Modern Parallel Research Articles

High-order space–time parallel computing of the Navier–Stokes equations

Space–time parallel methods can leverage modern parallel computing architectures to further accelerate numerical simulations where parallelizing only in space limits concurrency. In this paper, we develop a high-order space–time parallel computing method for solving the Navier–Stokes equations. The method is based on revisionist integral deferred correction (RIDC) algorithm proposed by Christlieb, Macdonald and Ong [SIAM J. Sci. Comput., 32 (2010), pp. 818-835]. This parallel-in-time algorithm yields high-order accurate results within a wall-clock time comparable to that of a forward or backward Euler simulation. We extend the RIDC algorithm to a space–time parallel method and apply the method to a compressible fluid solver using the message passing interface (MPI) framework. The global communicator is split into space and time communicators to manage parallel computing in spatial and temporal domains, respectively. Accuracy tests show that the method is high-order accurate. Numerical results encompassing both smooth flows and flows with discontinuities demonstrate that the proposed method can accelerate the computation robustly. Additionally, a three-dimensional space–time parallel computation of flow around a sphere is performed. The results indicate that RIDC has the potential to be used in the field of computational fluid dynamics.

Reducing CO2 Emissions of Hybrid Heavy-Duty Trucks and Buses: Paving the Transition to Low-Carbon Transport

This study investigates the CO2 reduction potential of powertrain hybridisation on heavy-duty lorries and city buses. The analysis considers modern parallel and serial hybrid architectures, assessing their efficiency and limits in CO2 emission reduction through vehicle simulation in VECTO, which is the official tool of the European Commission for calculating heavy-duty vehicle fuel and energy consumption. The results reveal distinct trends for each vehicle type and architecture. In lorries, more significant improvements are observed in urban delivery profiles, reaching up to ~16%, indicating the benefits of hybridisation in transient conditions with energy recuperation opportunities. City buses, particularly those with serial architectures, exhibit significant emission reductions that reach 36%, making them suitable for urban environments. The optimisation of electric motor size and performance plays a crucial role in achieving emission reductions, while battery capacity must be carefully considered to avoid adverse effects. For lorries in urban delivery use, further improvements of 17.5% can be achieved by utilising a 160 kW engine motor and 30 kWh battery. Buses are already quite well optimised, with serial architecture presenting the highest benefits with a 120 kW electric motor and a battery of 11 kWh. Future research should focus on supercapacitors and gearboxes to improve efficiency at higher vehicle speeds and assess hybridisation potential in interurban coach travel. The heavy-duty vehicle sector can make significant strides towards low-carbon transport by maximising hybrid powertrain efficiency and emission reductions.

National education of a harmoniously developed personality of younger generation: practical experience of the teacher and politician Sabir Shayakhmetovich Ziganshin

In this article, the author reveals an urgent problem of modern society and state – the formation of a harmoniously developed personality of younger generation. The author focuses on national education, which at the regional level contributes to the formation of such a model of personality. She gives specific definitions of the concept of «national education» by projecting this category through the past and modern parallels. As an example, she reveals the concept of national education, developed and practically implemented by an outstanding teacher and politician of the Republic of Bashkortostan in the 70-ies of the XX century Sabir Shayakhmetovich Ziganshin. Puts the following ideas into the basis of the national education of a harmoniously developed student's personality: the formation of a personality based on the historical past and culture of his people, folk pedagogy, knowledge and ability to use his native language in the process of communication, labor education, and the construction of the educational process through the integration of regular and extracurricular activities. The model of national education became the main foundations in the formation of the national-regional education system of Bashkortostan, which is relevant to this day, and requires reassessment by modern scientists and pedagogical community. Studying the phenomenon of national education of a harmoniously developed personality and the introduction of S.Sh. Ziganshin's pedagogical ideas today would be an important step in achieving the strategic objectives set for school education and society.

A cross-guidance cross-lingual model on generated parallel corpus for classical Chinese machine reading comprehension

Chinese diachronic gap is a key issue in classical Chinese machine reading comprehension (CCMRC). Preceding work on bridging this gap has been mostly restricted to limited monolingual classical Chinese corpora pre-training and lexical knowledge integration, which require a great deal of human resources. In this paper, we propose a cross-guidance cross-lingual model (CGCLM), pre-trained on a classical and modern Chinese parallel corpus generated from a large language model, to bridge the Chinese diachronic gap and reduce the manual effort. The CGCLM facilitates accurate translation by providing in-context examples and feedback based on the longest common substring between source and target sentences, thereby avoiding untranslated Chinese words. Specifically, we consider three pre-training tasks, i.e., cross-masked language modeling, linguistic label cross-prediction, and semantic cross-aware translation language modeling. The knowledge acquired from masked tokens uncovering and linguistic label predicting can lead to the implicit semantic alignment between two language styles. Taking advantage of the semantic similarity between the same syntactic levels of parallel pairs, cross-aware modeling integrates and transmits contextualized semantic information. We utilize an 18.6G monolingual corpus to create a 37.2G parallel corpus. Manual evaluation has resulted in only acceptable discrepancies between our generated and human-edited parallel corpora. Extensive experimental results show that our proposed model outperforms the state-of-the-art by an average accuracy of 3.13%, 2.44%, and 2.17% on CCMRC, classical Chinese language understanding evaluation (CCLUE), and modern Chinese language understanding evaluation (MCLUE) tasks.

„Eine verschollen geglaubte Textsammlung ruft die Vergangenheit auf“1: Rezension zu: die bewegung der antillen unter der schädeldecke. junge rumäniendeutsche lyrik zwischen 1975 und 1980. Eine (historische) Anthologie herausgegeben von Walter Fromm, 1. Auflage, Ludwigsburg: Pop Verlag 2022. Erweiterte, kritische Neuauflage 2022 mit einem einleitenden Essay von Prof. Dr. Waldemar Fromm und einer soziokulturellen Kontextualisierung von Prof. Dr. Anton Sterbling. Reihe

Abstract The present text is dedicated to an anthology of German poetry from Romania, which was collected in 1980 but could not be published during the communist dictatorship and was subsequently published in 2022 to great success on the German book market. The anthology illustrates the fact that a modern movement parallel to the one in Germany emerged within the insular German-language literature in Romania, developing a set of very distinct particularities.

Современные и исторические параллели восприятия аномии в армянском обществе

<p>The characteristics of perception of anomie are revealed both in modern societies and in the historical past. Although the psychological phenomenon of anomie is expressed in thinking specific for various historical periods, the study of historical and modern parallels allows us to see some models of the development of psychological scientific thought and perception of anomie. Anomie is presented as a psychological pattern of perception with certain characteristics such as social apathy, emotional burnout, alienation, normlessness, social isolation and others. The variety of causes and manifestations of anomie requires the search for new ways and strategies for studying human activity and motivation, which will not only reduce the risks of social apathy, but also adapt the individual to the conditions of rapidly changing social processes. In order to do it it is necessary to study the relationship between the various components of perception of anomie and compare modern anomie issues with historical parallels. The identification of modern and historical parallels in the development of the phenomenon of anomie shows the changes of perception of socio-psychological well-being and worldview. In the methodological sense, the comparative method of the investigation of the perception of anomie allows us to reveal and highlight the historical basis of human worldview.</p>

Task graph-based performance analysis of parallel-in-time methods

In this paper, we present a performance model based on task graphs for various iterative parallel-in-time (PinT) methods. PinT methods have been developed to speed up the simulation time of time-dependent problems using modern parallel supercomputers. The performance model is based on a data-driven notation of the methods, from which a task graph is generated. Based on this task graph and a distribution of time points across processes typical for PinT methods, a theoretical lower runtime bound for the method can be obtained, as well as a prediction of the runtime for a given number of processes. In particular, the model is able to cover the large parameter space of PinT methods and make predictions for arbitrary parameter settings. Here, we describe a general procedure for generating task graphs based on three iterative PinT methods, namely, Parareal, multigrid-reduction-in-time (MGRIT), and the parallel full approximation scheme in space and time (PFASST). Furthermore, we discuss how these task graphs can be used to analyze the performance of the methods. In addition, we compare the predictions of the model with parallel simulation times using five different PinT libraries.

Matroidal connectivity and conditional matroidal connectivity of star graphs

As modern parallel computer systems continue to grow in scale, the occurrence of failures becomes inevitable, which places severe reliability challenges on the systems' underlying interconnection network. Recently, two novel indicators, named the matroidal connectivity and conditional matroidal connectivity, were proposed to better evaluate the reliability of interconnection networks. These indicators enable flexible restrictions on faulty edges in each dimension and have proven to be promising in improving the edge fault-tolerance of interconnection networks. In this paper, we aim to apply these indicators to the n-dimensional star graph Sn, which is a famous interconnection network with high symmetry and recursive hierarchical structure. We partition the faulty edge set F⊆E(Sn) into n−1 parts according to each faulty edge's dimension. By sorting the cardinality of these parts and exerting the restriction that the (n−i)-th largest part's cardinality does not exceed i!−1 for 1≤i≤n−1, we prove that Sn−F is always connected where |F|≤∑i=1n−1(i!−1). Then we determine the exact values of matroidal connectivity and conditional matroidal connectivity of star graphs. Moreover, we make comparisons to demonstrate our results outperform other similar works in terms of edge fault-tolerance.

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

SummarySequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs.

A Multi-GPU Aggregation-Based AMG Preconditioner for Iterative Linear Solvers

We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on modern parallel computers made of hybrid nodes hosting Nvidia Graphics Processing Unit (GPU) accelerators. The work extends previous efforts of some of the authors in the exploitation of a single GPU accelerator and proposes an implementation, based on the hybrid MPI-CUDA software environment, of a Krylov-type linear solver relying on an efficient Algebraic MultiGrid (AMG) preconditioner already available in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BootCMatchG</i> library. Our design for the hybrid implementation has been driven by the best practices for minimizing data communication overhead when multiple GPUs are employed, yet preserving the efficiency of the GPU kernels. Strong and weak scalability results of the new version of the library on well-known benchmark test cases are discussed. Comparisons with the Nvidia AmgX solution show a speedup, in the solve phase, up to 2.0x.

Parallel-in-time optimization of induction motors

Parallel-in-time (PinT) methods were developed to accelerate time-domain solution of evolutionary problems using modern parallel computer architectures. In this paper we incorporate one of the efficient PinT approaches, in particular, the asynchronous truncated multigrid-reduction-in-time algorithm, into a bound constrained optimization procedure applied to an induction machine. Calculation of an optimal motor geometry with respect to its efficiency in the steady state is thus parallelized at each iteration of the optimization algorithm. As a result, a more efficient motor model is obtained about 11 times faster compared to optimization using the standard sequential time stepping.

HipBone: A performance-portable graphics processing unit-accelerated C++ version of the NekBone benchmark

We present hipBone, an open-source performance-portable proxy application for the Nek5000 (and NekRS) computational fluid dynamics applications. HipBone is a fully GPU-accelerated C++ implementation of the original NekBone CPU proxy application with several novel algorithmic and implementation improvements which optimize its performance on modern fine-grain parallel GPU accelerators. Our optimizations include a conversion to store the degrees of freedom of the problem in assembled form in order to reduce the amount of data moved during the main iteration and a portable implementation of the main Poisson operator kernel. We demonstrate near-roofline performance of the operator kernel on three different modern GPU accelerators from two different vendors. We present a novel algorithm for splitting the application of the Poisson operator on GPUs which aggressively hides MPI communication required for both halo exchange and assembly. Our implementation of nearest-neighbor MPI communication then leverages several different routing algorithms and GPU-Direct RDMA capabilities, when available, which improves scalability of the benchmark. We demonstrate the performance of hipBone on three different clusters housed at Oak Ridge National Laboratory, namely, the Summit supercomputer and the Frontier early-access clusters, Spock and Crusher. Our tests demonstrate both portability across different clusters and very good scaling efficiency, especially on large problems.

Detailed Modeling of Heterogeneous and Contention-Constrained Point-to-Point MPI Communication

The network topology of modern parallel computing systems is inherently heterogeneous, with a variety of latency and bandwidth values. Moreover, contention for the bandwidth can exist on different levels when many processes communicate with each other. Many-pair, point-to-point MP communication is thus characterized by heterogeneity and contention, even on a cluster of homogeneous multicore CPU nodes. To get a detailed understanding of the individual communication cost per MPI process, we propose a new modeling methodology that incorporates both heterogeneity and contention. First, we improve the standard max-rate model to better quantify the actually achievable bandwidth depending on the number of MPI processes in competition. Then, we make a further extension that more detailedly models the bandwidth contention when the competing MPI processes have different numbers of neighbors, with also non-uniform message sizes. Thereafter, we include more flexibility by considering interactions between intra-socket and inter-socket messaging. Through a series of experiments done on different processor architectures, we show that the new heterogeneous and contention-constrained performance models can adequately explain the individual communication cost associated with each MPI process. The largest test of realistic point-to-point MPI communication involves 8,192 processes and in total 2,744,632 simultaneous messages over 64 dual-socket AMD Epyc Rome compute nodes connected by InfiniBand, for which the overall prediction accuracy achieved is 8 4%.

A distributed computing model for big data anonymization in the networks.

Recently big data and its applications had sharp growth in various fields such as IoT, bioinformatics, eCommerce, and social media. The huge volume of data incurred enormous challenges to the architecture, infrastructure, and computing capacity of IT systems. Therefore, the compelling need of the scientific and industrial community is large-scale and robust computing systems. Since one of the characteristics of big data is value, data should be published for analysts to extract useful patterns from them. However, data publishing may lead to the disclosure of individuals' private information. Among the modern parallel computing platforms, Apache Spark is a fast and in-memory computing framework for large-scale data processing that provides high scalability by introducing the resilient distributed dataset (RDDs). In terms of performance, Due to in-memory computations, it is 100 times faster than Hadoop. Therefore, Apache Spark is one of the essential frameworks to implement distributed methods for privacy-preserving in big data publishing (PPBDP). This paper uses the RDD programming of Apache Spark to propose an efficient parallel implementation of a new computing model for big data anonymization. This computing model has three-phase of in-memory computations to address the runtime, scalability, and performance of large-scale data anonymization. The model supports partition-based data clustering algorithms to preserve the λ-diversity privacy model by using transformation and actions on RDDs. Therefore, the authors have investigated Spark-based implementation for preserving the λ-diversity privacy model by two designed City block and Pearson distance functions. The results of the paper provide a comprehensive guideline allowing the researchers to apply Apache Spark in their own researches.

Multiple context-free path querying by matrix multiplication

Many graph analysis problems can be formulated as formal language-constrained path querying problems where the formal languages are used as constraints for navigational path queries. Recently, the context-free language (CFL) reachability formulation has become very popular and can be used in many areas, for example, querying graph databases, Resource Description Framework (RDF) analysis. However, the generative capacity of context-free grammars (CFGs) is too weak to generate some complex queries, for example, from natural languages, and the various extensions of CFGs have been proposed. Multiple context-free grammar (MCFG) is one of such extensions of CFGs. Despite the fact that, to the best of our knowledge, there is no algorithm for MCFL-reachability, this problem is known to be decidable. This paper is devoted to developing the first such algorithm for the MCFL-reachability problem. The essence of the proposed algorithm is to use a set of Boolean matrices and operations on them to find paths in a graph that satisfy the given constraints. The main operation here is Boolean matrix multiplication. As a result, the algorithm returns a set of matrices containing all information needed to solve the MCFL-reachability problem. The presented algorithm is implemented in Python using GraphBLAS API. An analysis of real RDF data and synthetic graphs for some MCFLs is performed. The study showed that using a sparse format for matrix storage and parallel computing for graphs with tens of thousands of edges the analysis time can be 10–20 minutes. The result of the analysis provides tens of millions of reachable vertex pairs. The proposed algorithm can be applied in problems of static code analysis, bioinformatics, network analysis, as well as in graph databases when a path query cannot be expressed using context-free grammars. The provided algorithm is linear algebra-based, hence, it allows one to use high-performance libraries and utilize modern parallel hardware.

Solving Time-Fractional reaction–diffusion systems through a tensor-based parallel algorithm

Machine Learning (ML) approach is a discussed research topic because of its benefit in several research fields. The most important issues in the training process of ML are accuracy and speed: a suitable mathematical model is critical and a fast data processing is mandatory. Fractional Calculus is involved in a large number of important applications and, recently, many ML algorithms, in order to improve accuracy of results when performing training in solving optimization problems, are based on decision and control performed by means of time-fractional models to better understand complex systems. However, the high computational cost, which characterizes the numerical solution, of this approach might be a problem for large scale Machine Learning systems. High Performance Computing (HPC) is the way of addressing the need of real time computation. In fact, through tensor-based parallel strategies designed for modern parallel architectures, Fractional Calculus tools are very helpful for the ML training step. In this contest, we consider a time-fractional diffusion system and, after introducing a suitable modification of a numerical model to solve it, we propose a related and novel parallel implementation on GPUs (Graphics Processing Units). Experiments show the gain of performance in terms of execution time and accuracy of our parallel implementation.

Toward Minimum WCRT Bound for DAG Tasks Under Prioritized List Scheduling Algorithms

Many modern real-time parallel applications can be modeled as a directed acyclic graph (DAG) task. Recent studies show that the worst-case response time (WCRT) bound of a DAG task can be significantly reduced when the execution order of the vertices is determined by the priority assigned to each vertex of the DAG. How to obtain the optimal vertex priority assignment, and how far from the best-known WCRT bound of a DAG task to the minimum WCRT bound are still open problems. In this article, we aim to construct the optimal vertex priority assignment and derive the minimum WCRT bound for the DAG task. We encode the priority assignment problem into an integer linear programming (ILP) formulation. To solve the ILP model efficiently, we do not involve all variables or constraints. Instead, we solve the ILP model iteratively, i.e., we initially solve the ILP model with only a few primary variables and constraints, and then at each iteration, we increment the ILP model with the variables and constraints which are more likely to derive the optimal priority assignment. Experimental work shows that our method is capable of solving the ILP model optimally without involving too many variables or constraints, e.g., for instances with 50 vertices, we find the optimal priority assignment by involving 12.67% variables on average and within several minutes on average.

Special Issue 19th international workshop on algorithms, models and tools for parallel computing on heterogeneous platforms (HeteroPar'21)

Heterogeneity has emerged as one of the most profound and challenging characteristics of today's parallel environments. From the macro level, where networks of distributed computers, composed of diverse node architectures, are interconnected with potentially heterogeneous networks, to the micro level, where deeper memory hierarchies and various accelerator architectures are increasingly common, the impact of heterogeneity on all computing tasks is increasing rapidly. Traditional parallel algorithms, programming environments, and tools, designed for legacy homogeneous multiprocessors, achieve a small fraction of the efficiency and the potential performance that can be obtained in current and future heterogeneous computing platforms. New ideas, innovative algorithms, and specialized programming environments and tools are needed to efficiently use these modern parallel and heterogeneous architectures. The International workshop on algorithms, models and tools for parallel computing on heterogeneous platforms (HeteroPar) is a forum for researchers working on algorithms, programming languages, tools, and theoretical models for efficiently solving complex problems on heterogeneous parallel platforms. HeteroPar 2021 took place (virtually) in Lisbon, Portugal, organized for the 13th time in conjunction with the Euro-Par annual international conference. The format of the workshop included one keynote and 11 technical presentations. The workshop papers represented an interesting mix of topics, addressing the implementation of algorithms and kernels for heterogeneous computing, programming models, data management, runtime and resource management, energy efficiency, cloud computing, and artificial intelligence-based methods oriented towards heterogeneous platforms, as the basis for the next generation exascale computers. The selected papers cover a good spectrum of research topics in the area of heterogeneous computing showing the challenges present on these modern platforms.

Adaptive Subsystem Density Functional Theory.

Subsystem density functional theory (DFT) is emerging as a powerful electronic structure method for large-scale simulations of molecular condensed phases and interfaces. Key to its computational efficiency is the use of approximate nonadditive noninteracting kinetic energy functionals. Unfortunately, currently available nonadditive functionals lead to inaccurate results when the subsystems interact strongly such as when they engage in chemical reactions. This work disrupts the status quo by devising a workflow that extends subsystem DFT's applicability also to strongly interacting subsystems. This is achieved by implementing a fully automated adaptive definition of subsystems which is realized during geometry optimizations or ab initio molecular dynamics simulations. The new method prescribes subsystem merging and splitting events redistributing the resources (both for work and data) in an efficient way making use of modern parallelization strategies and object-oriented programming. We showcase the method with examples probing from moderate-to-strong inter-subsystem interactions, opening the door to using subsystem DFT for modeling chemical reactions in molecular condensed phases with a black box computational tool.

Domain decomposition methods for 3D crack propagation problems using XFEM

The extended finite element method (XFEM) has been successfully implemented in solving crack propagation problems by enriching the polynomial basis functions of standard finite elements with specialized non-smooth functions. The resulting approximation space can be used to solve problems with moving discontinuities, such as cracks, without the need of remeshing in the vicinity of the crack. The enrichment of the displacement field in XFEM inflicts a substantial increase in the ellipticity of the discretized problem. As a consequence, the resulting algebraic systems become strongly ill-conditioned, rendering the convergence of iterative solvers very slow. On the other hand, direct solvers may become inefficient in 3D problems, due to the increased bandwidth of the system matrix. In this paper, two of the most efficient domain decomposition solvers, namely the FETI-DP and P-FETI-DP, are proposed for solving the linear systems resulting from XFEM crack propagation analysis in large-scale 3D problems. Both solvers are amenable to parallelization and can be implemented in modern parallel computing environments, with multicore processors and distributed memory systems, following appropriate modifications, to achieve a drastic reduction of memory and computing time in computationally intensive problems.