A hierarchical parallel discrete event simulation kernel for multicore platform

Tang, Wenjie; Yao, Yiping; Zhu, Feng

doi:10.1007/s10586-012-0201-3

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…Therefore, when D-Cunetsim-CPU reaches a speedup of 17.8 times, D-NS3 achieves only a speedup of 7 times while both use the same hardware. We also found that Cunetsim achieves the maximum gain, speedup of 560 times, on such a large scale scenario as the GPU reaches its optimal operating level with the total number of 50k threads [30]. Furthermore, D-Cunetsim-GPU presents a significant speedup of 3300 times.…”

Section: Impact Of Network Densitymentioning

confidence: 64%

Coordinator-Master-Worker Model For Efficient Large Scale Network Simulation

Romdhanne¹,

Nikaein²,

Bonnet³

2013

Proceedings of the Sixth International Conference on Simulation Tools and Techniques

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In this work, we propose a coordinator-master-worker (CMW) model for medium to extra-large scale network simulation. The model supports distributed and parallel simulation for a heterogeneous computing node architecture with both multicore CPUs and GPUs. The model aims at maximizing the hardware usage rate while reducing the overall management overhead. In the CMW model, the coordinator is the toplevel simulation CPU process that performs an initial partitioning of the simulation into multiple instances and is responsible for load balancing and synchronization services among all the active masters. The master is also a CPU process and provides event scheduling, synchronization, and communication services to the workers. It manages workers operating potentially on different computing resources within the same shared memory context and communicates with the coordinator and others masters through the messages passing interface. The worker is the elementary actor of CMW model that performs the simulation routines and interacts with the input and output data, and can be a CPU or a GPU thread. Compared to existing master-worker models, the CMW is natively parallel and GPU compliant, and can be extended to support additional computing resources. The performance gain of the model is evaluated through different benchmarking scenarios using low-cost publicly available GPU platforms. The results have been shown that the speedup up to 3000 times can be achieved compared to a sequential execution and up to 6 times compared to a mono-GPU MWbased simulation. The hardware activities rate of the CMW services for both CPU and GPU are analyzed in detail.

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Section: Impact Of Network Densitymentioning

confidence: 64%

Coordinator-Master-Worker Model For Efficient Large Scale Network Simulation

Romdhanne¹,

Nikaein²,

Bonnet³

2013

Proceedings of the Sixth International Conference on Simulation Tools and Techniques

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“…For a node containing processes numbered by , we call the processes a family, and one process is assigned as family controller, which is in accordance with the design of NTW-MT [1], [2] and double-indexed shared memory [15]. Family controller can allocate a block of shared memory, and each process use a part of the block as so called a segment to contain messages sent to it, the access to which is controlled by the structure shm_entry as shown in Fig.…”

Section: Allocationmentioning

confidence: 99%

“…In this section, we turns to evaluate the effect of ZeROshm to real simulation. The overall architecture of our simulator is same as NTW-MT [1], [2], while intra-node communication is improved by ZeROshm, thereby eliminating twice copy of intra-node messages between process space and the shared memory.…”

Section: ) Pure Point-to-point Message Passingmentioning

confidence: 99%

“…We launch 3 processes which includes 1 controller process taking care of global control of simulation, e.g. synchronization all processes, and 2 worker processes that contains 2 processing threads for processing events, and readers can refer to NTW-MT [1], [2] for more details about the use of process and thread. We create 10,000 PHOLD instances and distribute them evenly among all processing threads.…”

Section: Radius Time_scalementioning

confidence: 99%

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Towards Order-Preserving and Zero-Copy Communication on Shared Memory for Large Scale Simulation

Li,

Shen,

Lin

et al. 2023

Chinese J. Elect.

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Parallel simulation generally needs efficient, reliable and order-preserving communication. In this article, a zero-copy, reliable and order-preserving intra-node message passing approach named ZeROshm is proposed. This mechanism partitions shared memory into segments assigned to processes for receiving messages. Each segment consists of two levels of index L1 and L2 that recordes the order of messages in the host segment, and the processes also read from and write to the segments directly according to the indexes, thereby eliminating allocating and copying buffers. As experimental results show, ZeROshm exhibits nearly equivalent performance to message passing interface (MPI) for small message and superior performance for large message. Specifically, ZeROshm costs less time by 43%, 40% and 55% respectively in pure communication, communication with contention and real PHOLD simulation within a single node. Additionally, in hybrid environment, the combination of ZeROshm and MPI also shorten the execution time of PHOLD simulation by about 42% compared to pure MPI.

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Optimization for Accelerating Large Scale Agent Based Simulation

Guo

Chen

et al. 2016

Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems

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A hierarchical parallel discrete event simulation kernel for multicore platform

Cited by 12 publications

References 11 publications

Coordinator-Master-Worker Model For Efficient Large Scale Network Simulation

Coordinator-Master-Worker Model For Efficient Large Scale Network Simulation

Towards Order-Preserving and Zero-Copy Communication on Shared Memory for Large Scale Simulation

Optimization for Accelerating Large Scale Agent Based Simulation

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