Worker Threads Research Articles

PROGRAMACIÓN EN PHP DE UN MODELO LOGARÍTMICO MULTIPLICATIVO DE CRECIMIENTO DE VENTAS EN EL SECTOR EMPRESARIAL

The thread of this research work is to program a logarithmic model tested by the authors Machek and Machek, published in 2014. They propose a mathematical model that focuses on the factors that contribute to a greater extent to the behavior of sales in pesos, variable applied to measure business growth. This type of models published in international journals, is little applied in the Mexican business sector, every time, by the complexity of the model, hence the interest of present easily programmed in PHP, to the user end and taker of decisions in this type of organizations and meet the goal of increasing the number of larger companies in the country, which contribute to a greater extent to domestic production, generating economic development. K eywords: Personnel Home Page (PHP), Ratios of Productivity, Ratios Intensity of Work, Ratios Capital Intensity.

Optimization of patch antennas via multithreaded simulated annealing based design exploration

Abstract In this paper, we present a new software framework for the optimization of the design of microstrip patch antennas. The proposed simulation and optimization framework implements a simulated annealing algorithm to perform design space exploration in order to identify the optimal patch antenna design. During each iteration of the optimization loop, we employ the popular MEEP simulation tool to evaluate explored design solutions. To speed up the design space exploration, the software framework is developed to run multiple MEEP simulations concurrently. This is achieved using multithreading to implement a manager-workers execution strategy. The number of worker threads is the same as the number of cores of the computer that is utilized. Thus, the computational runtime of the proposed software framework enables effective design space exploration. Simulations demonstrate the effectiveness of the proposed software framework. Highlights A software framework for the optimization of the design of microstrip patch antennas. A simulated annealing algorithm to perform the design space exploration. The popular MEEP simulator is employed to evaluate explored solutions for accuracy. Multithreading is used as a technique to speed-up the proposed tool.

Asynchronous Non-Generational Model to Parallelize Metaheuristics: A Bioinformatics Case Study

The integration of parallel computing techniques into metaheuristics has traditionally represented a promising approach to tackle computationally demanding optimization problems. In the last years, metaheuristics have evolved by including more complex search mechanisms whose parallelization often leads to performance issues under classic parallel schemes. This work investigates the asynchronous non-generational parallelization model, which is aimed at dealing with performance pitfalls by allowing worker threads to behave as asynchronous independent agents. We incorporate asynchronous principles into a recently proposed metaheuristic for multiobjective optimization, the Indicator-Based Multiobjective Bat Algorithm, and apply the resulting approach to solve a real-world problem in the bioinformatics domain: the reconstruction of evolutionary histories. Experiments on multicore multiprocessor systems comprising up to 64 cores reveal the suitability of the model to address the main challenges of the metaheuristic design under study, outperforming other implementations and methods in terms of parallel performance while also achieving significant solution quality.

Enhancing application performance via DAG-driven scheduling in task parallelism for cloud center

Nowadays, offloading technologies are applied to smart devices, which add more jobs into cloud data center. In cloud data center, limited physical resources and competitions of different jobs all need to be improved the performance. Considering more jobs are in the kind of task parallelism, how to improve their performance is very important. However, due to the size of transistor is approaching physical extreme limit, the count of transistor integrated into a single CPU core is seriously restricted. On the other hand, constrained by cooling efficiency, the frequency of CPU can not be raising without restriction which could lead CPU’s energy consumption and heat production to a rapidly growth. As performance improvement of new generations of hardware has slowed, the era of serial computing is over and programmers are getting hard to acquire free application acceleration through hardware updating. The direction of computer architecture is transforming to parallel structure and in order to be highly qualified in the new world of parallel computing, squeezing last bit of performance of current state-of-the-art architectures is an urgent task for whole cloud computing community. In this paper, we present Function Flow a C++11-based generic framework for task parallelism. Our insight is that heavy use of generic parallel algorithms in task parallelism may introduce numerous unnecessary synchronous operation which can cause loss of performance of applications. To solve the problem, in Function Flow we propose a DAG-driven task scheduler for programs that can be expressed as a Direct Acyclic Graph of tasks with dependency edges. Function Flow distributes work threads to cores and schedules tasks based purely on tasks’ state in DAGs constructed by programmers. Because our implementation is based on callback mechanism, DAGs are represented compactly and the scheduler in Function Flow works in a dynamic and fully-distributed manner. To achieve high performance the only thing programmers need to do is characterizing dependencies between tasks with the help of user-friendly interfaces provided by Function Flow. We use several micro-benchmarks to demonstrate the efficiency of our approach and analyze the performance of the framework.

Synthesis of divide and conquer parallelism for loops

Divide-and-conquer is a common parallel programming skeleton supported by many cross-platform multithreaded libraries, and most commonly used by programmers for parallelization. The challenges of producing (manually or automatically) a correct divide-and-conquer parallel program from a given sequential code are two-fold: (1) assuming that a good solution exists where individual worker threads execute a code identical to the sequential one, the programmer has to provide the extra code for dividing the tasks and combining the partial results (i.e. joins), and (2) the sequential code may not be suitable for divide-and-conquer parallelization as is, and may need to be modified to become a part of a good solution. We address both challenges in this paper. We present an automated synthesis technique to synthesize correct joins and an algorithm for modifying the sequential code to make it suitable for parallelization when necessary. This paper focuses on class of loops that traverse a read-only collection and compute a scalar function over that collection. We present theoretical results for when the necessary modifications to sequential code are possible, theoretical guarantees for the algorithmic solutions presented here, and experimental evaluation of the approach's success in practice and the quality of the produced parallel programs.

On the reduction modulo $p$ of Mahler equations

The guiding thread of the present work is the following result, in the vain of Grothendieck's conjecture for differential equations : if the reduction modulo almost all prime $p$ of a given linear Mahler equation with coefficients in $\mathbb{Q}(z)$ has a full set of algebraic solutions, then this equation has a full set of rational solutions. The proof of this result, given at the very end of the paper, relies on intermediate results of independent interest about Mahler equations in characteristic zero as well as in positive characteristic.

Asymmetry-aware work-stealing runtimes

Amdahl's law provides architects a compelling reason to introduce system asymmetry to optimize for both serial and parallel regions of execution. Asymmetry in a multicore processor can arise statically (e.g., from core microarchitecture) or dynamically (e.g., applying dynamic voltage/frequency scaling). Work stealing is an increasingly popular approach to task distribution that elegantly balances task-based parallelism across multiple worker threads. In this paper, we propose asymmetry-aware work-stealing (AAWS) runtimes, which are carefully designed to exploit both the static and dynamic asymmetry in modern systems. AAWS runtimes use three key hardware/software techniques: work-pacing, work-sprinting, and work-mugging. Work-pacing and work-sprinting are novel techniques that combine a marginal-utility-based approach with integrated voltage regulators to improve performance and energy efficiency in high- and low-parallel regions. Work-mugging is a previously proposed technique that enables a waiting big core to preemptively migrate work from a busy little core. We propose a simple implementation of work-mugging based on lightweight user-level interrupts. We use a vertically integrated research methodology spanning software, architecture, and VLSI to make the case that holistically combining static asymmetry, dynamic asymmetry, and work-stealing runtimes can improve both performance and energy efficiency in future multicore systems.

Market mechanism design for profitable on-demand transport services

On-demand transport services in the form of dial-a-ride and taxis are crucial parts of the transport infrastructure in all major cities. However, not all on-demand transport services are equal: not-for-profit dial-a-ride services with coordinated drivers significantly differ from profit-motivated taxi services with uncoordinated drivers. In fact, there are two key threads of work on efficient scheduling, routing, and pricing for passengers: dial-a-ride services; and taxi services. Unfortunately, there has been only limited development of algorithms for joint optimization of scheduling, routing, and pricing; largely due to the widespread assumption of fixed pricing. In this paper, we introduce another thread: profit-motivated on-demand transport services with coordinated drivers. To maximize provider profits and the efficiency of the service, we propose a new market mechanism for this new thread of on-demand transport services, where passengers negotiate with the service provider. In contrast to previous work, our mechanism jointly optimizes scheduling, routing, and pricing. Ultimately, we demonstrate that our approach can lead to higher profits and reduced passenger prices, compared with standard fixed price approaches, while also improving efficiency.

Parallelizing query optimization on shared-nothing architectures

Data processing systems offer an ever increasing degree of parallelism on the levels of cores, CPUs, and processing nodes. Query optimization must exploit high degrees of parallelism in order not to gradually become the bottleneck of query evaluation. We show how to parallelize query optimization at a massive scale. We present algorithms for parallel query optimization in left-deep and bushy plan spaces. At optimization start, we divide the plan space for a given query into partitions of equal size that are explored in parallel by worker nodes. At the end of optimization, each worker returns the optimal plan in its partition to the master which determines the globally optimal plan from the partition-optimal plans. No synchronization or data exchange is required during the actual optimization phase. The amount of data sent over the network, at the start and at the end of optimization, as well as the complexity of serial steps within our algorithms increase only linearly in the number of workers and in the query size. The time and space complexity of optimization within one partition decreases uniformly in the number of workers. We parallelize single- and multi-objective query optimization over a cluster with 100 nodes in our experiments, using more than 250 concurrent worker threads (Spark executors). Despite high network latency and task assignment overheads, parallelization yields speedups of up to one order of magnitude for large queries whose optimization takes minutes on a single node.

Finite Element Research of Rubber-cord Flat Coupling

The finite-element research of the intense deformed condition of the highly elastic flat coupling at the shaft axial offset and twisting small angles is presented in the paper. The rubber-cord disk modeling which is a working coupling element was carried out with the use of the program Ansys complex in the assumption that threads of a cord work only for stretching independently of each other. The rubber layers contribution is not considered. The сomparison of the calculation results with the available experimental data has confirmed the legitimacy of the accepted simplifications.

A Hybrid Parallel Delaunay Image-to-mesh Conversion Algorithm Scalable on Distributed-memory Clusters

In this paper, we present a scalable three dimensional hybrid MPI+Threads parallel Delaunay image-to-mesh conversion algorithm. A nested master-worker communication model for parallel mesh generation is implemented which simultaneously explores process-level parallelization and thread-level parallelization: inter-node communication using MPI and inter-core communication inside one node using threads. In order to overlap the communication (task request and data movement) and computation (parallel mesh refinement), the inter-node MPI communication and intra-node local mesh refinement is separated. The master thread that initializes the MPI environment is in charge of the inter-node MPI communication while the worker threads of each process are only responsible for the local mesh refinement within the node. We conducted a set of experiments to test the performance of the algorithm on Turing, a distributed memory cluster at Old Dominion University High Performance Computing Center and observed that the granularity of coarse level data decomposition, which affects the coarse level concurrency, has a significant influence on the performance of the algorithm. With the proper value of granularity, the algorithm expresses impressive performance potential and is scalable to 30 distributed memory compute nodes with 20 cores each (the maximum number of nodes available for us in the experiments).

Fault Tolerant Scheduling for Parallel Loops on Shared Memory Systems

While multicore/multiprocessor systems achieve significant speedup for many applications by exploiting loop level parallelism, they also suffer from increased reliability problems as a result of ever scaling device size. This paper addresses the reliability of loop dominated applications, aiming to execute parallel loops efficiently in the presence of various types of hardware faults. In this paper, we present a fault tolerant work-stealing scheme which makes parallel loop execution resilient to hardware faults. A lightweight buffer-commit mechanism is applied in the proposed scheme to ensure the correctness of the re-execution of loop iterations. In addition, we split large failing chunks of loop iterations at runtime to improve load balancing, and a worker thread is discarded when faults occur frequently on it. We evaluated our techniques on a multi-socket multicore system, using a set of loop dominated benchmarks. The proposed scheme achieves the minimum overhead of supporting fault tolerance and optimal load balancing.

An Adaptive Task Creation Pruning Strategy for Optimizing Irregular Applications

The Open MP task directive makes it possible to efficiently parallelize irregular applications, with task granularity as one of the most critical issues. To implement Open MP specification on multi-core architecture, a model is presented specializing in the execution of irregular applications. The model captures computation and communication within a node with host cores and accelerator cores. Based on this model, we propose an adaptive task creation pruning strategy including two stages to adjust dynamically task granularity. The first stage is task creation in breadth-first manner until getting to a threshold, which utilizes potential parallelism of multi-core processor. The second stage is starvation-triggered task regenerationonce some worker thread becomes starved, which ensures work-stealing and thus achieves load balance. The evaluation is conducted with a series of typical irregular benchmarks, and the results indicate that our approach offers more effective performance in parallel execution of irregular benchmarks.

Concurrent Bounded Model Checking

We introduce a methodology, based on symbolic execution, for Concurrent Bounded Model Checking. In our approach, we translate a program into a formula in a disjunctive form. This design enables concurrent verification, with a main thread running symbolic execution, without any constraint solving, to build subformulas, and a set of worker threads running a decision procedure for satisfiability checks. We have implemented this methodology in a tool called JCBMC, the first bounded model checker for Java. JCBMC is built as an extension of Java Pathfinder, an open-source verification platform developed by NASA. JCBMC uses Symbolic PathFinder (SPF) for the symbolic execution, Z3 as the solver and implements concurrency with multi-threading. For evaluation, we compare JCBMC against SPF and the Bounded Model Checker CBMC. The results of the experiments show that we can achieve significant advantages of performance over these two tools.

Dynamic macroblock wavefront parallelism for parallel video coding

Wavefront parallelism is effective for parallel video encoding thanks to its merits of low latency, no quality loss and high degree of parallelism. In traditional video encoders, macroblock row wavefront (MRW) parallelism was widely adopted. However the performance of MRW is limited by workload unbalance and computing resource unbalance among multiple work threads. This paper proposes a new dynamic macroblock wavefront (DMW) parallelism to alleviate the limitations of MRW. In DMW, the available macroblocks (MBs) are scheduled to work threads MB by MB other than MB row by MB row; and after one MB is encoded by a work thread, the MB on its right (if available) has the highest priority to be scheduled to avoid synchronization delay. Experimental results demonstrate that video encoder with traditional MRW can be accelerated by more than 10% with our proposed DMW. When hyper-threading is used, the advantage of DMW is more prominent.

How do interruptions affect clinician performance in healthcare? Negotiating fidelity, control, and potential generalizability in the search for answers

Interruptions and distractions are a feature of work in most complex sociotechnical systems in which people must handle multiple threads of work. Over the last 10–15 years there has been a crescendo of reviews and investigations into the impact that interruptions and distractions have on safety-critical aspects of healthcare work, such as medication administration, but findings are still inconclusive. Despite this, many healthcare communities have taken steps to reduce interruptions and distractions in safety-critical work tasks, a step that will usually do no harm but that may have unintended consequences. Investigations with a higher yield of certainty would provide better evidence and better guidance to healthcare communities. In this viewpoint paper we survey some key papers reporting investigations of interruptions and distractions in the field, in simulators, and in the laboratory. We also survey reports of field interventions aimed at minimizing interruptions and distractions with the intention of improving the safety of medication administration and other safety-critical healthcare tasks. To analyse the papers adopting each form of investigation, we use the three dimensions of fidelity, formal control exercised, and the potential generalizability to the field. We argue that studies of interruptions and distractions outside the healthcare clinical context, but intended to generalize to it, should become more formally representative of the cognitive context of healthcare work. Research would be improved if investigators undertook programs of studies that successively achieve fidelity, control, and potential generalizability, or if they strengthened the design of individual studies.

Massive atomics for massive parallelism on GPUs

One important type of parallelism exploited in many applications is reduction type parallelism. In these applications, the order of the read-modify-write updates to one shared data object can be arbitrary as long as there is an imposed order for the read-modify-write updates. The typical way to parallelize these types of applications is to first let every individual thread perform local computation and save the results in thread-private data objects, and then merge the results from all worker threads in the reduction stage. All applications that fit into the map reduce framework belong to this category. Additionally, the machine learning, data mining, numerical analysis and scientific simulation applications may also benefit from reduction type parallelism. However, the parallelization scheme via the usage of thread-private data objects may not be vi- able in massively parallel GPU applications. Because the number of concurrent threads is extremely large (at least tens of thousands of), thread-private data object creation may lead to memory space explosion problems. In this paper, we propose a novel approach to deal with shared data object management for reduction type parallelism on GPUs. Our approach exploits fine-grained parallelism while at the same time maintaining good programmability. It is based on the usage of intrinsic hardware atomic instructions. Atomic operation may appear to be expensive since it causes thread serialization when multiple threads atomically update the same memory object at the same time. However, we discovered that, with appropriate atomic collision reduction techniques, the atomic implementation can out- perform the non-atomics implementation, even for benchmarks known to have high performance non-atomics GPU implementations. In the meantime, the usage of atomics can greatly reduce coding complexity as neither thread-private object management or explicit thread-communication (for the shared data objects protected by atomic operations) is necessary.

NDL-v2.0: A new version of the numerical differentiation library for parallel architectures

NDL-v2.0: A new version of the numerical differentiation library for parallel architectures

Garment homework in Argentina: Drawing together the threads of informal and precarious work

AbstractThis article explores and applies Kalleberg’s concept and dimensions of precarious work in relation to garment homework in Argentina. Although precarious work exists across formal and informal employment, its nature and dimensions are most commonly researched in relation to formal work in developed economies where the loss of standard conditions can be documented. Similarly, homework is most usually discussed as a category of informal work, in the context of developing countries, within which precariousness is one among numerous aspects of adverse job quality. Applying the concept of precariousness enables homework to be assessed systematically against specific labour standards, yielding a more powerful analysis than reference to a general deficit. This may increase our understanding of homework especially with regard to addressing labour standards.

DEFINITION OF DYNAMIC INDUCTANCES OF THE ASYNCHRONOUS ENGINE WITH REGARD TO PROCESSES OF SATURATION

Development of mathematical model of asynchronous machines in (d, q) coordinate system with taking into account the saturation of magnetic circuits is performed. In the paper, we used the method of dynamic inductances, which is connected with the use of a systematic study of motor properties. An analytical model of asynchronous motor magnetic circuit, which is taken into account action of the tangential and radial dynamic inductances is created. On the basis of this the sensor dynamic inductances of rich machine, which was recorded in (d, q) coordinate system of equations of rotor and stator circles of asynchronous motor with the saturation were built. The author suggests to use the method of dynamic inductances at construction of asynchronous engine mathematical model, that allows to take into account the saturation of the main magnetic path for the modes with a wide range of changes in the flow of mutual induction between the stator and the rotor, saturation flow path, the scattering modes that are characterized by large current circuits of the machine, induction, due to processes of saturation, between mutually perpendicular to the contours of the machine, as well as the joint saturation worker thread and threads scattering modes that are characterized by the significant size of the workflow and large values of currents contours of the machine. The mathematical model allows to take into account the change of the magnetic state of asynchronous motor as necessity to build adequate systems of control drives and driving