynamic energy consumption of the parallel execution of an application. The inputs to the algorithm are discrete dynamic energy profiles of individual computing devices. The profiles are practically constructed using an approach that accurately models the energy consumption by execution of a hybrid scientific data‐parallel application on a heterogeneous platform containing different computing devices such as CPU, GPU, and Xeon Phi. The proposed algorithm is experimentally analyzed using two multithreaded data‐parallel applications, matrix multiplication and 2D fast Fourier transform. The load‐imbalanced solutions provided by the algorithm achieve significant dynamic energy reductions for the two applications (in average by 130% and 44%, respectively) compared with the load‐balanced solutions.

Comparative Performance Evaluation of Modern Heterogeneous High-Performance Computing Systems CPUs

The study presents a comparison of computing systems based on IBM POWER8, IBM POWER9, and Intel Xeon Platinum 8160 processors running parallel applications. Memory subsystem bandwidth was studied, parallel programming technologies were compared, and the operating modes and capabilities of simultaneous multithreading technology were analyzed. Performance analysis for the studied computing systems running parallel applications based on the OpenMP and MPI technologies was carried out by using the NAS Parallel Benchmarks. An assessment of the results obtained during experimental calculations led to the conclusion that IBM POWER8 and Intel Xeon Platinum 8160 systems have almost the same maximum memory bandwidth, but require a different number of threads for efficient utilization. The IBM POWER9 system has the highest maximum bandwidth, which can be attributed to the large number of memory channels per socket. Based on the results of numerical experiments, recommendations are given on how the hardware of a similar grade can be utilized to solve various scientific problems, including recommendations on optimal processor architecture choice for leveraging the operation of high-performance hybrid computing platforms.

Pre-exascale accelerated application development: The ORNL Summit experience

High-performance computing (HPC) increasingly relies on heterogeneous architectures to achieve higher performance. In the Oak Ridge Leadership Facility (OLCF), Oak Ridge, TN, USA, this trend continues as its latest supercomputer, Summit, entered production in early 2019. The combination of IBM POWER9 CPU and NVIDIA V100 GPU, along with a fast NVLink2 interconnect and other latest technologies, pushes system performance to a new height and breaks the exascale barrier by certain measures. Due to Summit's powerful GPUs and much higher GPU–CPU ratio, offloading to accelerators becomes a requirement for any application, which intends to effectively use the system. To facilitate navigating a complex landscape of competing heterogeneous architectures, a collection of applications from a wide spectrum of scientific domains is selected for early adoption on Summit. In this article, the experience and lessons learned are summarized, in the hope of providing useful guidance to address new programming challenges, such as scalability, performance portability, and software maintainability, for future application development efforts on heterogeneous HPC systems.

Zonal Flow Solver (ZFS): a highly efficient multi-physics simulation framework

Multi-physics simulations are at the heart of today's engineering applications. The trend is towards more realistic and detailed simulations, which demand highly resolved spatial and temporal scales of various physical mechanisms to solve engineering problems in a reasonable amount of time. As a consequence, numerical codes need to run efficiently on high-performance computers. Therefore, the framework Zonal Flow Solver (ZFS) featuring lattice-Boltzmann, finite-volume, discontinuous Galerkin, level set and Lagrange solvers has been developed. The solvers can be combined to simulate, e.g. quasi-incompressible and compressible flow, aeroacoustics, moving boundaries and particle dynamics. In this manuscript, the multi-physics implementation of the coupling mechanisms are presented. The parallelisation approach, the involved solvers and their scalability on state-of-the-art heterogeneous high-performance computers are discussed. Various multi-physics applications complement the discussion. The results show ZFS to be a highly efficient and flexible multi-purpose tool that can be used to solve varying classes of coupled problems.

A Methodology for Comparing the Reliability of GPU-Based and CPU-Based HPCs

Today, GPUs are widely used as coprocessors/accelerators in High-Performance Heterogeneous Computing due to their many advantages. However, many researches emphasize that GPUs are not as reliable as desired yet. Despite the fact that GPUs are more vulnerable to hardware errors than CPUs, the use of GPUs in HPCs is increasing more and more. Moreover, due to native reliability problems of GPUs, combining a great number of GPUs with CPUs can significantly increase HPCs’ failure rates. For this reason, analyzing the reliability characteristics of GPU-based HPCs has become a very important issue. Therefore, in this study we evaluate the reliability of GPU-based HPCs. For this purpose, we first examined field data analysis studies for GPU-based and CPU-based HPCs and identified factors that could increase systems failure/error rates. We then compared GPU-based HPCs with CPU-based HPCs in terms of reliability with the help of these factors in order to point out reliability challenges of GPU-based HPCs. Our primary goal is to present a study that can guide the researchers in this field by indicating the current state of GPU-based heterogeneous HPCs and requirements for the future, in terms of reliability. Our second goal is to offer a methodology to compare the reliability of GPU-based HPCs and CPU-based HPCs. To the best of our knowledge, this is the first survey study to compare the reliability of GPU-based and CPU-based HPCs in a systematic manner.

SimAS: A simulation‐assisted approach for the scheduling algorithm selection under perturbations

SummaryMany scientific applications consist of large and computationally intensive loops. Dynamic loop self‐scheduling (DLS) techniques are used to parallelize and to balance the load of such applications during execution. Load imbalance arises from variations in the loop iteration (or tasks) execution times, caused by problem, algorithmic, or systemic characteristics. Variations in systemic characteristics are referred to as perturbations. Our hypothesis is that no single DLS technique can achieve the absolute best performance under various perturbations on heterogeneous high‐performance computing (HPC) systems. Therefore, the selection of the most efficient DLS technique is critical to achieve the best application performance. The goal of this work is to solve the algorithm selection problem for the scheduling of computationally intensive applications under perturbations. Existing work only considers perturbations caused by variations in the delivered computational speed of the HPC systems. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. A simulation‐assisted scheduling algorithm selection (SimAS) approach is introduced herein as a novel control‐theoretic‐inspired approach to select DLS techniques dynamically that improve the performance of applications executing on heterogeneous HPC systems under perturbations. The present work examines the performance of seven applications on a heterogeneous HPC system under all the above system perturbations. SimAS is evaluated using native and simulative experiments. The performance results confirm the original hypothesis that motivates this work. The experimental evaluation shows that the SimAS‐based DLS selection identifies the most efficient technique and improves application performance in most cases.

Communication Profiling and Characterization of Deep-Learning Workloads on Clusters With High-Performance Interconnects

Heterogeneous high-performance computing systems with GPUs are equipped with high-performance interconnects like InfiniBand, Omni-Path, PCIe, and NVLink. However, little exists in the literature that captures the performance impact of these interconnects on distributed deep learning (DL). In this article, we choose Horovod, a distributed training middleware, to analyze and profile various DNN training workloads using TensorFlow and PyTorch in addition to standard MPI microbenchmarks. We use a wide variety of systems with CPUs like Intel Xeon and IBM POWER9, GPUs like Volta V100, and various interconnects to analyze the following metrics: 1) message-size with Horovod's tensor-fusion; 2) message-size without tensor-fusion; 3) number of MPI/NCCL calls; and 4) time taken by each MPI/NCCL call. We observed extreme performance variations for non-power-of-two message sizes on different platforms. To address this, we design a message-padding scheme for Horovod, illustrate significantly smoother allreduce latency profiles, and report cases where we observed improvement for end-to-end training.

Optimizing FHEW With Heterogeneous High-Performance Computing

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping; plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots; second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845; and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping; the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft’s simple encrypted arithmetic library (SEAL).

High performance parallel KMP algorithm on a heterogeneous architecture

String matching algorithm is widely used in many application areas such as bio-informatics, network intrusion detection, computer virus scan, among many others. KMP (Knuth-Morris-Pratt) algorithm is commonly used for its fast execution time compared with many other string matching algorithms when applied to large input texts. However, the performance of the KMP algorithm is limited when the input text size increases significantly beyond a certain limit. In this paper, we propose a high performance parallel KMP algorithm on the Heterogeneous High Performance Computing (HPC) architecture based on the general-purpose multicore microprocessor and the Graphic Processing Unit (GPU) using OpenCL as the GPGPU (General Purpose computing using Graphic Processing Unit) platform. The proposed parallel KMP algorithm mainly focuses on optimizing the CPU-GPU memory hierarchy by overlapping the data transfer between the CPU memory and the GPU memory with the string matching operations on the GPU. It also optimizes the allocation of the work-groups and the work-items, and places the pattern data and the Failure Table to the on-chip shared-memory of the GPU. The experimental results show that the optimized parallel KMP algorithm leads up to ~8 times faster execution time.

Scientific workflows applied to the coupling of a continuum (Elmer v8.3) and a discrete element (HiDEM v1.0) ice dynamic model

Abstract. Scientific computing applications involving complex simulations and data-intensive processing are often composed of multiple tasks forming a workflow of computing jobs. Scientific communities running such applications on computing resources often find it cumbersome to manage and monitor the execution of these tasks and their associated data. These workflow implementations usually add overhead by introducing unnecessary input/output (I/O) for coupling the models and can lead to sub-optimal CPU utilization. Furthermore, running these workflow implementations in different environments requires significant adaptation efforts, which can hinder the reproducibility of the underlying science. High-level scientific workflow management systems (WMS) can be used to automate and simplify complex task structures by providing tooling for the composition and execution of workflows – even across distributed and heterogeneous computing environments. The WMS approach allows users to focus on the underlying high-level workflow and avoid low-level pitfalls that would lead to non-optimal resource usage while still allowing the workflow to remain portable between different computing environments. As a case study, we apply the UNICORE workflow management system to enable the coupling of a glacier flow model and calving model which contain many tasks and dependencies, ranging from pre-processing and data management to repetitive executions in heterogeneous high-performance computing (HPC) resource environments. Using the UNICORE workflow management system, the composition, management, and execution of the glacier modelling workflow becomes easier with respect to usage, monitoring, maintenance, reusability, portability, and reproducibility in different environments and by different user groups. Last but not least, the workflow helps to speed the runs up by reducing model coupling I/O overhead and it optimizes CPU utilization by avoiding idle CPU cores and running the models in a distributed way on the HPC cluster that best fits the characteristics of each model.

Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes

In drug discovery, molecular docking is the task in charge of estimating the position of a molecule when interacting with the docking site. This task is usually used to perform screening of a large library of molecules, in the early phase of the process. Given the amount of candidate molecules and the complexity of the application, this task is usually performed using high-performance computing (HPC) platforms. In modern HPC systems, heterogeneous platforms provide a better throughput with respect to homogeneous platforms. In this work, we ported and optimized a molecular docking application to a heterogeneous system, with one or more GPU accelerators, leveraging a hybrid OpenMP and OpenACC approach. The target application focuses on the virtual screening phases in the drug discovery process, and it is based on geometric transformations of the target ligands. We prove that our approach has a better exploitation of the node compared to pure CPU/GPU data splitting approaches, reaching a throughput improvement up to 25% while considering the same computing node.

A study on the evaluation of HPC microservices in containerized environment

SummaryContainers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment.