114,648 research outputs found
Towards accelerated agent-based crowd simulation for Hajj and Umrah
There are many scientific applications ranging from weather prediction to oil and gas exploration that requires high-performance computing. It aids industries and researchers to enrich further their advancements. With the advent of general purpose computing over GPUs, most of the applications above are shifting towards High-Performance Computing (HPC). Agent-based crowd simulation is one of the candidates that requires high-performance computing. This type of application is used to predict crowd movement in highly congested areas. One of the most crucial scenarios in which this application can be used is to mimic the movement of the multi-cultural crowd performing Hajj and Umrah in Masjid Al-Haram, Makkah. Adequate performance for an agent-based crowd system is a common problem in computer science. While the existing event planning software, specifically for Hajj and Umrah, are unable to provide the required performance. The main reason is the increasing amount of autonomous pilgrims every year. In this paper, we propose a high performance agent-based crowd simulation that represents pilgrim movement during these rituals. The performance is achieved by parallelizing an open source steering library called OpenSteer using CUDA over GPU. By using our technique, event organizers will be able to simulate large crowds and will also be able to predict whether the developed event plan is viable or not. We have also discussed the architecture and implementation of this parallel Hajj simulation
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A customizable multi-agent system for distributed data mining
We present a general Multi-Agent System framework for
distributed data mining based on a Peer-to-Peer model. Agent
protocols are implemented through message-based asynchronous
communication. The framework adopts a dynamic load balancing
policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
Going Stupid with EcoLab
In 2005, Railsback et al. proposed a very simple model ({\em Stupid
Model}) that could be implemented within a couple of hours, and later
extended to demonstrate the use of common ABM platform functionality. They
provided implementations of the model in several agent based modelling
platforms, and compared the platforms for ease of implementation of this simple
model, and performance. In this paper, I implement Railsback et al's Stupid
Model in the EcoLab simulation platform, a C++ based modelling platform,
demonstrating that it is a feasible platform for these sorts of models, and
compare the performance of the implementation with Repast, Mason and Swarm
versions
Contract-Based General-Purpose GPU Programming
Using GPUs as general-purpose processors has revolutionized parallel
computing by offering, for a large and growing set of algorithms, massive
data-parallelization on desktop machines. An obstacle to widespread adoption,
however, is the difficulty of programming them and the low-level control of the
hardware required to achieve good performance. This paper suggests a
programming library, SafeGPU, that aims at striking a balance between
programmer productivity and performance, by making GPU data-parallel operations
accessible from within a classical object-oriented programming language. The
solution is integrated with the design-by-contract approach, which increases
confidence in functional program correctness by embedding executable program
specifications into the program text. We show that our library leads to modular
and maintainable code that is accessible to GPGPU non-experts, while providing
performance that is comparable with hand-written CUDA code. Furthermore,
runtime contract checking turns out to be feasible, as the contracts can be
executed on the GPU
A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Agent-based simulation is used as a tool for supporting (time-critical) decision making in differentiated contexts. Hence, techniques for speeding up the execution of agent-based models, such as Parallel Discrete Event Simulation (PDES), are of great relevance/benefit. On the other hand, parallelism entails that the final output provided by the simulator should closely match the one provided by a traditional sequential run. This is not obvious given that, for performance and efficiency reasons, parallel simulation engines do not allow the evaluation of global predicates on the simulation model evolution with arbitrary time-granularity along the simulation time-Axis. In this article we present a study on the effects of parallelization of agent-based simulations, focusing on complementary aspects such as performance and reliability of the provided simulation output. We target Terrain Covering Ant Robots (TCAR) simulations, which are useful in rescue scenarios to determine how many agents (i.e., robots) should be used to completely explore a certain terrain for possible victims within a given time. © 2014 Springer-Verlag Berlin Heidelberg
Using Pilot Systems to Execute Many Task Workloads on Supercomputers
High performance computing systems have historically been designed to support
applications comprised of mostly monolithic, single-job workloads. Pilot
systems decouple workload specification, resource selection, and task execution
via job placeholders and late-binding. Pilot systems help to satisfy the
resource requirements of workloads comprised of multiple tasks. RADICAL-Pilot
(RP) is a modular and extensible Python-based pilot system. In this paper we
describe RP's design, architecture and implementation, and characterize its
performance. RP is capable of spawning more than 100 tasks/second and supports
the steady-state execution of up to 16K concurrent tasks. RP can be used
stand-alone, as well as integrated with other application-level tools as a
runtime system
Programming agent-based demographic models with cross-state and message-exchange dependencies: A study with speculative PDES and automatic load-sharing
Agent-based modeling and simulation is a versatile and promising methodology to capture complex interactions among entities and their surrounding environment. A great advantage is its ability to model phenomena at a macro scale by exploiting simpler descriptions at a micro level. It has been proven effective in many fields, and it is rapidly becoming a de-facto standard in the study of population dynamics. In this article we study programmability and performance aspects of the last-generation ROOT-Sim speculative PDES environment for multi/many-core shared-memory architectures. ROOT-Sim transparently offers a programming model where interactions can be based on both explicit message passing and in-place state accesses. We introduce programming guidelines for systematic exploitation of these facilities in agent-based simulations, and we study the effects on performance of an innovative load-sharing policy targeting these types of dependencies. An experimental assessment with synthetic and real-world applications is provided, to assess the validity of our proposal
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