With the transition to the digital economy, the demand for specialists with advanced knowledge Mining Big Data, including using high-performance computing systems, is growing. The International School of Information Technologies “Data Science”, hereinafter referred to as the IT School, was created on the initiative of the Joint Institute for Nuclear Research (JINR). Several universities participate in the School, namely, Dubna State University, Plekhanov Russian University of Economics (PRUE), Moscow State University, Saint-Petersburg State University, etc.
In this paper, we discuss the features, structure, and properties of high-performance computing platforms used in educational processes and research activities of the IT School at Dubna State University 0 and Plekhanov Russian University of Economics 0, as well as their use in educational programs.
The goal of the IT School is to train highly qualified IT specialists in the field of Data Science who will be able to use Big Data analytics to solve scientific and practical problems. The educational program aims to develop deep knowledge in mathematical statistics, machine learning, programming, methods, and technologies of data processing and analysis, understanding business requirements and industry problems. It is expected that the participating organization will have both local programs specific to the educational institution and generic classes that will be implemented through joint events such as schools, conferences, and workshops. The events will be attended not only by the IT School participants, but also by students from JINR Member States and other countries. The international status of the IT School is predicated on the international status of JINR, the plan to invite lecturers from among prominent scientists of JINR Member States and other cooperating organizations, e.g. CERN.
Dubna State University is one the main universities of the IT School, in which the educational program “Big Data analysis” has been held since 2019. Within the school, Dubna University specializes in training IT specialists for solving high-energy and nuclear physics problems, as well as developing computing infrastructures for such scientific megaprojects as NICA, PIC, LHC, FAIR, SKA, etc. The “HybriLIT” heterogeneous platform 0, which is part of the Multifunctional Information and Computing Complex (MICC) 0 of the Laboratory of Information Technologies (LIT) 0 of JINR, plays a crucial role in the educational process.
In 2014, the Laboratory of cloud technologies and Big Data analytics was created at Plekhanov University 0. Lectures on Big Data technologies have been given since the creation of the laboratory, and the educational program “Introduction to distributed computing and Big Data analytics” was elaborated on their basis. In September 2018, an inter-faculty group “Data Science” was created with the goal of teaching Big Data and machine learning methods with the perspective application to economic and social problems. For practical classes and student research, the resources of the universal high-performance platform at PRUE are used.
Heterogeneous resources at JINR function as part of the JINR MICC 0 and consist of the “HybriLIT” education and testing polygon and the Govorun supercomputer, which share a unified software and information environment 0. The supercomputer is used for problems requiring massively parallel computing in various fields of nuclear physics and high-energy physics. The structure of the HybriLIT platform is shown in Figure 1.
Training courses on parallel programming and hybrid computing technologies are held using the “HybriLIT” infrastructure. An ecosystem of machine learning/deep learning (ML/DL) and data analysis was deployed on “HybriLIT” for the research and development of ML/DL algorithms, mathematical models, and resource-intensive computing on CPUs and graphics accelerators 0. The platform has two components: a) component for resource-intensive massively parallel tasks of neural network learning on NVIDIA GPUs and b) model and algorithm development ecosystem based on JupyterHub 0, which is a multi-user platform for Jupyter Notebooks being an interactive web-based programming environment for different languages, including Python.
At present, the platform includes 10 computing nodes, containing NVIDIA graphics accelerators (Tesla K20, K40, K80), and Intel Xeon co-processors. The total computing performance of the cluster reaches 142 Tflops.
For effective utilization, a software and information environment, including a website, an Indico service, a GitLab service 0, etc., was deployed and is being actively developed.
The educational program of the IT School at Dubna State University was developed taking into account the JINR personnel requirements. The program includes the study of such subjects as “Mathematical Basis and Tools for Data Analysis”, “Technologies and Platforms for Distributed and Parallel Computing”, “Big Data Analytics” 0.
Within the subject of “Technologies and Platforms for Distributed and Parallel Computing” students study OpenMP, MPI, CUDA 0 and OpenCL 0 parallel programming technologies. In addition, hybrid computing is considered, such as the combined use of MPI and OpenMP, or MPI and CUDA within one problem. For organizing practical lessons, the “HybriLIT” platform is used, since it contains both computing resources and programming libraries to use these technologies.
“Mathematical Basis and Tools for Data Analysis” aims to familiarize students with computational and statistical methods and tools for solving a wide range of data analysis problems, as well as develop practical programming skills in languages mainly used for large-scale data analysis, such as Python, R, Scala, and C++. To facilitate the educational process, Python data analysis, machine learning, and deep learning libraries (namely, NumPy, SciPy, matplotlib, scikit-learn, pandas, TensorFlow) were installed on the platform. The lesson results are prepared in Jupyter Notebooks with Markdown markup and can be exported to PDF for review.
The purpose of the “Big Data Analytics” subject is to introduce students to modern tools and technologies of data analysis, such as Hadoop and Apache Spark, as part of the high-performance computing platform. In addition, participants get acquainted with version control and community development tools of GitLab, which is part of the “HybriLIT” platform, as well as cloud infrastructure technologies and Unix-like operating systems for constructing and managing computing platforms.
HybriLIT platform services (Indico, GitLab, etc.) form the basis of the most practical engagement of students for effective learning. This allows teaching the latest technologies and IT solutions that are not yet taught at most universities.
For solving economic and socially important problems, as well as for IT education, a universal platform that includes a high-performance heterogeneous subsystem, a cloud infrastructure, and a storage system, was built at Plekhanov University.
The cloud infrastructure is based on the Open Nebula 5.6 cloud platform with 1 control node, 3 nodes in the Ceph storage cluster used for image and data storage, and 4 virtualization host nodes. The HPC infrastructure was deployed in October 2018 and consists of the high-performance Dell PowerEdge C4140 server containing 2 Intel Xeon Gold 6130 processors, 4 NVIDIA Tesla V100 graphics accelerators and 256GB of RAM. For internal storage, it has 480GB of SSD. The estimated HPC power is 60 single-precision Tflops and 30 double-precision Tflops. The components of the PRUE HPC system are shown in Figure 2.
The system software includes the Centos 7.5 operating system and Intel MPI 0, OpenMPI 0
and NVIDIA CUDA parallel programming frameworks. C/C++ and Fortran compilers and a Python
development environment are deployed on the high-performance platform with the most relevant
parallel computing modules: braid, chainer, cntk, keras, numpy, pandas, scipy, tensorflow-gpu,
torch [
The HPC system at Plekhanov University is widely used by university scientific laboratories. For example, the Research Laboratory “Monetary System Studies and Financial Market Analysis” conducts research on the applicability of the Echo state of neural networks for forecasting (forecast of the trajectory of derivatives of stock prices by the ESN neural network) and studies of the option exchange segment for the most liquid assets of RTS and Si stock exchanges. The Research Laboratory “Applied Modeling” uses the HPS system for studies in the field of logistics (optimization of air transport loading), machine learning (training a neural network for detecting an aircraft in satellite images), factor-frame modeling (analysis of the mutual influence of factors in complex socioeconomic models with feedback).
In September 2018 at Plekhanov Russian University of Economics, an inter-faculty group “Data Science” was created in order to study Big Data and machine learning methods for the application in sociology and economics. The scientific laboratory “Cloud technologies and Big Data analytics” of PRUE elaborated a special educational program “Introduction to distributed computing and Big Data analytics”. In addition to Big Data, the program covers modern programming methods, distributed computing, basic data structures and algorithms, database theory and practice, etc. Practical lessons and student research are performed using resources of the high-performance platform.
Access to adequate tools and resources is vital for education in data science, Big Data analytics, and parallel programming. Creating a heterogeneous platform that combines hardware, software and necessary services provides an effective way to facilitate students’ training. The “HybriLIT” platform at JINR and the high-performance platform at PRUE met the requirements of the educational process of groups within the International School of Information Technologies “Data Science”. The experience gained provides a blueprint for deploying educational computing platforms in the future.
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