PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment

Tamrakar, Shailesh; Richmond, Paul; D’Souza, Roshan

doi:10.1177/0037549716673724

Cited by 8 publications

(7 citation statements)

References 63 publications

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“…D-Mason [13,14] provides an effective and efficient way of parallelizing Mason programs for distributed systems, handling communication strategies and load balancing [15], tested on Amazon Web Services [15], and used on several social science scenarios [16]. Flame [7] is an agent-based environment based on an underlying formal model, called the X-Machine, and used in various scenarios such as cell simulations [17] and immune system modeling [18]. FlameGPU [19] is an extension of Flame that executes agent-based models on GPU architectures.…”

Section: Introductionmentioning

confidence: 99%

OpenABL: A Domain-Specific Language for Parallel and Distributed Agent-Based Simulations

Cosenza

Popov

Juurlink

et al. 2018

Euro-Par 2018: Parallel Processing

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Agent-based simulations are becoming widespread among scientists from different areas, who use them to model increasingly complex problems. To cope with the growing computational complexity, parallel and distributed implementations have been developed for a wide range of platforms. However, it is difficult to have simulations that are portable to different platforms while still achieving high performance. We present OpenABL, a domain-specific language for portable, highperformance, parallel agent modeling. It comprises an easy-to-program language that relies on high-level abstractions for programmability and explicitly exploits agent parallelism to deliver high performance. A sourceto-source compiler translates the input code to a high-level intermediate representation exposing parallelism, locality and synchronization, and, thanks to an architecture based on pluggable backends, generates target code for multi-core CPUs, GPUs, large clusters and cloud systems. OpenABL has been evaluated on six applications from various fields such as ecology, animation, and social sciences. The generated code scales to large clusters and performs similarly to handwritten target-specific code, while requiring significantly fewer lines of codes.

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Section: Introductionmentioning

confidence: 99%

OpenABL: A Domain-Specific Language for Parallel and Distributed Agent-Based Simulations

Cosenza

Popov

Juurlink

et al. 2018

Euro-Par 2018: Parallel Processing

Self Cite

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“…D-Mason has been tested also on Amazon Web Services [44], and used on several social science scenarios [45]. Flame [10] is an agent-based environment based on an underlying formal model, called the X-Machine, and used in various scenarios such as cell simulations [46] and immune system modeling [47]. FlameGPU [15] extends Flame enabling the execution of agent-based models on GPU architectures.…”

Section: Parallel Agent-based Simulationsmentioning

confidence: 99%

Easy and efficient agent-based simulations with the OpenABL language and compiler

Cosenza

Popov

Juurlink

et al. 2021

Future Generation Computer Systems

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Agent-based simulations represent an effective scientific tool, with numerous applications from social sciences to biology, which aims to emulate or predict complex phenomena through a set of simple rules performed by multiple agents. To simulate a large number of agents with complex models, practitioners have developed high-performance parallel implementations, often specialized for particular scenarios and target hardware. It is, however, difficult to obtain portable simulations, which achieve high performance and at the same time are easy to write and to reproduce on different hardware. This article gives a complete presentation of OpenABL, a domain-specific language and a compiler for agent-based simulations that enable users to achieve high-performance parallel and distributed agent simulations with a simple and portable programming environment. OpenABL is comprised of (1) an easy-to-program language, which relies on domain abstractions and explicitly exposes agent parallelism, synchronization and locality, (2) a source-to-source compiler, and (3) a set of pluggable compiler backends, which generate target code for multi-core CPUs, GPUs, and cloud-based systems. We evaluate OpenABL on simulations from different fields. In particular, our analysis includes predator-prey and keratinocyte, two complex simulations with multiple step functions, heterogeneous agent types, and dynamic creation and removal of agents. The results show that OpenABL-generated codes are portable to different platforms, perform similarly to manual target-specific implementations, and require significantly fewer lines of codes.

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“…There are several studies on the application of GPUs to biological systems [32–35]. There are several existing works on parallel implementation of the immune system model simulation in continuous space [1, 9, 36]. PI-FLAME [36] is a GPU-accelerated viral infection response simulator using continuous space, which demonstrates up to 13x reduction in simulation runtime compared to a serial CPU based implementation.…”

Section: Related Workmentioning

confidence: 99%

Parallelisation strategies for agent based simulation of immune systems

et al. 2019

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BackgroundIn recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches.ResultsThis paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment.ConclusionsOur performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware.

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PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment

Cited by 8 publications

References 63 publications

OpenABL: A Domain-Specific Language for Parallel and Distributed Agent-Based Simulations

OpenABL: A Domain-Specific Language for Parallel and Distributed Agent-Based Simulations

Easy and efficient agent-based simulations with the OpenABL language and compiler

Parallelisation strategies for agent based simulation of immune systems

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