Unifying Fixed Code Mapping, Communication, Synchronization and Scheduling Algorithms for Efficient and Scalable Loop Pipelining

This paper analyzes the impact of dynamic task scheduling, processing element allocation and data transfer management on system performance of heterogeneous MPSoCs. Therefore, all parts of a runtime scheduling unit are analyzed. Bottlenecks are identified and their complexity evaluated. Furthermore, traced information is processed with two newly introduced tools. The first one generates an annotated SDF3 file. The second one creates a static schedule which applies the same scheduling and allocation decision as the dynamic scheduler. The execution of the static schedule reduces the burden of task management to a minimum. The resulting static execution is compared with the execution of the dynamic schedule. Hence, runtime overhead of dynamic scheduling is unveiled.

Energy minimization in dynamic train scheduling and control for metro rail operations

GPU based parallel genetic algorithm for solving an energy efficient dynamic flexible flow shop scheduling problem

In this paper, we study a simple question: when are dynamic relaying strategies essential in optimizing the diversity-multiplexing tradeoff (DMT) in half-duplex wireless relay networks? This is motivated by apparently two contrasting results even for a simple three-node network with a single half-duplex relay. When all channels in the system are assumed to be independent and identically fading, a static schedule where the relay listens half the time and transmits half the time combined with quantize-map-and-forward (QMF) relaying is known to achieve the full-duplex performance. However, when there is no direct link between the source and the destination, a dynamic decode-and-forward (DDF) strategy is needed to achieve the optimal tradeoff. In this case, a static schedule is strictly suboptimal and the optimal tradeoff is significantly worse than the full-duplex performance. In this paper, we study the general case when the direct link is neither as strong as the other links nor fully nonexistent, and identify regimes where dynamic schedules are necessary and those where static schedules are enough. We identify four qualitatively different regimes for the single-relay channel, where the tradeoff between diversity and multiplexing is significantly different. We show that in all these regimes one of the above two strategies is sufficient to achieve the optimal tradeoff by developing a new upper bound on the best achievable tradeoff under channel state information available only at the receivers. A natural next question is whether these two strategies are sufficient to achieve the DMT of more general half-duplex wireless networks with a larger number of relays. We propose a generalization of the two existing schemes through a dynamic QMF (DQMF) strategy, where the relay listens for a fraction of time depending on received channel state information but not long enough to be able to decode. We show that such a DQMF strategy is needed to achieve the optimal DMT in a parallel channel with two relays, outperforming both DDF and static QMF strategies.

In heterogeneous environments, dynamic scheduling algorithms are a powerful tool towards performance improvement of scientific applications via load balancing. However, these scheduling techniques employ heuristics that require prior knowledge about workload via profiling resulting in higher overhead as problem sizes and number of processors increase. In addition, load imbalance may appear only at run-time, making profiling work tedious and sometimes even obsolete. Recently, the integration of dynamic loop scheduling algorithms into a number of scientific applications has been proven effective. This paper reports on performance improvements obtained by integrating the Adaptive Weighted Factoring, a recently proposed dynamic loop scheduling technique that addresses these concerns, into two scientific applications: computational field simulation on unstructured grids, and N-Body simulations. Reported experimental results confirm the benefits of using this methodology, and emphasize its high potential for future integration into other scientific applications that exhibit substantial performance degradation due to load imbalance.

Uncountable loops (such as while loops in C) and if-conditions are some of the most common constructs in programming. While-loops are widely used to determine the convergence in linear algebra algorithms or goal finding problems from graph algorithms, to name a few. In general while-loops are used whenever the loop iteration space, the number of iterations a loop executes is unknown. Usually in while-loops, the execution of the next iteration is decided inside the current loop iteration (i.e. the execution of iteration i depends on the values computed in iteration i-1). This precludes their parallel execution in today's ubiquitous multi-core architectures. In this paper a technique to speculatively create parallel tasks from the next iterations before the current one completes is proposed. If consecutive loop-iterations are only control dependent, then multiple iterations can be executed simultaneously; later in the execution path, the runtime system will decide to either commit the results of such speculatively executed iterations or undo the changes made by them. Data dependences within or between non-speculative and speculative work are honored to guarantee correctness. The proposed technique is implemented in SMPSs, a task-based dataflow programming model for shared-memory multiprocessor architectures. The approach is evaluated on a set of applications from graph algorithms and linear algebra. Results are promising with an average increase in the speedup of 1.2x with 16 threads when compared to non speculative execution of the applications. The increase in the speedup is significant, since the performance gain is achieved over an already parallelized version of the benchmarks.

In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

A modified data dependency graph (DDG) for modeling loop-optimizing restrictions is proposed. By using it, pipelining, loop-carried data dependencies and folding of individual multicycle operations over iteration boundaries can be modeled. Properties of semantically correct software pipelining transformations are first defined in the acyclic DDG of the unrolled loop. The case when an operation in a loop body is dependent on that same operation in some previous iteration is analyzed in the acyclic DDG of the unrolled loop. The algorithm for finding a real machine optimal schedule is split into two independent tasks: finding an optimal acyclic data dependency graph of one loop body and then finding an optimal schedule for a real machine by implementing basic block methods. A method for loop optimizing named 'pipelining of strongly connected components' is proposed. It is proved that the method is semantically correct. The loop body optimization is converted into sub-tasks of optimizing individual strongly connected components of the modified data dependency graph of a loop. >

An intelligent scheduling algorithm based on multi-agents and genetic algorithm is proposed to overcome flexible job-shop automation and optimization problems in manufacturing industry. The algorithm is made of a management-agent, a scheduling-agent and machine-agents.Static scheduling is realized by a customized genetic algorithm,while dynamic scheduling is realized by these coordinative agents. Several scenarios are used to illustrate that the proposed system is practical, efficient and advanced.

Many computational solutions can be expressed as workflows. A Cluster of processors is a shared resource among several users and hence the need for a scheduler which deals with multi-user jobs presented as workflows. The scheduler must find the number of processors to be allotted for each workflow and schedule tasks on allotted processors. In this work, a new method to find optimal and maximum number of processors that can be allotted for a workflow is proposed. Regression analysis is used to find the best possible way to share available processors, among suitable number of submitted workflows. An instance of a scheduler is created for each workflow, which schedules tasks on the allotted processors. Towards this end, a new framework to receive online submission of workflows, to allot processors to each workflow and schedule tasks, is proposed and experimented using a discrete-event based simulator. This space-sharing of processors among multiple workflows shows better performance than the other methods found in literature. Because of space-sharing, an instance of a scheduler must be used for each workflow within the allotted processors. Since the number of processors for each workflow is known only during runtime, a static schedule can not be used. Hence a hybrid scheduler which tries to combine the advantages of static and dynamic scheduler is proposed. Thus the proposed framework is a promising solution to multiple workflows scheduling on cluster.

In this article, we study the dynamic scheduling problem of maintenance tasks in a distributed environment. This environment is presented as a center of competence related to several sites. The human resources have not the same qualification, and then, the assignment decision has to take care of several parameters principally with particular competences. Our contribution concerns the optimal insertion of a new task in an existing scheduling. The proposed method is based on a combination of a local decent algorithm and the Kangaroo methodology. The objective function is to minimize the following criteria: the total weighted tardiness, the standard deviation between resources and the number of task which could have a new assignment (assigned to a new resource). The main of the algorithm is to disturb as less as possible the existing scheduling. The obtained results are validated and compared to the results obtained by static scheduling.

Along with sharply increasing bandwidth requirements, modern network applications and new protocols demand highly intelligent and sophisticated processing over the network. Since these workloads require processing capability beyond state-of-the-art microprocessors, parallel architectures are required in order to handle the packet data without slowing down line speed. Network processors with various parallel architectures are appearing in the market, however, a thorough investigation of the implications of static versus dynamic scheduling of this class of emerging workloads has not been done. In this paper, we characterize the performance and power dissipation of statically identified ILP architectures, and we also compare them to dynamically scheduled architectures for network processing. In dynamically scheduled architectures, the power consumption of the instruction window greatly increases with increasing issue widths. On the other hand, statically scheduled architectures show better performance, as well as tremendous advantages in power, since they do not have instruction window wakeup/select, reorder buffer, and other scheduling related hardware modules. With the large parallelism and the loop nature of network applications, our experimental analysis supports static scheduling as an appropriate strategy for network processor applications.

Energy-aware workload distribution becomes crucial for extending the lifetime of wireless sensor networks (WSNs) in complex applications as those in Internet-of-Things or in-network DSP processing scenarios. Today static workload schedules are well understood, while dynamic schedules (i.e., with multiple partitions) remain unexplored. This paper models the dynamic scheduling by considering both the communication and computation energy consumption. It formulates a series of (integer) linear programming problems to characterize the optimal scheduling strategies. Surprisingly, even 2-partition scheduling can provide the maximum gains. Besides the interest to evaluate the optimality of on-line heuristics for dynamic scheduling, the reported off-line strategies can be immediately applied to WSN applications.