Balancing processor loads and exploiting data locality in N-body simulations

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.

Heterogeneous systems with multiple different compute devices have come into common use recently, and the heterogeneity of the compute device is mainly reflected in three aspects: hardware architecture, instruction set architecture, and processing capability. Heterogeneous CPU-accelerator systems have attracted increasing attention especially. To make full use of multiple CPUs and accelerators to execute data-parallel applications, programmers may need to manually map computation and data to all available compute devices, which is tedious, error-prone, and difficult. Especially for some data-parallel applications, the inter-device communication could easily become the performance bottleneck of multi-device co-execution. Therefore, firstly, a runtime system is designed for supporting heterogeneous cooperative execution (HCE) of data-parallel applications, which can help programmers to automatically and efficiently map computation and data to multiple compute devices. Secondly, an incremental data transfer method is designed to avoid redundant data transfers between devices, and a three-way overlapping communication optimization method based on software pipelining is designed to effectively hide the inter-device communication overhead. Based on our previously proposed feedback-based dynamic and elastic task scheduling (FDETS) scheme and asynchronous-based dynamic and elastic task scheduling (ADETS) scheme, the modified FDETS that supports incremental data transfer and the modified ADETS that supports three-way overlapping communication optimization are proposed, which not only can effectively partition and balance the workload among multiple compute devices but also can significantly reduce data transfer overhead between devices. Thirdly, a prototype of the proposed runtime system is implemented, which provides a set of runtime APIs for task scheduling, device management, memory management, and transfer optimization. Our experimental results show that the communication overhead between devices is greatly reduced using the proposed inter-device communication optimization methods and the multi-device co-execution significantly outperforms the best single-device execution.

With the rise of wireless power transfer technology, charging scheduling issue is prevalent in wireless rechargeable sensor networks (WRSNs). Most prior arts focused on two-dimensional (2-D) networks with homogeneous mobile chargers. However, three-dimensional (3-D) networks with collaborations among heterogeneous mobile chargers are more practical. In this paper, we consider 3-D networks in which wireless charging vehicles (WCVs) are employed with unmanned aerial vehicles (UAVs). To prolong network lifetime, we focus on device sleep time and energy usage and propose a 3-D Dynamic Collaborative Scheduling scheme (3DCS). Theoretical values of energy threshold and partition number are determined to assign charging tasks to chargers. Then, scheduling algorithms that include target selection, infeasibility test, and target update, are developed. In addition, a collaborative algorithm is developed to re-assign charging tasks from busy chargers toward their neighboring chargers to further improve charging efficiency. Test-bed experiments and extensive simulations reveal that, compared with several distinguished scheduling schemes, our scheme has a superior performance in charging throughput, energy efficiency, and other characteristics.

In the formulation of single process fault free intelligent RGV dynamic scheduling strategy, a double objective optimization model is established to improve the efficiency of RGV and shorten the production time of single material. For the solution of the model, firstly, the shortest moving path of RGV is determined according to Greedy algorithm and Tabu search algorithm, and the loading and unloading sequence of RGV is determined according to the short job priority scheduling algorithm. For intelligent RGV dynamic scheduling problem with single process failure, The failure time is determined by the cumulative failure probability model of the system, According to the period of the fault point, after determining the fault CNC, a new dynamic scheduling model is obtained according to the no fault scheduling strategy. Finally, the model and the algorithm are verified by the data.

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.

The Scheduling and mapping of tasks on a set of processors is considered as a critical problem in parallel and distributed computing system. This paper deals with the problem of dynamic scheduling on a special type of multiprocessor architecture known as Linear Crossed Cube (LCQ) network. This proposed multiprocessor is a hybrid network which combines the features of both linear types of architectures as well as cube based architectures. Two standard dynamic scheduling schemes namely Minimum Distance Scheduling (MDS) and Two Round Scheduling (TRS) schemes are implemented on the LCQ network. Parallel tasks are mapped and the imbalance of load is evaluated on different set of processors in LCQ network. The simulations results are evaluated and effort is made by means of through analysis of the results to obtain the best solution for the given network in term of load imbalance left and execution time. The other performance matrices like speedup and efficiency are also evaluated with the given dynamic algorithms.

In the Software Defined Networking (SDN) based Data Center Networks (DCN), load balancing is one of the research hotspots. This paper proposes a Dynamic Traffic Scheduling (DTS) strategy for load balancing taking the advantages of the SDN central controller. Considering the realtime network status, the paths can be adjusted during the flow transmission. A flow equilibrium degree is defined as the trigger of enabling DTS. The implementation process of DTS are also described in detail. An OpenFlow based SDN simulation platform is constructed. The experimental results indicate that DTS strategy generate better performance compared with other routing strategy without dynamic rerouting mechanism under various load.

Nowadays scheduling jobs on parallel computers or grid components in grid environment has becoming more and more important. Detailed knowledge of the individual user preference is a necessary factor to be taken into account for developing appropriate scheduling strategies. Other than minimizing makespan [1], workload and the balancing of resource utilization is also an important aspect for scheduling. In the premise of satisfying the preference of individual user, previous approaches mainly focused on minimizing makespan, however workload balancing is rarely optimized. Therefore some of resources may leave underused while other resources are overused. In this paper, a new dynamic scheduling scheme is proposed, using the packing and partial cloning, and the expected QoS can be obtained. This scheme has been further evaluated by simulations and shown to yield good results compared with the former studies.

GPUs are becoming prevalent in various domains of computing and are widely used for streaming (regular) applications. However, they are highly inefficient when executing irregular applications with unstructured inputs due to load imbalance. Dynamic parallelism (DP) is a new feature of emerging GPUs that allows new kernels to be generated and scheduled from the device-side (GPU) without the host-side (CPU) intervention to increase parallelism. To efficiently support DP, one of the major challenges is to saturate the GPU processing elements and provide them with the required data in a timely fashion. There have been considerable efforts focusing on exploiting data locality in GPUs. However, there is a lack of quantitative analysis of how irregular applications using dynamic parallelism behave in terms of data reuse. In this paper, we quantitatively analyze the data reuse of dynamic applications in three different granularities of schedulable units: kernel, work-group, and wavefront. We observe that, for DP applications, data reuse is highly irregular and is heavily dependent on the application and its input. Thus, existing techniques cannot exploit data reuse effectively for DP applications. To this end, we first conduct a limit study on the performance improvements that can be achieved by hardware schedulers that are provided with accurate data reuse information. This limit study shows that, on an average, the performance improves by 19.4% over the baseline scheduler. Based on the key observations from the quantitative analysis of our DP applications, we next propose LASER, a Locality-Aware SchedulER, where the hardware schedulers employ data reuse monitors to help make scheduling decisions to improve data locality at runtime. Our experimental results on 16 benchmarks show that LASER, on an average, can improve performance by 11.3%.

ABSTRACTMesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among proces-sors on a parallel machine. We present a novel method todynamically balance the processor workloads with a globalview. This paper presents, for the first time, the imple-mentation and integration of all major components withinour dynamic load balancing strategy for adaptive grid cal-culations. Mesh adaption, repartitioning, processor assign-ment, and remapping are critical components of the frame-work that must be accomplished rapidly and efficiently soas not to cause a significant overhead to the numerical sim-ulation. Previous results indicated that mesh repartitioningand data remapping are potential bottlenecks for perform-ing large-scale scientific calculations. We resolve these issuesand demonstrate that our framework remains viable on alarge number of processors.1 INTRODUCTIONDynamic mesh adaption on unstructured grids is a power-ful tool for computing unsteady three-dimensional problemsthat require grid modifications to efficiently resolve solu-tion features. By locally refining and coarsening the meshto capture flowfield phenomena of interest, such proceduresmake standard computational methods more cost effective.Highly refined meshes are required to accurately captureshock waves, contact discontinuities, vortices, and shear lay-ers. Local mesh adaption provides the opportunity to obtainsolutions that are comparable to those obtained on globally-refined grids but at a much lower cost.Unfortunately, the adaptive solution of unsteady prob-lems causes load imbalance among processors on a paral-lel machine. This is because the computational intensity isboth space and time dependent. An efficient parallel imple-mentation of such methods is extremely difficult to achieve,primarily because of the dynamically-changing nonuniformgrid. Various methods on dynamic load balancing have beenreported to date [5-9,11-14,16-18,24-26]; however, most ofthem either lack a global view of loads across processors orProceedings of the 9th ACM Symposium on Parallel Al-gorithms and Architectures, Newport, Rhode Island, June22-25, 1997.do not apply their techniques to realistic large-scale appli-cations.Figure 1 depicts our framework for parallel adaptive flowcomputation. It consists of a flow solver and a mesh adap-tor, with a partitioner and a remapper that load balancesand redistributes the computational mesh when necessary.Our goal is to build a portable system for efficiently per-forming adaptive large-scale flow calculations in a parallelmessage-passing environment. The mesh is first partitionedand mapped among the available processors. The flow solverthen runs for several iterations, updating solution variables.Once an acceptable solution is obtained, the mesh adaptionprocedure is invoked. It first targets edges for coarseningand refinement based on an error indicator computed fromthe flow solution. The old mesh is then coarsened, resultingin a smaller grid. Since edges have already been marked forrefinement, it is possible to exactly predict the new meshbefore actually performing the refinement step. Programcontrol is thus passed to the load balancer at this time. Aquick evaluation step determines if the new mesh will be sounbalanced as to warrant a repartitioning. If the currentpartitions will remain adequately load balanced, control mpassed back to the subdivision phase of the mesh adaptor.Otherwise, a repartitioning procedure is used to divide thenew mesh into subgrids. The new partitions are then reas-signed to the processors in a way that minimizes the costof data movement. If the remapping cost is less than thecomputational gain that would be achieved with balancedpartitions, all necessary data is appropriately redistributed.Otherwise, the new partitioning is discarded. The computa-tional mesh is then actually refined and the flow calculationis restarted.Notice from the framework in Fig. 1 that splitting themesh refinement step into two distinct phases of edge mark-ing and mesh subdivision allows the subdivision phase tooperate in a more load balanced fashion. In addition, sincedata remapping is performed before the mesh grows in sizedue to refinement, a smaller volume of data is moved. Thiscan lead to a potentially significant savings in the redistribu-tion cost. The load balancer also balances the computationalload for the flow solver while reducing the runtime commu-nication. This is important because flow solvers are usuallyseveral times more expensive than mesh adaptors. In anycase, it is obvious that mesh adaption, repartitioning, pro-cessor assignment, and remapping are critical componentsof the framework and must be accomplished rapidly and ef-ficiently so as not to cause a significant overhead to the flowcomputation.

In this paper, we provide simulation results for the optimal channel-aware adaptive transmission, dynamic resource allocation and multiuser scheduling schemes for the downlink of a wireless cell that is assisted by a number of relays. The relays are half-duplex and apply a regenerative decode-and-forward scheme, and the channels are Rayleigh block-faded. We apply the optimal resource allocation scheme for two specific scheduling criteria, the maximum capacity scheduler and the proportional fair scheduler. For both scheduling schemes, we study the gains of using relays on the overall aggregate throughput of the system as well as for the individual users. Furthermore, we compare the performance versus the total number of relays and their locations in the wireless cell and we study the statistics of relays' usage. Moreover, we compare between the two scheduling schemes in terms of achievable rate and fairness among users.

With the widespread applications of Internet of Things (IoT), the emergency response performance for large-scale network packets is facing serious challenge, especially for renewable distributed energy resources monitoring in a smart grid. Therefore, how to improve the real-time performance of the emergency data packets has been a critical issue. Traditional packet scheduling schemes and topology optimization strategies are not suitable for a large-scale IoT-based smart grid. To address this problem, this paper proposes a new packet scheduling scheme named LOES, which first combines the priority-based packet scheduling scheme with local optimization. We exchange local geographic information to reduce the hop counts and distance between distributed source nodes and sink nodes. Each destination node determines the packet scheduling sequence according to the received emergency information. Finally, we compare LOES with first come first serve, multilevel scheme, and dynamic multilevel priority packet scheduling scheme using packet loss rate, packet waiting time, and average packet end-to-end delay as metrics. The simulation results show that LOES outperforms these previous scheduling schemes.

This paper presents the optimized design and implementation of sparse tensor-times-dense matrix multiply (SpTTM) for CPU and GPU platforms. This primitive is a critical bottleneck in data analysis and mining applications based on tensor methods, such as the Tucker decomposition. We first design and implement sequential SpTTM to avoid explicit data transformations between a tensor and a matrix, which is the conventional approach. We further optimize SpTTM on multicore CPU and GPU systems by parallelizing, avoiding locks, and exploiting data locality. Our sequential SpTTM is up to 3.5× faster than the SpTTM from Tensor Toolbox and 1.5× over that from Cyclops Tensor Framework. Our parallel algorithms show 4.1× speedup on multicore Intel Core i7 and 18.8× speedup on NVIDIA K40c GPU over our sequential SpTTM respectively.

Although wireless control is one of the key technologies for future industries, most wireless networks are only used for monitoring. When wireless networks are applied to transmit control commands, the uncertain link qualities and limited network resources may destroy the performance of multi-loop control systems. Hence, it is critical to allocate these resources to optimize the control performance as the network condition changes and plants evolve. This article presents comprehensive optimal scheduling strategies for wireless control systems based on adaptive dynamic programming. First, we propose an effective adaptive dynamic programming scheduling (ADPS) strategy to solve the optimal scheduling problem based on the single-step control performance at runtime while significantly reducing computational complexity. Moreover, to overcome the “short-sightedness” of single-step performance prediction, we extend ADPS to ADPS-m ( m ulti-step prediction), which optimizes multi-step performance by incorporating a longer-horizon evolution of the plants. Furthermore, we propose ADPS-H ( H eterogeneous flow scheduling) to support heterogeneous flows with different data rates and sizes and ADPS-H-m ( m ulti-step prediction for H eterogeneous flow scheduling), which schedules heterogeneous flows in a longer prediction horizon. We prove that all these scheduling strategies can achieve optimality and stability under mild assumptions. Extensive experiments integrating TOSSIM and MATLAB/Simulink are performed to evaluate all of the proposed methods in case studies of four- and ten-loop control systems. The simulation results demonstrate that these strategies can effectively improve the control performance at lower computing costs under both cyber and physical disturbances. Under the noise level of \(-\) 76 dBm, for the four-loop case, ADPS achieves the same control performance as the linear programming while saving 99.5% of the execution time. ADPS-m further improves the control performance by up to 27.0% compared with ADPS at the prediction horizon of 3, and ADPS-H-m improves the performance by up to 32.3% and 8.4% compared with round-robin and ADPS-H, respectively. The ten-loop case indicates the effectiveness and scalability of the proposed approaches.

Energy efficient network path reconfiguration for industrial field data