An approach for the effective utilization of GP-GPUS in parallel combined simulation

Bauer, David W.; McMahon, Matthew T.; Page, Ernest H.

doi:10.1109/wsc.2008.4736131

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…Regarding the CPU-dominated cases, the CPU is responsible for the core logic of the simulation and some complex computations are offloaded to the GPU. In Bauer et al, 3 the authors use a GPU to accelerate combined simulations in which the continuous component (the event handlers) runs on the GPU, while the discrete component of the simulation (the event scheduler) runs on the CPU. In Perumalla, 4 the author proposed a hybrid method to run a heat diffusion simulation on a GPU.…”

Section: Related Workmentioning

confidence: 99%

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An alternative approach for collaborative simulation execution on a CPU+GPU hybrid system

et al. 2019

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In the past few years, the graphics processing unit (GPU) has been widely used to accelerate time-consuming models in simulations. Since both model computation and simulation management are main factors that affect the performance of large-scale simulations, only accelerating model computation will limit the potential speedup. Moreover, models that can be well accelerated by a GPU could be insufficient, especially for simulations with many lightweight models. Traditionally, the parallel discrete event simulation (PDES) method is used to solve this class of simulation, but most PDES simulators only utilize the central processing unit (CPU) even though the GPU is commonly available now. Hence, we propose an alternative approach for collaborative simulation execution on a CPU+GPU hybrid system. The GPU supports both simulation management and model computation as CPUs. A concurrency-oriented scheduling algorithm was proposed to enable cooperation between the CPU and the GPU, so that multiple computation and communication resources can be efficiently utilized. In addition, GPU functions have also been carefully designed to adapt the algorithm. The combination of those efforts allows the proposed approach to achieve significant speedup compared to the traditional PDES on a CPU.

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Section: Related Workmentioning

confidence: 99%

“…There have been some studies targeted at accelerating simulations using GPUs. [2][3][4] In these works, models with heavy computation are offloaded to the GPU to achieve acceleration. However, both model computation and simulation management are major sources of overhead in a large-scale simulation.…”

Section: Introductionmentioning

confidence: 99%

An alternative approach for collaborative simulation execution on a CPU+GPU hybrid system

et al. 2019

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“…For instance, Bauer et al used a GPU to accelerate combined simulations in which the continuous component (the event handlers) runs on a GPU while the discrete component of the simulation (the event scheduler) runs on the CPU. 10…”

Section: Related Workmentioning

confidence: 99%

A GPU-based discrete event simulation kernel

Tang

Yao

2013

SIMULATION

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The graphic processing unit (GPU) can perform some large-scale simulations in an economical way. However, harnessing the power of a GPU to discrete event simulation (DES) is difficult because of the mismatch between GPU’s synchronous execution mode and DES’s asynchronous time advance mechanism. In this paper, we present a GPU-based simulation kernel (gDES) to support DES and propose three algorithms to support high efficiency. Since both limited parallelism and redundant synchronization affect the performance of DES based on a GPU, we propose a breadth-expansion conservative time window algorithm to increase the degree of parallelism while retaining the number of synchronizations. By using the expansion method, it can import as many as possible ‘safe’ events. The irregular and dynamic requirement for storing the events leads to uneven and sparse memory usage, thereby causing waste of memory and unnecessary overhead. A memory management algorithm is proposed to store events in a balanced and compact way by using a lightweight stochastic method. When events processed by threads in a warp have different types, the performance of gDES decreases rapidly because of branch divergence. An event redistribution algorithm is proposed by reassigning events of the same type to neighboring threads to reduce the probability of branch divergence. We analyze the superiority of the proposed algorithms and gDES with a series of experiments. Compared to a CPU-based simulator on a multicore platform, the gDES can achieve up to 11×, 5×, and 8× speedup in PHOLD, QUEUING NETWORK, and epidemic simulation, respectively.

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“…The core logic of the simulation framework, e. g., event schedulers and event queues, remain in host memory and run on the CPU. In this context, Bauer et al [14] investigate the applicability of GPUs to combined simulations [15] in which the discrete component of the simulation (the event scheduler) executes on CPUs while the continuous component (the event handlers) runs on GPUs. Using a synthetic workload model, the authors report considerable speedups for simulations containing computationally complex events.…”

Section: A Integrating Gpus With Pdesmentioning

confidence: 99%

Multi-level Parallelism for Time- and Cost-Efficient Parallel Discrete Event Simulation on GPUs

Kunz

Schemmel

Gross

et al. 2012

2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation

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Abstract-Developing complex technical systems requires a systematic exploration of the given design space in order to identify optimal system configurations. However, studying the effects and interactions of even a small number of system parameters often requires an extensive number of simulation runs. This in turn results in excessive runtime demands which severely hamper thorough design space explorations.In this paper, we present a parallel discrete event simulation scheme that enables cost-and time-efficient execution of large scale parameter studies on GPUs. In order to efficiently accommodate the stream-processing paradigm of GPUs, our parallelization scheme exploits two orthogonal levels of parallelism: External parallelism among the inherently independent simulations of a parameter study and internal parallelism among independent events within each individual simulation of a parameter study. Specifically, we design an event aggregation strategy based on external parallelism that generates workloads suitable for GPUs. In addition, we define a pipelined event execution mechanism based on internal parallelism to hide the transfer latencies between host-and GPU-memory. We analyze the performance characteristics of our parallelization scheme by means of a prototype implementation and show a 25-fold performance improvement over purely CPU-based execution.

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An approach for the effective utilization of GP-GPUS in parallel combined simulation

Cited by 9 publications

References 24 publications

An alternative approach for collaborative simulation execution on a CPU+GPU hybrid system

An alternative approach for collaborative simulation execution on a CPU+GPU hybrid system

A GPU-based discrete event simulation kernel

Multi-level Parallelism for Time- and Cost-Efficient Parallel Discrete Event Simulation on GPUs

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