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Time Warp synchronized parallel discrete event simulators are organized to operate asynchronously and aggressively without explicit synchronization between the concurrently executing simulators. In place of an explicit synchronization mechanism, the concurrent simulators maintain a common virtual clock model and implement a rollback/recovery mechanism to restore causal order when out-of order events are detected. When the critical path of execution of the simulation is balanced across these parallel simulators, this can result in a highly effective, lightweight synchronization mechanism. However, imbalances in the workload across the parallel simulators can result in excessive rollback at some nodes and ultimately result in an overall slowing of the simulation as prematurely computed and transmitted events are processed. On small shared memory multi-core systems, a lowest timestamp first scheduling policy can effectively balance the workload. However, on larger many-core chips, conventional load balancing and workload migration will once again become necessary. Fortunately, emerging many-core chips contain some interesting features that can potentially be exploited to improve the performance of parallel simulations. For example, the Intel Single-chip Cloud Computer (SCC) provides mechanisms that a running application can use to adjust the frequency/voltage of different regions (called islands) of the chip. These islands are network and processing core centric and thus, in a Time Warp simulation, one can increase the frequency of the cores executing threads on the critical path (those experiencing infrequent rollback) and decrease the frequency of the cores executing threads off the critical path (those experiencing excessive rollback). This paper investigates the run-time control and adjustment of core frequency in an AMD Phenom II X6 multi-core processor to explore and demonstrate that the dynamic run-time control of core frequency can sometimes improve the performance of a Time Warp synchronized parallel simulation.
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Simulus: Easy Breezy Simulation in Python
This paper introduces Simulus, a full-fledged open-source discrete-event simulator, supporting both event-driven and process-oriented simulation world-views. Simulus is implemented in Python and aspires to be a part of the Python's ecosystem supporting scientific computing. Simulus also provides several advanced modeling constructs to ease common simulation tasks (e.g., complex queuing models, interprocess synchronizations, and message-passing communications). Simulus also provides organic support for simultaneously running a time-synchronized group of simulators, either sequentially or in parallel, thereby allowing composable simulation of individual simulators handling different aspects of a target system, and enabling large-scale simulation running on parallel computers. This paper describes the salient features of Simulus and examines its major design decisions.
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- Award ID(s):
- 2008000
- PAR ID:
- 10251068
- Date Published:
- Journal Name:
- 2020 Winter Simulation Conference
- Page Range / eLocation ID:
- 2329 to 2340
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WSC48552.2020.9383886
