Loop Scheduling Research Articles

A NUMA-Aware Version of an Adaptive Self-Scheduling Loop Scheduler

A NUMA-Aware Version of an Adaptive Self-Scheduling Loop Scheduler

Iterative Trajectory Planning and Resource Allocation for UAV-Assisted Emergency Communication with User Dynamics

The demand for air-to-ground communication has surged in recent years, underscoring the significance of unmanned aerial vehicles (UAVs) in enhancing mobile communication, particularly in emergency scenarios due to their deployment efficiency and flexibility. In situations such as emergency cases, UAVs can function as efficient temporary aerial base stations and enhance communication quality in instances where terrestrial base stations are incapacitated. Trajectory planning and resource allocation of UAVs continue to be vital techniques, while a relatively limited number of algorithms account for the dynamics of ground users. This paper focuses on emergency communication scenarios such as earthquakes, proposing an innovative path planning and resource allocation algorithm. The algorithm leverages a multi-stage subtask iteration approach, inspired by the block coordinate descent technique, to address the challenges presented in such critical environments. In this study, we establish an air-to-ground communication model, subsequently devising a strategy for user dynamics. This is followed by the introduction of a joint scheduling process for path and resource allocation, named ISATR (iterative scheduling algorithm of trajectory and resource). This process encompasses highly interdependent decision variables, such as location, bandwidth, and power resources. For mobile ground users, we employ the cellular automata (CA) method to forecast the evacuation trajectory. This algorithm successfully maintains data communication in the emergency-stricken area and enhances the communication quality through bandwidth division and power control which varies with time. The effectiveness of our algorithm is validated by evaluating the average throughput with different parameters in various simulation conditions and by using several heuristic methods as a contrast.

Optimization of the Cost of Repetitive Construction Projects Using Computerized Iteration Method

This study introduces an iterative scheduling method that combines two approaches for managing repetitive construction projects: the Critical Path Method (CPM) and the Repetitive Scheduling Method (RSM). The primary objective of this study is to demonstrate how optimization techniques can be applied to minimize the cost of construction projects within a defined range, spanning from the shortest to the longest possible project durations. In the shortest project duration (as determined by CPM), all activities are allocated idle times based on precedence constraints, while in the longest duration (as determined by RSM), there is no idle time allocated. To calculate the optimal schedule, a computerized iterative method specifically designed for this purpose considers all possible combinations of activities with and without idle time. The optimum schedule is the one that minimizes the total project cost. The study reveals that by using an Excel spreadsheet, it is feasible to deterministically optimize the cost of repetitive construction projects, achieving the minimum cost. This minimization process can also be implemented as a Python application. Notably, this proposed system provides multiple optimal solutions, enabling managers to select the most suitable one. This advantage distin-guishes it from conventional methods, such as genetic algorithms and other optimization techniques. However, there are some limitations when applying this application, one of which is the maximum capacity available to run the application.

Study on the Development Path of Rural Cultural Industry under the Perspective of Rural Revitalization

Abstract Based on the theory of systematics and synergy, this paper measures the degree of coordination within the system by measuring the system order parameters that determine the phase transition characteristics and laws of the system and establishes a model of the coordination degree of the composite system that can be realistically calculated. This paper sorts out the main constraints on the coordinated development of rural cultural industries, constructs an index system to evaluate the level of coordinated development of regional cultural industries, and introduces it into the composite system coordination model to measure the synergy degree of rural cultural industries. Starting from the interaction effect between the digital cultural industry and rural revitalization, this paper designs the research on the digital cultural industry from the perspective of rural revitalization and uses data analysis software to analyze the development path of the rural cultural industry. The results show that the iterative scheduling strategy achieves the best scheduling objective function in each test set, with values of 154.62, 287.074, 360.262 and 348.408, respectively. That is, the iterative scheduling strategy can better complete the passenger flow scheduling. In 2012 and 2019, the ‘ Moran sI values of rural cultural industry efficiency were 0.1752 and 0.3094, respectively. That is, the technical efficiency of rural cultural industries from the perspective of rural revitalization has a significant spatial dependence on geographical space and has a positive autocorrelation relationship. This study analyzes the dilemmas faced by the development of rural cultural industries and proposes practical ways to solve these issues.

Reuse-based online joint routing and scheduling optimization mechanism in deterministic networks

Deterministic networks plan the entire network traffic and calculate the scheduling time to meet the critical traffic requirements of specific domains, enabling real-time and deterministic interaction of massive data. However, in dynamic industrial automation scenarios where devices undergo changes, existing mechanisms face challenges in quickly responding to dynamic transmission demand changes caused by rapid traffic migration. To address this issue, this paper proposes a reuse-based online scheduling mechanism that utilizes dynamic path planning of flows and coordinated scheduling of time slots to achieve deterministic transmission of dynamic flows. In the offline phase, the mechanism proposes a backbone link selection and a scalable intelligent routing strategy, constructs a set of routing and scheduling co-design constraints, and generates an offline scheduling table using an iterative scheduling algorithm. In the online scheduling phase, a reuse-based online scheduling algorithm is proposed to achieve rapid scheduling and deterministic transmission of dynamic real-time flows. It utilizes the offline scheduling results and the period offset of migrated flows. The reuse of offline scheduling results reduces computation time and expands the solution space. Experimental results demonstrate that the proposed mechanism achieves a maximum increase in scheduling success rate of 37.3% and reduces time costs by up to 66.6% compared to existing online scheduling algorithms.

Joint Scheduling of Participants, Local Iterations, and Radio Resources for Fair Federated Learning over Mobile Edge Networks

During the training process of Federated learning (FL), proper devices are selected to participate in the training process to avoid model unfairness. In a mobile edge network, participant selection must be considered together with three factors: non-iid datasets possessed by devices, tunable local iterations on devices, and radio resource allocation to counter the impact of time-varying channel conditions on parameter transmissions. Since datasets of devices are given, to ensure model fairness and achieve fast convergence in the FL training process, participants, local iterations, and radio resources must be scheduled jointly in each iteration of FL training. In this paper, the joint scheduling problem is analyzed and formulated. Since it is NP-hard, a heuristic scheduling method called PALORA is designed to conduct joint scheduling of participants, local iterations, and radio resources. PALORA consists of three sequentially interactive function blocks: 1) a pointer network embedded deep reinforcement learning method to select participants, 2) an estimation algorithm to determine the numbers of local iterations, and 3) a breadth-first search method to allocate radio resources to the selected participants. PALORA is evaluated via extensive simulations based on real-world datasets. Results show that it significantly outperforms benchmark approaches.

Experimental Survey of FPGA-Based Monolithic Switches and a Novel Queue Balancer

This paper studies small to medium-sized monolithic switches for FPGA implementation and presents a novel switch design that achieves high algorithmic performance and FPGA implementation efficiency. Crossbar switches based on virtual output queues (VOQs) and variations have been rather popular for implementing switches on FPGAs, with applications in network switches, memory interconnects, network-on-chip (NoC) routers etc. The implementation efficiency of crossbar-based switches is well-documented on ASICs, though we show that their disadvantages can outweigh their advantages on FPGAs. One of the most important challenges in such input-queued switches is the requirement for iterative scheduling algorithms. In contrast to ASICs, this is more harmful on FPGAs, as the reduced operating frequency and narrower packets cannot “hide” multiple iterations of scheduling that are required to achieve a modest scheduling performance.Our proposed design uses an output-queued switch internally for simplifying scheduling, and a queue balancing technique to avoid queue fragmentation and reduce the need for memory-sharing VOQs. Its implementation approaches the scheduling performance of a state-of-the-art FPGA-based switch, while requiring considerably fewer resources.

A Novel Fully Hardware-Implemented SVD Solver Based on Ultra-Parallel BCV Jacobi Algorithm

Efficient FPGA-based floating-point singular value decomposition (SVD) is challenging for its enormous complexity with the rapid growth of the matrix dimension. Numerous hardware architectures have been proposed to improve the performance of SVD by increasing capacity of computation units, reusing data, and enhancing bandwidth. These designs, however, are not optimum due to their low parallelism, poor data access efficiency, and inferior iterations scheduling. In this express, we propose a block column vector Hestenes-Jacobi (BCV Jacobi) algorithm that decomposes an arbitrary large matrix into several blocks, enhances the access efficiency by customizing the distinctive data structure, and improves the system-level parallelism by simplifying the iteration scheduling. The proposed BCV Jacobi algorithm also achieves better scalability and efficiency. Experimental results show that the performance of the proposed FPGA based SVD processor is superior to other SVD implementations in terms of parallelism, data access efficiency, matrix size, and execution time. When compared with state of the art SVD accelerator engine, the proposed algorithm speeds up the runtime over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2{\times }$ </tex-math></inline-formula> on average.

Consensus-based dispatch optimization of a microgrid considering meta-heuristic-based demand response scheduling and network packet loss characterization

The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization.

Quality-driven video streaming for ultra-dense OFDMA heterogeneous networks

The explosive growth of mobile video traffic has brought enormous traffic pressure to the existing bandwidth-limited cellular network. Ultra-dense heterogeneous networks (HetNets) are deemed as one of the most promising 5G technologies for increasing the cellular network capacity. In this paper, we study quality-driven multiuser video streaming algorithm for two-tier orthogonal frequency division multiple access (OFDMA) HetNets. Specifically, we formulate a stochastic optimization problem for the quality-driven multiuser video streaming in two-tier OFDMA HetNets to maximize the average perceived video quality of small cell users while meeting the average data rate demand of macrocell users. We then decompose the original stochastic problem into a sequence of per-slot deterministic subproblems of adaptive bitrate selection and radio resource management which have no requirement of prior channel knowledge by employing Lyapunov optimization technique. We accordingly propose an online quality-driven video streaming algorithm. In this algorithm, we derive the optimal bitrate decision for the adaptive bitrate selection subproblem, and design an iterative scheduling algorithm of joint subchannel assignment and power control by utilizing alternating optimization and successive convex approximation techniques to solve the radio management subproblem. Extensive simulation results validate the high efficiency of the proposed quality-driven video streaming algorithm in OFDMA HetNets.

Learning-Based Scalable Scheduling and Routing Co-Design With Stream Similarity Partitioning for Time-Sensitive Networking

The deterministic and real-time communication is the indispensable requirement in Industrial Internet of Things (IIoT) application areas. Time-sensitive networking (TSN) is a promising technology for this kind of communication demands through designing proper scheduling and routing mechanisms. However, it is still challenging to design the mechanisms for large-scale instances due to high computational complexity. In order to guarantee schedulability and scalability, a learning-based scalable scheduling and routing co-design (LSSR) architecture is proposed in this article for TSN. A stream partition method combining classification and graph-based clustering is established to reduce interpartition conflicts to enhance schedulability based on the explored domain knowledge and the characterized stream data set for practical requirements. Integrated with the stream partition method, we construct the constraints of scheduling and routing co-design to guarantee the deterministic and real-time transmission. An iterative scheduling algorithm is proposed to reduce the computational complexity and thus, to enhance scalability. Simulations demonstrate the effectiveness and advantages of the proposed LSSR scheme.

Problem Decomposition and Multi-shot ASP Solving for Job-shop Scheduling

AbstractScheduling methods are important for effective production and logistics management, where tasks need to be allocated and performed with limited resources. In particular, the Job-shop Scheduling Problem (JSP) is a well known and challenging combinatorial optimization problem in which tasks sharing a machine are to be arranged in a sequence such that encompassing jobs can be completed as early as possible. Given that already moderately sized JSP instances can be highly combinatorial, and neither optimal schedules nor the runtime to termination of complete optimization methods is known, efficient approaches to approximate good-quality schedules are of interest. In this paper, we propose problem decomposition into time windows whose operations can be successively scheduled and optimized by means of multi-shot Answer Set Programming (ASP) solving. From a computational perspective, decomposition aims to split highly complex scheduling tasks into better manageable subproblems with a balanced number of operations so that good-quality or even optimal partial solutions can be reliably found in a small fraction of runtime. Regarding the feasibility and quality of solutions, problem decomposition must respect the precedence of operations within their jobs and partial schedules optimized by time windows should yield better global solutions than obtainable in similar runtime on the entire instance. We devise and investigate a variety of decomposition strategies in terms of the number and size of time windows as well as heuristics for choosing their operations. Moreover, we incorporate time window overlapping and compression techniques into the iterative scheduling process to counteract window-wise optimization limitations restricted to partial schedules. Our experiments on JSP benchmark sets of several sizes show that successive optimization by multi-shot ASP solving leads to substantially better schedules within the runtime limit than global optimization on the full problem, where the gap increases with the number of operations to schedule. While the obtained solution quality still remains behind a state-of-the-art Constraint Programming system, our multi-shot solving approach comes closer the larger the instance size, demonstrating good scalability by problem decomposition.

LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications

Exascale computing systems will exhibit high degrees of hierarchical parallelism, with thousands of computing nodes and hundreds of cores per node. Efficiently exploiting hierarchical parallelism is challenging due to load imbalance that arises at multiple levels. OpenMP is the most widely-used standard for expressing and exploiting the ever-increasing node-level parallelism. The scheduling options in OpenMP are insufficient to address the load imbalance that arises during the execution of multithreaded applications. The limited scheduling options in OpenMP hinder research on novel scheduling techniques which require comparison with others from the literature. This work introduces LB4OMP, an open-source dynamic load balancing library that implements successful scheduling algorithms from the literature. LB4OMP is a research infrastructure designed to spur and support present and future scheduling research, for the benefit of multithreaded applications performance. Through an extensive performance analysis campaign, we assess the effectiveness and demystify the performance of all loop scheduling techniques in the library. We show that, for numerous applications-systems pairs, the scheduling techniques in LB4OMP outperform the scheduling options in OpenMP. Node-level load balancing using LB4OMP leads to reduced cross-node load imbalance and to improved MPI+OpenMP applications performance, which is critical for Exascale computing.

Iterative Dynamic Critical Path Scheduling: An Efficient Technique for Offloading Task Graphs in Mobile Edge Computing

Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, we investigate the problem of offloading task graphs in edge computing scenarios. Previous work based on list-scheduling heuristics is likely to suffer from severe processor time wastage due to intricate task dependencies and data transfer requirements. To this end, we propose a novel offloading algorithm, referred to as Iterative Dynamic Critical Path Scheduling (IDCP). IDCP minimizes the makespan by iteratively migrating tasks to keep shortening the dynamic critical path. Through IDCP, what is managed are essentially the sequences among tasks, including task dependencies and scheduled sequences on processors. Since we only schedule sequences here, the actual start time of each task is not fixed during the scheduling process, which effectively helps to avoid unfavorable schedules. Such flexibilities also offer us much space for continuous scheduling optimizations. Our experimental results show that our algorithm significantly outperforms existing list-scheduling heuristics in various scenarios, which demonstrates the effectiveness and competitiveness of our algorithm.

Endometriosis in the Mouse: Challenges and Progress Toward a 'Best Fit' Murine Model.

Endometriosis is a prevalent gynecologic condition associated with pelvic pain and infertility characterized by the implantation and growth of endometrial tissue displaced into the pelvis via retrograde menstruation. The mouse is a molecularly well-annotated and cost-efficient species for modeling human disease in the therapeutic discovery pipeline. However, as a non-menstrual species with a closed tubo-ovarian junction, the mouse poses inherent challenges as a preclinical model for endometriosis research. Over the past three decades, numerous murine models of endometriosis have been described with varying degrees of fidelity in recapitulating the essential pathophysiologic features of the human disease. We conducted a search of the peer-reviewed literature to identify publications describing preclinical research using a murine model of endometriosis. Each model was reviewed according to a panel of ideal model parameters founded on the current understanding of endometriosis pathophysiology. Evaluated parameters included method of transplantation, cycle phase and type of tissue transplanted, recipient immune/ovarian status, iterative schedule of transplantation, and option for longitudinal lesion assessment. Though challenges remain, more recent models have incorporated innovative technical approaches such as in vivo fluorescence imaging and novel hormonal preparations to overcome the unique challenges posed by murine anatomy and physiology. These models offer significant advantages in lesion development and readout toward a high-fidelity mouse model for translational research in endometriosis.

A Compressed Data Partition and Loop Scheduling Scheme for Neural Networks

Neural networks (NNs) have been widely adopted in various application domains, ranging from image and video recognition to natural language processing. Recent studies reveal that deeper NNs with more parameters greatly enhance the output accuracy. However, complex NNs incur intensive memory accesses. Since the weights of even a single layer can exceed the on-chip storage capacity, the data usually need to be partitioned. Compression can effectively reduce the storage space requirements. However, there is no research considering the partition of the spare matrix. In this paper, we propose a sparse NN data partition and loop scheduling scheme. We establish the compression efficiency model of the matrix sparse algorithm and design a partition selection method based on sparsity characteristics analyzed by the compression efficiency model. Finally, we design a loop scheduling scheme based on the proper partition size. The experiment results show that the average memory access of each layer can be compressed to 68% of the original; additionally, the throughput of the three networks is increased to an average of 1.66 times.

An adaptive self‐scheduling loop scheduler

AbstractMany shared‐memory parallel irregular applications, such as sparse linear algebra and graph algorithms, depend on efficient loop scheduling (LS) in a fork‐join manner despite that the work per loop iteration can greatly vary depending on the application and the input. Because of the importance of LS, many different methods (e.g., workload‐aware self‐scheduling) and parameters (e.g., chunk size) have been explored to achieve reasonable performance, and many of these methods require expert prior knowledge about the application and input before runtime. This work proposes a new LS method that requires little to no expert knowledge to achieve speedups close to those of tuned LS methods by self‐managing chunk size based on a heuristic of throughput and using work‐stealing to recover from workload imbalances. This method, named iCh, is implemented into libgomp for testing. It is evaluated against OpenMP's guided, dynamic, and taskloop methods and is evaluated against BinLPT and generic work‐stealing on an array of applications that includes: a synthetic benchmark, breadth‐first search, K‐Means, the molecular dynamics code LavaMD, and sparse matrix‐vector multiplication. On a 28 thread Intel system, iCh is the only method to always be one of the top three LS methods. On average across all applications, iCh is within of the best method and is even able to outperform other LS methods for breadth‐first search and K‐Means.

Hipernetch: High-Performance FPGA Network Switch

We present Hipernetch, a novel FPGA-based design for performing high-bandwidth network switching. FPGAs have recently become more popular in data centers due to their promising capabilities for a wide range of applications. With the recent surge in transceiver bandwidth, they could further benefit the implementation and refinement of network switches used in data centers. Hipernetch replaces the crossbar with a “combined parallel round-robin arbiter”. Unlike a crossbar, the combined parallel round-robin arbiter is easy to pipeline, and does not require centralised iterative scheduling algorithms that try to fit too many steps in a single or a few FPGA cycles. The result is a network switch implementation on FPGAs operating at a high frequency and with a low port-to-port latency. Our proposed Hipernetch architecture additionally provides a competitive switching performance approaching output-queued crossbar switches. Our implemented Hipernetch designs exhibit a throughput that exceeds 100 Gbps per port for switches of up to 16 ports, reaching an aggregate throughput of around 1.7 Tbps.

The EU DEMO staged design approach in the Pre-Concept Design Phase

This paper provides an overview of the methodology adopted within the EUROfusion framework covering the Pre-Concept Design Phase (2014–2020). The phase activities focused on advancing the technical basis of the design of the European DEMOnstration Fusion Power Plant (DEMO). The emphasis in the latter stages was placed upon an integrated design approach, based on systems engineering, providing a clear direction for urgent technology R&D and addressing the key design integration issues by assessing the critical systems interdependencies and uncertainties of certain design assumptions. The programme has continued to benefit from the ITER return of experience, in terms of design, licensing, and construction and DEMO will maintain a strong link with the ITER schedule in the coming design phases. The growing need for a structured and transparent review process gave rise to the introduction of a phase-gate review process, the implementation of which led to the first formal DEMO-wide gate review held at the end of the Pre-Concept Design Phase. The Gate Panel's recommendations provided clarity on the key priorities for the programme moving forward and serve to underpin the planning for the next phase.

TEA-SEA: Tiling and scheduling of non-uniform two-level perfectly nested loops using an evolutionary approach

Nowadays, in scientific and computational programs, increasing the execution speed of programs is very important. In implementing scientific programs, loops are dedicating a large part of time to themselves. Therefore, the parallel execution of the iteration loop reduces the runtime of all programs. This paper has been working on the parallelism of two-level perfectly nested loops with non-uniform dependencies and rectangular iteration space, and then their scheduling is based on the scheduling of task graphs. For this purpose, the iteration space of non-uniform loop is partitioned using a tiling transformation, and then the tiled space is modelled as a task, graph for scheduling. In the task graph, vertices expressive tiles and edges expressive data dependencies inter-tiles. It should be noted that the computational load of vertices is not the same, and the vertices corresponding imperfect tiles have a less computational load. The scheduling is done on both types of homogeneous and heterogeneous multiprocessor systems. The tiling of iteration space loop and the scheduling of task graph is arranged in the category of NP-Hard problems that using evolutionary algorithms can be efficient to discover a suitable solution for them. The results of the proposed approach show the performance of this method better than previous methods in the tiling and scheduling of non-uniform loops. According to the examinations performed on uniform loops, this algorithm also performs better than previous algorithms on these types of loops.