58,753 research outputs found
Racing to hardware-validated simulation
Processor simulators rely on detailed timing models of the processor pipeline to evaluate performance. The diversity in real-world processor designs mandates building flexible simulators that expose parts of the underlying model to the user in the form of configurable parameters. Consequently, the accuracy of modeling a real processor relies on both the accuracy of the pipeline model itself, and the accuracy of adjusting the configuration parameters according to the modeled processor. Unfortunately, processor vendors publicly disclose only a subset of their design decisions, raising the probability of introducing specification inaccuracies when modeling these processors. Inaccurately tuning model parameters deviates the simulated processor from the actual one. In the worst case, using improper parameters may lead to imbalanced pipeline models compromising the simulation output. Therefore, simulation models should be hardware-validated before using them for performance evaluation. As processors increase in complexity and diversity, validating a simulator model against real hardware becomes increasingly more challenging and time-consuming. In this work, we propose a methodology for validating simulation models against real hardware. We create a framework that relies on micro-benchmarks to collect performance statistics on real hardware, and machine learning-based algorithms to fine-tune the unknown parameters based on the accumulated statistics. We overhaul the Sniper simulator to support the ARM AArch64 instruction-set architecture (ISA), and introduce two new timing models for ARM-based in-order and out-of-order cores. Using our proposed simulator validation framework, we tune the in-order and out-of-order models to match the performance of a real-world implementation of the Cortex-A53 and Cortex-A72 cores with an average error of 7% and 15%, respectively, across a set of SPEC CPU2017 benchmarks
Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)
In this tutorial paper, we will firstly review some basic simulation concepts
and then introduce the parallel and distributed simulation techniques in view
of some new challenges of today and tomorrow. More in particular, in the last
years there has been a wide diffusion of many cores architectures and we can
expect this trend to continue. On the other hand, the success of cloud
computing is strongly promoting the everything as a service paradigm. Is
parallel and distributed simulation ready for these new challenges? The current
approaches present many limitations in terms of usability and adaptivity: there
is a strong need for new evaluation metrics and for revising the currently
implemented mechanisms. In the last part of the paper, we propose a new
approach based on multi-agent systems for the simulation of complex systems. It
is possible to implement advanced techniques such as the migration of simulated
entities in order to build mechanisms that are both adaptive and very easy to
use. Adaptive mechanisms are able to significantly reduce the communication
cost in the parallel/distributed architectures, to implement load-balance
techniques and to cope with execution environments that are both variable and
dynamic. Finally, such mechanisms will be used to build simulations on top of
unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International
Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul
(Turkey), IEEE, July 2011. ISBN 978-1-61284-382-
Implementation of UAV Coordination Based on a Hierarchical Multi-UAV Simulation Platform
In this paper, a hierarchical multi-UAV simulation platform,called XTDrone,
is designed for UAV swarms, which is completely open-source 4 . There are six
layers in XTDrone: communication, simulator,low-level control, high-level
control, coordination, and human interac-tion layers. XTDrone has three
advantages. Firstly, the simulation speedcan be adjusted to match the computer
performance, based on the lock-step mode. Thus, the simulations can be
conducted on a work stationor on a personal laptop, for different purposes.
Secondly, a simplifiedsimulator is also developed which enables quick algorithm
designing sothat the approximated behavior of UAV swarms can be observed
inadvance. Thirdly, XTDrone is based on ROS, Gazebo, and PX4, andhence the
codes in simulations can be easily transplanted to embeddedsystems. Note that
XTDrone can support various types of multi-UAVmissions, and we provide two
important demos in this paper: one is aground-station-based multi-UAV
cooperative search, and the other is adistributed UAV formation flight,
including consensus-based formationcontrol, task assignment, and obstacle
avoidance.Comment: 12 pages, 10 figures. And for the, see
https://gitee.com/robin_shaun/XTDron
Fast Hardware Implementations of Static P Systems
In this article we present a simulator of non-deterministic static P systems
using Field Programmable Gate Array (FPGA) technology. Its major feature
is a high performance, achieving a constant processing time for each transition. Our
approach is based on representing all possible applications as words of some regular
context-free language. Then, using formal power series it is possible to obtain the
number of possibilities and select one of them following a uniform distribution, in
a fair and non-deterministic way. According to these ideas, we yield an implementation
whose results show an important speed-up, with a strong independence from
the size of the P system.Ministry of Science and Innovation of the Spanish Government under the project TEC2011-27936 (HIPERSYS)European Regional Development Fund (ERDF)Ministry of Education of Spain (FPU grant AP2009-3625)ANR project SynBioTI
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