Parallel graph mining with dynamic load balancing

Knowledge mining from single graph plays an important role in decision support systems on single graphs such as social networks, bioinformatics, etc. In recent years, the problem of Frequent Subgraph Mining (FSM) from a single graph have been developed and attracted several studies. However, the problem of mining association rules or links from frequent subgraphs has not had many contributions. In this article, we state the problem of direct mining association rules from frequent subgraphs. Existing approaches on this topic perform the task in two phases. First, they traverse the search space to directly discover parent-child relationships from the discovered frequent subgraphs, then association rules are generated. We propose a one-phase algorithm, named So-GPARs, to generate rules as soon as frequent supergraphs are constructed from already existing frequent subgraphs. Our experiments on three single graph datasets show that the one-phase algorithm is more efficient than the two-phase algorithm in terms runtime of the rules generating phase.

SCALE: A hybrid MPI and multithreading based work stealing approach for massive contingency analysis in power systems

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Graph mining is a well-established research field, and lately it has drawn in considerable research communities. It allows to process, analyze, and discover significant knowledge from graph data. In graph mining, one of the most challenging tasks is frequent subgraph mining (FSM). FSM consists of applying the data mining algorithms to extract interesting, unexpected, and useful graph patterns from the graphs. FSM has been applied to many domains, such as graphical data management and knowledge discovery, social network analysis, bioinformatics, and security. In this context, a large number of techniques have been suggested to deal with the graph data. These techniques can be classed into two primary categories: (i) a priori-based FSM approaches and (ii) pattern growth-based FSM approaches. In both of these categories, an extensive research work is available. However, FSM approaches are facing some challenges, including enormous numbers of frequent subgraph patterns (FSPs); no suitable mechanism for applying ranking at the appropriate level during the discovery process of the FSPs; extraction of repetitive and duplicate FSPs; user involvement in supplying the support threshold value; large number of subgraph candidate generation. Thus, the aim of this research is to make do with the challenges of enormous FSPs, avoid duplicate discovery of FSPs, and use the ranking for such patterns. Therefore, to address these challenges a new FSM framework A RAnked Frequent pattern-growth Framework (A-RAFF) is suggested. Consequently, A-RAFF provides an efficacious answer to these challenges through the initiation of a new ranking measure called FSP-Rank. The proposed ranking measure FSP-Rank effectively reduced the duplicate and enormous frequent patterns. The effectiveness of the techniques proposed in this study is validated by extensive experimental analysis using different benchmark and synthetic graph datasets. Our experiments have consistently demonstrated the promising empirical results, thus confirming the superiority and practical feasibility of the proposed FSM framework.

Routing is a very complex process in the field programmable gate array (FPGA) CAD flow. The increase of both FPGA size and design complexity leads to a long routing time hindering the productivity. In this article, we propose a more effective parallel router that combines static and dynamic load balance in parallel routing for FPGAs. First, we explore hierarchical region partitioning to assign routing tasks to different cores for static load balance. Then, we coordinate message propagation and task migration at runtime so that load balance between cores can be dynamically maintained in parallel routing. Finally, we combine static and dynamic load balance in the parallel routing for a higher degree of parallelism. Our parallel router performs on the multicore distributed-memory systems and the communication between cores is through message passing interface messages. We demonstrate the effectiveness of our parallel router using large-scale Titan designs. On average, our parallel router can scale up to 32 cores to achieve about 17× speedup with slight loss of quality, compared with the latest VTR 8 router.

Clusters, composed of symmetric multiprocessor (SMP) machines and heterogeneous machines, have become increasingly popular for high‐performance computing. Message‐passing libraries, such as message‐passing interface (MPI) and parallel virtual machine (PVM), are de facto parallel programming libraries for clusters that usually consist of homogeneous and uni‐processor machines. For SMP machines, MPI is combined with multithreading libraries like POSIX Thread and OpenMP to take advantage of the architecture. In addition to existing parallel programming libraries that are in C/C++ and FORTRAN programming languages, the Java programming language presents itself as another alternative with its object‐oriented framework, platform neutral byte code, and ever‐increasing performance. This paper presents a new parallel programming model and a library, VCluster, which implements this model. VCluster is based on migrating virtual threads instead of processes to support clusters of SMP machines more efficiently. The implementation uses thread migration, which can be used in dynamic load balancing. VCluster was developed in pure Java, utilizing the portability of Java to support clusters of heterogeneous machines. Several applications are developed to illustrate the use of this library and compare the usability and performance of VCluster with other approaches. Copyright © 2007 John Wiley & Sons, Ltd.

In the era of petascale supercomputing, the importance of load balancing is crucial. Although dynamic load balancing is widespread, it is increasingly difficult to implement effectively with thousands of processors or more, prompting a second look at static load-balancing techniques even though the optimal allocation of tasks to processors is an NP-hard problem. We propose a heuristic static load-balancing algorithm, employing fitted benchmarking data, as an alternative to dynamic load balancing. The problem of allocating CPU cores to tasks is formulated as a mixed-integer nonlinear optimization problem, which is solved by using an optimization solver. On 163,840 cores of Blue Gene/P, we achieved a parallel efficiency of 80% for an execution of the fragment molecular orbital method applied to model protein-ligand complexes quantum-mechanically. The obtained allocation is shown to outperform dynamic load balancing by at least a factor of 2, thus motivating the use of this approach on other coarse-grained applications.

We study the problem of balancing the load on processors in an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on a number of different networks has been well-studied, analytical work on dynamic load balancing is limited. The difficulty lies in modeling the arrivals and departures of jobs in a clean manner. In this paper, we introduce a new framework for dynamic load balancing in which the job traffic is modeled by an adversary. Our main result is that a simple local load balancing algorithm maintains the load of the network within a stable level against a powerful adversary. Our result holds for different models of traffic patterns and processor communication.

Good load balancing is the key to deriving maximal performance from multiprocessors. Several successful dynamic load balancing techniques on tightly-coupled multiprocessors have been developed. However, load balancing is more difficult on loosely-coupled multiprocessors because inter-processor communication overheads cost more. Dynamic load balancing techniques have been employed in a few programs on loosely-coupled multiprocessors, but they are tightly built into the particular programs and not much attention is paid to scalability. We have developed a dynamic load balancing scheme which is applicable to OR-parallel programs in general. Processors are grouped, and work loads of groups and processors are balanced hierarchically. Moreover, it is scalable to any number of processors because of this multi-level hierarchical structure. The scheme is tested for the all-solution exhaustive search Pentomino program on the mesh-connected loosely-coupled multiprocessor Multi-PSI, and speedups of 28.4 times with 32 processors and 50 times with 64 processors have been attained.

Good load balancing is the key to deriving maximal performance from multiprocessors. Several successful dynamic load balancing techniques on tightly-coupled multiprocessors have been developed. However, load balancing is more difficult on loosely-coupled multiprocessors because inter-processor communication overheads cost more. Dynamic load balancing techniques have been employed in a few programs on loosely-coupled multiprocessors, but they are tightly built into the particular programs and not much attention is paid to scalability. We have developed a dynamic load balancing scheme which is applicable to OR-parallel programs in general. Processors are grouped, and work loads of groups and processors are balanced hierarchically. Moreover, it is scalable to any number of processors because of this multi-level hierarchical structure. The scheme is tested for the all-solution exhaustive search Pentomino program on the mesh-connected loosely-coupled multiprocessor Multi-PSI, and speedups of 28.4 times with 32 processors and 50 times with 64 processors have been attained.

The fundamental problems in dynamic load balancing and job scheduling in parallel and distributed computers involve moving load between processors. In this paper, we consider a new model for load movement in synchronous parallel and distributed machines. In each step of our model, each processor can transfer load to at most one neighbor; also, any amount of load can be moved along a communication link between two processors in one step. This is a reasonable model for load movement in significant classes of dynamic load balancing problems.We derive efficient algorithms for a number of task reallocation problems under our model of load movement. These include dynamic load balancing on processor networks, adaptive mesh re-partitioning such as those in finite element methods, and progressive job migration under dynamic generation and consumption of load.To obtain the above-mentioned results, we introduce and solve the abstract problem of Incremental Weight Migration (IWM) on arbitrary graphs. Our main result is a simple, randomized, algorithm for this problem which provably results in asymptotically optimal convergence towards the state where weights on the nodes of the graph are all equal. This algorithm utilizes an appropriate random set of edges forming a matching. Our algorithm for the IWM problem is used in deriving efficient algorithms for all the problems mentioned above.Our results are very general. The algorithms we derive are local, and hence, scalable. They work for arbitrary load distributions and for networks of arbitrary topology which can possibly undergo link failures. Of independent interest is our proof technique which we use to lower bound the convergence of our algorithms in terms of the eigenstructure of the underlying graph.Finally, we present preliminary experimental results analyzing issues in load balancing related to our algorithms.

Graph data mining has been a crucial as well as inevitable area of research. Large amounts of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social Networks, etc. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. In the past few years, several research groups have attempted to handle the Frequent Subgraph Mining (FSM) problem in multiple ways. Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. Later, Google's MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing. Although MapReduce came with many benefits, its disk I/O and noniterative style model could not help much for FSM domain since subgraph mining process is an iterative approach. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. This is a right fit solution for iterative style of programming as well. In this survey, we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results.

Dynamic load balancing with enhanced shared-memory parallelism for particle-in-cell codes

In this paper we propose an improved quasi-Monte Carlo method for solving Linear Algebra problems. We show that by using low-discrepancy sequences both the convergence and the CPU time of the algorithm are improved. Two parallelization schemes using the Message Passing Interface with static and dynamic load balancing are proposed. The dynamic scheme is useful for computing in the GRID environment.

The uncertain nature of mapping user tasks to Virtual Machines (VMs) causes system failure or execution delay in Cloud Computing. To maximize cloud resource throughput and decrease user response time, load balancing is needed. Possible load balancing is needed to overcome user task execution delay and system failure. Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration. Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs. Thus, the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism (HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly. This load balancing approach improved performance by considering average network latency, dependability, and throughput. This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation, assign jobs to VMs with more solution diversity, and prevent the solution from reaching a local optimality. Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan, 16.21% increase in mean throughput, and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA, HD-WOA, and Binary Bird Swap.