Area Of High Performance Computing Research Articles

Direct electromagnetic information processing with planar diffractive neural network.

Diffractive neural network in electromagnetic wave-driven system has attracted great attention due to its ultrahigh parallel computing capability and energy efficiency. However, recent neural networks based on the diffractive framework still face the bottlenecks of misalignment and relatively large size limiting their further applications. Here, we propose a planar diffractive neural network (pla-NN) with a highly integrated and conformal architecture to achieve direct signal processing in the microwave frequency. On the basis of printed circuit fabrication process, the misalignment could be effectively circumvented while enabling flexible extension for multiple conformal and stacking designs. We first conduct validation on the fashion-MNIST dataset and experimentally build up a system using the proposed network architecture for direct recognition of different geometry structures in the electromagnetic space. We envision that the presented architecture, once combined with the advanced dynamic maneuvering techniques and flexible topology, would exhibit unlimited potentials in the areas of high-performance computing, wireless sensing, and flexible wearable electronics.

Towards Effective Bulgarian Competence Centre in High Performance Computing – Service Portfolio and Competences

Three years ago a new European initiative started: establishing a network of Centres of Competence in HPC in the European countries through the project EuroCC (2020-2022) in its second phase EuroCC2 (2023-2025). The goal is to accelerate the improvement of national and thus European capabilities in the area of HPC+ technologies, where HPC+ means High Performance Computing (HPC) and HPC application in HPDA and AI. In order to form an effective National Competence Centre in HPC+ technologies, we performed extensive competence mapping and now devise an extensive service portfolio, open to users from academia, public administration and industry. In this paper we present our analysis of the situation in Bulgaria as well as our approaches to make NCC and effective focus point for those that can benefit from use of HPC+ technology in their research or business.

ABCD Analysis of Industries Using High-Performance Computing

Purpose: High-Performance Computing (HPC) is the technique of increasing the overall processing capacity of the system, and achieving high performance in solving large problems in science, engineering, or business. This research paper investigated the High-Performance Computing industry using exploratory research methodology and a listing of ABCD analysis is done. Along with this, the identification of various applications and grand challenges in HPC and various Software Tools required for industry-specific application areas of HPC is done. Design/Methodology/Approach: Qualitative exploratory research methodology to perform Industry Analysis of High-Performance Computing using ABCD Analysis. This research paper is written with the literature review of High-Performance Computing (HPC) industries and various software tools available to use in those industries. Findings/Results: Finding of this research paper is the identification of various grand challenges in HPC industries like healthcare and medical field, engineering and design, aerospace sector, urban planning, and infrastructure, finance, and business. It also lists different HPC software tools that can be used to solve these challenges, along with the findings of ABCD analysis of HPC industry. Originality/Value: Listing of ABCD Analysis (Advantages, Benefits, Constraints, and Disadvantages) of High-Performance Computing industry. Paper Type: Industry Analysis research paper

STUDY OF PARALLEL COMPUTING STRUCTURE AND CIRCUIT ANALYSIS FOR INTERCONNECTION NETWORK

Parallel computing has become a crucial topic in the concern of computer science and also it is revealed to be critical when researching in high performance. The evolution of computer architectures towards an improved number of nodes, where parallelism could be the approach to option for speeding up an algorithm within the last few decades. The combination of processing units build a model of computation (circuits) has gained an essential place in the area of high performance computing (HPC) due to its configuration and considerable processing supremacy that is parallel, series, etc…. In this paper, we study the idea of parallel computing, and its programming models and also explore some theoretical and technical concepts which can be often needed to understand the Interconnection network. In particular, we show how this technology is new in assisting the field of computational physics, especially when the issue is data parallel. In this paper firstly we convert the graphical model of perfect difference network into circuit diagram then convert circuit diagram into switching function, then simplify it and redraw the equivalent network.

On the HPC/HPDA/AI Competences in Bulgaria

EU member states and the European commission realized the importance of High Performance Computing (HPC) and its applications in High- Performance Data Analytics (HPDA) and Artificial Intelligence (AI) and provided substantial funding for buying hardware and developing the national competences in the area. The EuroCC project (https://www.eurocc-access.eu/) supported the establishment of National Competence Centres in HPC/HPDA/AI, which serve as a focus point for access to expertise and know-how in the area and foster the European collaboration in the area. Bulgaria tries to maintain a leading position in the area of HPC in the region and thus it is important to collect and analyse information about the available competences in the field, the demand from the research community and the private sector, and to map ways for future development. The paper gives an overview on the current competences in HPC, HPDA and AI in Bulgaria and provides information about the plans for future development.

Preface

It is over one year since COVID-19 broke out. The whole world is still struggling against the virulent pandemic COVID-19. It is still difficult to take international travel. Many conferences are held virtually, for the sake of protecting all the participants and conference staff from get infected by the virus. It is uncertain when the COVID-19 will end, so it remains unclear for postponement time, while many scholars and researchers wanted to attend this long-waited conference and have academic exchanges with their peers. Therefore, in order to actively respond the call of the government, and meet author’s request, 2021 2nd International Conference on Signal Processing and Computer Science (SPCS 2021), which was planned to be held in Qingdao, China from August 20 to 22, was changed to be held online through Zoom software. This approach not only avoids people gathering, but also meets their communication needs.The SPCS 2021 was organized by AEIC Academic Exchange Information Center. It dedicated to create a platform for academic communications between specialists and scholars in the fields of Signal Processing and Computer Science. The conference created a path to establish a research relation for the authors and listeners with opportunities for collaboration and networking among the universities and institutions for promoting research and developing technologies.The conference brings together about 90 well-known scholars in the field of Signal Processing and Computer Science at home and abroad. The reports were divided into keynote speeches, oral presentations, and poster presentations to share their latest research results and experiences in related research fields. In the first part, each keynote speakers were allocated 30 minutes to present their talks via Zoom. After the keynote talks, all participants joined in a WeChat communication group to discuss more about the talks and presentations.We were greatly honor to have invited seven distinguished experts as our keynote speakers. Prof. Roberto Montemanni, from University of Modena and Reggio Emilia, Italy was the first one to perform a thought-provoking speech: Artificial Intelligence Applications in Logistics. And then we had Assoc. Prof. Lingling An, from Xidian University, China. She presented an insightful speech: Enhancement Mechanism of Cerebellar Neural Network Dynamics. Prof. Xinsong Yang, from Sichuan University, China. He made a wonderful speech: Synchronization And Control for Differential Equations with Discontinuous State on the Right-Hand Sides. Prof. Xinhua Tang, from Southeast University, China. His research topics include GNSS receiver techniques, GNSS/INS integrated navigation system, Multi-source integration systems, Precision agriculture, Autonomous vehicles etc. Prof. Haibin Zhu, from Nipissing University, Canada. He delivered a speech: E-CARGO and Role-Based Collaboration. Assoc. Prof. Marcin Paprzycki, from Systems Research Institute, Polish Academy of Sciences, Poland. His original research interests were in the area of high performance computing / parallel computing / computational mathematics. Over time, they shifted towards intelligent systems, software agents and agent systems, and application of semantic technologies, among others. Our finale keynote speaker, Prof. Hoshang Kolivand, from Liverpool John Moores University, UK was invited to perform a speech: Current and Future of Directions of Augmented and Virtual Reality. Their insightful speeches had triggered heated discussion in the third session of the conference. The WeChat discussion lasted for about 30 minutes. Every participant praised this conference for disseminating useful and insightful knowledge.We are glad to share with you that we received lots of submissions from the conference and we selected a bunch of high-quality papers and compiled them into the proceedings after rigorously reviewed them. These papers feature following topics but are not limited to: Signal Processing, Applications of Signal Processing, Communication and Broadband Networks, Computational Science and other relevant directions. All the papers have been through rigorous review and process to meet the requirements of international publication standard.We would like to thank the organization staff, the members of the program committees and reviewers. They have worked very hard in reviewing papers and making valuable suggestions for the authors to improve their work. We also would like to express our gratitude to the external reviewers, for providing extra help in the review process, and the authors for contributing their research result to the conference.Lastly, we would like to warmly thank all the authors who, with their presentations and papers, generously contributed to the lively exchange of scientific information that is so vital to the endurance of scientific conferences of this kind.Committee of SPCS 2021List of titles Committee member are available in this Pdf.

Toward an AI-Enabled O-RAN-based and SDN/NFV-driven 5Ｇ& IoT Network Era

Artificial Intelligent Technology has impacted tremendously in the areas of high performance computing, and network and communicatons industries. The advantages of a system applying AI includes performance improvent, optimization, and intelligent or smart AnFor intelligent fesure of 5G, network slicing, provided by Network equipment vendor by applying AI, softwarization and virtualization technologies to the network. For many other industries and applications such as healthcare, agriculture, finance, have benefited from AI technology in particular machine learning and deep learning within AL.With the integration of AI, 5G, and Inernet of Thngs, the industrial applications, smart farms, precision medicine.,smart city. This article focuses on the System architecture and design of open networking solution of the future of 5G, beyond-5G (B5G) or 6G. Among the challenges of an ON system solution, the propriety of radio access network (RAN) is one of essential challenges. The Open-RAN Alliance is formed through the integration of C-RAN Alliance and X-RAN Forum. The O-RAN Alliance mission’s is converting the radio access network industry to become an open networking intelligent, virtualized, and fully interoperable RAN. To realize B5G or 6G by applying O-RAN architecture and ecosystem is called O-RAN based B5G/6G The Integration of O-RAN based 5G RAN part and the SDN/NFV-based softwarization and virtualization of Core Network, Transport Network and Management functions, we can derive a stage of fully Open Networking architecture for the software (AI/M/DL) developers to work.

DDF Library: Enabling functional programming in a task-based model

In recent years, the areas of High-Performance Computing (HPC) and massive data processing (also know as Big Data) have been in a convergence course, since they tend to be deployed on similar hardware. HPC systems have historically performed well in regular, matrix-based computations; on the other hand, Big Data problems have often excelled in fine-grained, data parallel workloads. While HPC programming is mostly task-based, like COMPSs, popular Big Data environments, like Spark, adopt the functional programming paradigm. A careful analysis shows that there are pros and cons to both approaches, and integrating them may yield interesting results. With that reasoning in mind, we have developed DDF, an API and library for COMPSs that allows developers to use Big Data techniques while using that HPC environment. DDF has a functional-based interface, similar to many Data Science tools, that allows us to use dynamic evaluation to adapt the task execution in run time. It brings some of the qualities of Big Data programming, making it easier for application domain experts to write Data Analysis jobs. In this article we discuss the API and evaluate the impact of the techniques used in its implementation that allow a more efficient COMPSs execution. In addition, we present a performance comparison with Spark in several application patterns. The results show that each technique significantly impacts the performance, allowing COMPSs to outperform Spark in many use cases.

Evaluation of Clusters based on Systems on a Chip for High-Performance Computing: A Review

High-performance computing systems are the maximum expression in the field of processing for large amounts of data. However, their energy consumption is an aspect of great importance, which was not considered decades ago. Hence, software developers and hardware providers are obligated to approach new challenges to address energy consumption, and costs. Constructing a computational cluster with a large amount of systems on a chip can result in a powerful, ecologic platform, with the capacity to offer sufficient performance for different applications, as long as low costs and minimum energy consumption can be maintained. As a result, energy efficient hardware has an opportunity to impact upon the area of high-performance computing. This article presents a systematic review of the evaluations conducted on clusters of ystems on a Chip for High-Performance computing in the research setting.

Integrated Cache Scheduling Replacement Algorithm to Reduce Cache Pollution

Cache memory management has recently become one of the most crucial research topics in the area of high performance computing. The cache memory has gained popularity due to its capability to offer faster access compared to main memory so processor can access data directly through cache very efficiently. As processor execution speed is faster than accessing memory so the goal is to keep programs rapidly accessible for processing. Cache memory helps not only to access data quickly but also improves the performance of the system. Various cache management policies have been developed to improve the performance of the system. In this paper we focus the issues related with the existing replacement policies and present a new algorithm to overcome these issues. In this paper, we proposed Integrated Cache Scheduling Replacement Algorithm (ICSRA) that will remove the object on the basis of integration of all the parameters like size, frequency and recency if the cache is full. This algorithm consider all size of object to replace with the condition of frequency and timestamp parameter to reduce the cache pollution [1]. Cache pollution means the cache unnecessary waste its space by holding such an object which is no longer be accessed. ICSRA helps to enhance the utilization of cache memory in an efficient manner as well as maximize the cache hit ratio to improve the performance of the system. A detailed classification has also been included based on different parameters which are depending on the analysis of the existing techniques.

The European Approach to the Exascale Challenge

An integrated European exascale computing and data infrastructure and ecosystem are key to building a vibrant data economy and enabling Europe to stay at the forefront of scientific discovery and industrial leadership. An ambitious legal and funding framework at the European level, the European High-Performance Computing (HPC) joint undertaking, has been agreed upon between EU countries and will be operational by the end of 2018. Its purpose is to stimulate investment in the area of HPC, with two objectives: making Europe one of the top three HPC powers in the world and acquiring two exascale systems by 2023, with at least one based on European technology. The joint undertaking will combine public and private investments to develop innovative European exascale hardware and software technology components, integrate them into prototype systems co-designed with extreme scale applications, and use public procurement at the European level to invest in operational HPC technologies.

Building a scientific workflow framework to enable real‐time machine learning and visualization

SummaryNowadays, we have entered the era of big data. In the area of high performance computing, large‐scale simulations can generate huge amounts of data with potentially critical information. However, these data are usually saved in intermediate files and are not instantly visible until advanced data analytics techniques are applied after reading all simulation data from persistent storages (eg, local disks or a parallel file system). This approach puts users in a situation where they spend long time on waiting for running simulations while not knowing the status of the running job. In this paper, we build a new computational framework to couple scientific simulations with multi‐step machine learning processes and in‐situ data visualizations. We also design a new scalable simulation‐time clustering algorithm to automatically detect fluid flow anomalies. This computational framework is built upon different software components and provides plug‐in data analysis and visualization functions over complex scientific workflows. With this advanced framework, users can monitor and get real‐time notifications of special patterns or anomalies from ongoing extreme‐scale turbulent flow simulations.

Preface to the Special Issue on: “27th International Conference on Parallel Computational Fluid Dynamics”

The international Conference on Parallel Computational Fluid Dynamics (CFD) is an annual event devoted to the discussion of the latest advances and challenges in the field of high-performance computing for fluid dynamics problems and related multidisciplinary applications. The 27th edition of the conference took place in May 2015 in Montreal, Canada, and was hosted by the CFD Laboratory of the McGill University. The Conference had seven plenary speakers, three parallel sessions and two mini symposia, entitled ‘Enabling Large-Scale Multi-physics Simulations’ and ‘CFD Applications on GPU and Many-Core Architecture’, respectively. A total of 88 research works were presented during the Conference, for a total of 110 participants from both Academia and Industry. Two-thirds of the presentations discussed themes in the area of parallel algorithms and software, GPU computing and peta-scale applications. The remaining one-third addressed the application of parallel solvers to problems in the areas of mechanical and biomedical engineering, turbulent flows, combustion and multi-physics problems. This Special Issue collects a selection of the works presented at the conference. The conference was successful in delivering high-quality talks in terms of scientific relevance and in proposing approaches to address the most modern and (possibly) future demands in the area of high-performance computing and computational engineering.

Performance Prediction for Large-Scale Parallel Applications Using Representative Replay

Automatically predicting performance of parallel applications has been a long-standing goal in the area of high performance computing. However, accurate performance prediction is challenging, since the execution time of parallel applications is determined by several factors, such as sequential computation time, communication time and their complex interactions. Despite previous efforts, accurately estimating the sequential computation time in each process for large-scale parallel applications remains an open problem. In this paper, we propose a novel approach to acquiring accurate sequential computation time using a parallel debugging technique called deterministic replay. The main advantage of our approach is that we only need a single node of a target platform but the whole target platform does not need to be available. Therefore, with this approach we can simply measure the real sequential computation time on a target node for each process on by one. Moreover, we observe that there is great computation similarity in parallel applications, not only within each process but also among different processes. Based on this observation, we further propose representative replay that can significantly reduce replay overhead, because we only need to replay partial iterations for representative processes instead of all of them. Finally, we implement a complete performance prediction system, called Phantom , which combines the above computation-time acquisition approach and a trace-driven simulator. We validate our approach on both traditional HPC platforms and the latest Amazon EC2 cloud platform. On both types of platforms, prediction error of our approach is less than 7 percent on average up to 2,500 processes.

A Grid-Enabled Infrastructure for Resource Sharing, E-Learning, Searching and Distributed Repository Among Universities

In the recent years, service-based approaches for sharing of data among repositories and online learning are rising to prominence because of their potential to meet the requirements in the area of high performance computing. Developing education based grid services and assuring high availability, reliability and scalability are demanding in web service architectures. On the other hand, grid computing provides flexibility towards aggregating distributed CPU, memory, storage, data and supports large number of distributed resource sharing to provide the full potential for education like applications to share the knowledge that can be attainable on any single system. However, the literature shows that the potential of grid resources for educational purposes is not being utilized yet. In this paper, an education based grid framework architecture that provides promising platform to support sharing of geographically dispersed learning content among universities is developed. It allows students, faculty and researchers to share and gain knowledge in their area of interest by using e-learning, searching and distributed repository services among universities from anywhere, anytime. Globus toolkit 5.2.5 (GTK) software is used as grid middleware that provides resource access, discovery and management, data movement, security, and so forth. Furthermore, this work uses the OGSA-DAI that provides database access and operations. The resulting infrastructure enables users to discover education services and interact with them using the grid portal.

AutoElastic: Automatic Resource Elasticity for High Performance Applications in the Cloud

Elasticity is undoubtedly one of the most striking characteristics of cloud computing. Especially in the area of high performance computing (HPC), elasticity can be used to execute irregular and CPU-intensive applications. However, the on- the-fly increase/decrease in resources is more widespread in Web systems, which have their own IaaS-level load balancer. Considering the HPC area, current approaches usually focus on batch jobs or assumptions such as previous knowledge of application phases, source code rewriting or the stop-reconfigure-and-go approach for elasticity. In this context, this article presents AutoElastic, a PaaS-level elasticity model for HPC in the cloud. Its differential approach consists of providing elasticity for high performance applications without user intervention or source code modification. The scientific contributions of AutoElastic are twofold: (i) an Aging-based approach to resource allocation and deallocation actions to avoid unnecessary virtual machine (VM) reconfigurations (thrashing) and (ii) asynchronism in creating and terminating VMs in such a way that the application does not need to wait for completing these procedures. The prototype evaluation using OpenNebula middleware showed performance gains of up to 26 percent in the execution time of an application with the AutoElastic manager. Moreover, we obtained low intrusiveness for AutoElastic when reconfigurations do not occur.

Моделирование отказов в высокопроизводительных вычислительных системах в рамках стандарта MPI и его расширения ULFM

In this paper, we consider one of the main problems that occur in the area of high-performance computing is to continue computations despite of failures. For the programs running on such systems it is very important to handle failures and continue computations on working nodes. One of the MPI 3.1 standardization efforts aim is adding new techniques, approaches, or concepts to support for fault tolerance in MPI applications. The paper briefly describes a library for simulation of failures and testing fault-tolerant algorithms using functional of developing MPI 3.1 standard. In the test problem we describe one of the techniques of fault tolerance and we compare checkpoint in operational memory versus checkpoint in the distributed file system.

Recent developments in high‐performance computing and simulation: distributed systems, architectures, algorithms, and applications

Recent developments in high‐performance computing and simulation: distributed systems, architectures, algorithms, and applications

Lattice Quantum Chromodynamics with Overlap Fermions on GPUs

Lattice quantum chromodynamics (QCD) calculations were one of the first applications to demonstrate the potential of GPUs in the area of high-performance computing; the nature of lattice QCD calculations matches well with the GPU's computational model. This article discusses ways to effectively use GPUs for lattice calculations using the overlap operator, a discretization that preserves chiral symmetry even at nonzero lattice spacing and makes possible lattice QCD simulations in the parameter region relevant to nuclear physics. The author shows that the large memory footprint of these codes requires the use of multiple GPUs in parallel and discusses methods for implementing this operator efficiently: hybrid CPU/GPU memory use for eigensolvers and MPI/OpenMP/CUDA parallelization strategies required to take full advantage of both GPU and CPU resources. He then compares the performance of codes on a GPU cluster and a CPU cluster with similar interconnects, discussing the strong scaling for problem sizes relevant to current lattice QCD simulations.

SPOT: A DSL for Extending Fortran Programs with Metaprogramming

Metaprogramming has shown much promise for improving the quality of software by offering programming language techniques to address issues of modularity, reusability, maintainability, and extensibility. Thus far, the power of metaprogramming has not been explored deeply in the area of high performance computing (HPC). There is a vast body of legacy code written in Fortran running throughout the HPC community. In order to facilitate software maintenance and evolution in HPC systems, we introduce a DSL that can be used to perform source-to-source translation of Fortran programs by providing a higher level of abstraction for specifying program transformations. The underlying transformations are actually carried out through a metaobject protocol (MOP) and a code generator is responsible for translating a SPOT program to the corresponding MOP code. The design focus of the framework is to automate program transformations through techniques of code generation, so that developers only need to specify desired transformations while being oblivious to the details about how the transformations are performed. The paper provides a general motivation for the approach and explains its design and implementation. In addition, this paper presents case studies that illustrate the potential of our approach to improve code modularity, maintainability, and productivity.