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MTCProv: a practical provenance query framework for many-task scientific computing

distributed AND parallel DATABASES 2012年第5-6期30卷 351-370页

作者： Gadelha, Luiz M. R., Jr. Wilde, Michael Mattoso, Marta Foster, Ian Univ Fed Rio de Janeiro COPPE Comp Engn Program BR-21945 Rio De Janeiro Brazil Natl Lab Sci Comp Petropolis Brazil Argonne Natl Lab Div Math & Comp Sci Chicago IL USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA Argonne Natl Lab Computat Inst Chicago IL USA

Scientific research is increasingly assisted by computer-based experiments. Such experiments are often composed of a vast number of loosely-coupled computational tasks that are specified and automated as scientific workflows. This large scale is also characteristic of the data that flows within such "many-task" computations (MTC). Provenance information can record the behavior of such computational experiments via the lineage of process and data artifacts. However, work to date has focused on lineage data models, leaving unsolved issues of recording and querying other aspects, such as domain-specific information about the experiments, MTC behavior given by resource consumption and failure information, or the impact of environment on performance and accuracy. In this work we contribute with MTCProv, a provenance query framework for many-task scientific computing that captures the runtime execution details of MTC workflow tasks on parallel and distributed systems, in addition to standard prospective and data derivation provenance. To help users query provenance data we provide a high level interface that hides relational query complexities. We evaluate MTCProv using an application in protein science, and describe how important query patterns such as correlations between provenance, runtime data, and scientific parameters are simplified and expressed.

关键词： Provenance Many-task computing Database queries parallel and distributed computing

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Extended computational kernels in a massively parallel implementation of the Trotter-Suzuki approximation

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COMPUTER PHYSICS COMMUNICATIONS 2015年 197卷 339-340页

作者： Wittek, Peter Calderaro, Luca ICFO Castelldefels 08860 Barcelona Spain Univ Boras S-50190 Boras Sweden Univ Padua I-35123 Padua Italy

We extended a parallel and distributed implementation of the Trotter Suzuki algorithm for simulating quantum systems to study a wider range of physical problems and to make the library easier to use. The new release allows periodic boundary conditions, many-body simulations of non-interacting particles, arbitrary stationary potential functions, and imaginary time evolution to approximate the ground state energy. The new release is more resilient to the computational environment: a wider range of compiler chains and more platforms are supported. To ease development, we provide a more extensive command-line interface, an application programming interface, and wrappers from high-level languages.

关键词： Quantum evolution Trotter-Suzuki algorithm parallel and distributed computing GPU computing

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Mathematical modeling and problem solving: from fundamentals to applications

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JOURNAL OF SUPERcomputing 2024年第10期80卷 14116-14119页

作者： Ohue, Masahito Sasayama, Kotoyu Takata, Masami Tokyo Inst Technol Sch Comp Dept Comp Sci Yokohama Kanagawa 2268501 Japan Mitsubishi Elect Bldg Solut Corp Elevator & Escalator Dev Dept Aichi 4928682 Japan Nara Womens Univ Res Grp Informat & Commun Technol Life Nara 6308506 Japan

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on parallel and distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas.

关键词： Mathematical modeling Machine learning parallel and distributed computing Drug discovery and materials science

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BaPa: A Novel Approach of Improving Load Balance in parallel Matrix Factorization for Recommender Systems

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IEEE TRANSACTIONS ON COMPUTERS 2021年第5期70卷 789-802页

作者： Guo, Ruixin Zhang, Feng Wang, Lizhe Zhang, Wusheng Lei, Xinya Ranjan, Rajiv Zomaya, Albert Y. China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Comp Sci Beijing 100084 Peoples R China Univ Newcastle Sch Comp Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Sydney Sch Comp Sci Camperdown NSW 2006 Australia

A simplified approach to accelerate matrix factorization of big data is to parallelize it. A commonly used method is to divide the matrix into multiple non-intersecting blocks and concurrently calculate them. This operation causes the Load balance problem, which significantly impacts parallel performance and is a big concern. A general belief is that the load balance across blocks is impossible by balancing rows and columns separately. We challenge the belief by proposing an approach of "Balanced Partitioning (BaPa)". We demonstrate under what circumstance independently balancing rows and columns can lead to the balanced intersection of rows and columns, why, and how. We formally prove the feasibility of BaPa by observing the variance of rating numbers across blocks, and empirically validate its soundness by applying it to two standard parallel matrix factorization algorithms, DSGD and CCD++. Besides, we establish a mathematical model of "Imbalance Degree" to explain further why BaPa works well. BaPa is applied to synchronous parallel matrix factorization, but as a general load balance solution, it has significant application potential.

关键词： Matrix decomposition Task analysis Partitioning algorithms Charge coupled devices Sparse matrices Recommender systems Computer science Matrix factorization stochastic gradient descent recommender systems parallel and distributed computing load balancing

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Bayanihan: building and studying web-based volunteer computing systems using Java

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 1999年第5-6期15卷 675-686页

作者： Sarmenta, LFG Hirano, S MIT Comp Sci Lab Cambridge MA 02139 USA Electrotech Lab Tsukuba Ibaraki 305 Japan

Project Bayanihan is developing the idea of volunteer computing, which seeks to enable people to form very large parallel computing networks very quickly by using ubiquitous and easy-to-use technologies such as web browsers and Java. By utilizing Java's object-oriented features, we have built a flexible software framework that makes it easy for programmers to write different volunteer computing applications, while allowing researchers to study and develop the underlying mechanisms behind them. In this paper, we show how we have used this framework to write master-worker style applications, and to develop approaches to the problems of programming interface, adaptive parallelism, fault-tolerance, computational security, scalability, and user interface design. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： metacomputing parallel and distributed computing network of workstations heterogeneous computing Java

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parallel history matching and associated forecast at the center for interactive smart oilfield technologies

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JOURNAL OF SUPERcomputing 2007年第2期41卷 109-117页

作者： Nomura, Ken-Ichi Kalia, Rajiv K. Nakano, Aiichiro Vashishta, Priya Landa, Jorge L. Univ So Calif CiSoft CACS Los Angeles CA 90089 USA Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Dept Phys & Astron Los Angeles CA 90089 USA Univ So Calif Dept Chem Engn & Mat Sci Los Angeles CA 90089 USA ChevronTexaco San Ramon CA 94583 USA

We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data sets and is of great importance to petroleum industry. A Monte Carlo method, combined with proxies to avoid excessive data processing, is employed to identify reservoir simulation models that best match the oilfield production history. Subsequently, the selected models are used to forecast future productions with uncertainty estimates. The parallelization framework combines: (1) message passing for tightly coupled intra-simulation decomposition;and (2) scheduler/Grid remote procedure calls for model parameter sweeps. A preliminary numerical test has included 3,159 simulations on a 256-processor Intel Xeon cluster at the USC-CACS. The results provide uncertainty estimates of unprecedented precision.

关键词： smart oilfield history matching and forecast inverse problem Monte Carlo method parallel and distributed computing massive data sets

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Exploiting application tunability for efficient, predictable resource management in parallel and distributed systems

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JOURNAL OF parallel and distributed computing 2000年第11期60卷 1420-1445页

作者： Chang, FZ Karamcheti, V Kedem, Z NYU Courant Inst Math Sci Dept Comp Sci New York NY 10003 USA

parallel and distributed computing is becoming increasingly mainstream driven both by the widespread availability of commodity small-scale symmetric multiprocessors and high-performance cluster platforms, as well as the growing use of parallelism and distribution in networked applications such as image recognition, media processing, virtual reality, and telepresence. However, many of these applications impose son timeliness and output quality constraints on top of the traditional performance requirements, necessitating efficient, predictable management of system resources. Existing techniques are inadequate to simultaneously support these twin requirements of efficiency and predictability. In this paper, we propose a novel approach for increasing system efficiency while meeting application timeliness and quality constraints. Our approach exploits the application tunability found in many general-purpose computations. Tunability refers to an application's ability to trade off resource requirements over several dimensions including time, quality, and resource type;the resulting flexibility enables the underlying resource management system to choose an application operating point best suited to available resource characteristics. We describe language and scheduler extensions to support tunability in the MILAN metacomputing environment and then systematically characterize performance benefits of tunability using a parameterizable task system. Our. results show that application tunability is easily expressible and can significantly improve resource utilization. (C) 2000 Academic Press.

关键词： application tunability parallel and distributed computing resource management

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Energy-Aware Scheduling for Precedence-Constrained parallel Virtual Machines in Virtualized Data Centers

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JOURNAL OF GRID computing 2015年第2期13卷 233-253页

作者： Ebrahimirad, Vahid Goudarzi, Maziar Rajabi, Aboozar Sharif Univ Technol Dept Comp Engn Tehran Iran Univ Tehran Sch Elect & Comp Engn Tehran Iran

Large scale Internet services are expected to only increase in complexity and popularity. Their energy consumption is also a major concern in data centers. Smart scheduling of their sub-services on data center Physical Machines (PM) can effectively improve their energy as well as performance. Since today servers are not energy-proportional yet, a major and traditionally neglected source of inefficiency in them is the utilization level of PMs. We present two scheduling algorithms for precedence-constrained parallel Virtual Machines (VM) in a virtualized data center where each VM represents a sub-service of the Internet-scale service. Our algorithms use virtualization technology to increase utilization of the PMs, and hence reduce total number of active PMs, to improve energy with minimal effect on makespan. Both proposed algorithms have a polynomial time complexity which make them suitable options for scheduling of large services. Simulation results using real-world services demonstrate that the algorithms are capable of increasing utilization level of PMs on average by 52 % and improving energy consumption by 18 % while the makespan of services is degraded less than 2 %.

关键词： Energy-aware scheduling List-based scheduling Precedence-constrained parallel virtual machines Virtualized data centers parallel and distributed computing

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Near-optimal large-scale k-medoids clustering

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INFORMATION SCIENCES 2021年 545卷 344-362页

作者： Ushakov, Anton V. Vasilyev, Igor Russian Acad Sci Matrosov Inst Syst Dynam & Control Theory Siberian Branch 134 Lermontov Str Irkutsk 664033 Russia

The k-medoids (k-median) problem is one of the best known unsupervised clustering problems. Due to its complexity, finding high-quality solutions for huge-scale datasets remains extremely challenging. The application of many approaches finding optimal or quality solutions is limited to only small and medium-size instances. On the other hand, many parallel, distributed algorithms that can handle huge-scale datasets usually provide very poor solutions. In this paper, we develop a first parallel, distributed primal-dual heuristic algorithm for the k-medoids problem. Its main component is a very efficient parallel subgradient column generation that solves a Lagrangian dual problem and finds a tight bound on solution quality. High-quality solutions are then produced by a parallel core selection technique. We considerably reduce computational burden and memory load by employing a nearest neighbor strategy to approximate the dissimilarity matrix. We demonstrate that our algorithm finds very close to optimal solutions, confirmed by the tightness of dual bounds, of instances that are much larger than those considered in the literature to date. Our experiments include clustering large-scale collections of face images into several thousand of clusters. We show that our approach outperforms parallel improved versions of the most popular k-medoids clustering algorithms, achieving nearly linear parallel speedup. (C) 2020 Elsevier Inc. All rights reserved.

关键词： parallel and distributed computing K-medoids clustering Nearest neighbors P-median problem MPI CLARA

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A Case for Stale Synchronous distributed Model for Declarative Recursive Computation

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THEORY AND PRACTICE OF LOGIC PROGRAMMING 2019年第5-6期19卷 1056-1072页

作者： Das, Ariyam Zaniolo, Carlo Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

A large class of traditional graph and data mining algorithms can be concisely expressed in Datalog, and other Logic-based languages, once aggregates are allowed in recursion. In fact, for most BigData algorithms, the difficult semantic issues raised by the use of non-monotonic aggregates in recursion are solved by Pre-Mappability (PreM), a property that assures that for a program with aggregates in recursion there is an equivalent aggregate-stratified program. In this paper we show that, by bringing together the formal abstract semantics of stratified programs with the efficient operational one of unstratified programs, PreM can also facilitate and improve their parallel execution. We prove that PreM-optimized lock-free and decomposable parallel semi-naive evaluations produce the same results as the single executor programs. Therefore, PreM can be assimilated into the data-parallel computation plans of different distributed systems, irrespective of whether these follow bulk synchronous parallel (BSP) or asynchronous computing models. In addition, we show that non-linear recursive queries can be evaluated using a hybrid stale synchronous parallel (SSP) model on distributed environments. After providing a formal correctness proof for the recursive query evaluation with PreM under this relaxed synchronization model, we present experimental evidence of its benefits.

关键词： Datalog Deductive Databases Recursive Query Stale Synchronous parallel Model Bulk Synchronous parallel Model parallel and distributed computing

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