11,300 research outputs found
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
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
An Implementation of Membrane Computing Using Reconfigurable Hardware
Because of their inherent large-scale parallelism, membrane computing models can be fully exploited only through the use of a parallel computing platform. We have fully implemented such a computing platform based on reconfigurable hardware that is intended to support the efficient execution of membrane computing models. This computing platform is the first of its type to implement parallelism at both the system and region levels. In this paper, we describe how our computing platform implements the core features of membrane computing models in hardware, and present a theoretical performance analysis of the algorithm it executes in hardware. The performance analysis suggests that the computing platform can significantly outperform sequential implementations of membrane computing as well as Petreska and Teuscher's hardware implementation, the only other complete hardware implementation of membrane computing in existence
Simulating Spiking Neural P systems without delays using GPUs
We present in this paper our work regarding simulating a type of P system
known as a spiking neural P system (SNP system) using graphics processing units
(GPUs). GPUs, because of their architectural optimization for parallel
computations, are well-suited for highly parallelizable problems. Due to the
advent of general purpose GPU computing in recent years, GPUs are not limited
to graphics and video processing alone, but include computationally intensive
scientific and mathematical applications as well. Moreover P systems, including
SNP systems, are inherently and maximally parallel computing models whose
inspirations are taken from the functioning and dynamics of a living cell. In
particular, SNP systems try to give a modest but formal representation of a
special type of cell known as the neuron and their interactions with one
another. The nature of SNP systems allowed their representation as matrices,
which is a crucial step in simulating them on highly parallel devices such as
GPUs. The highly parallel nature of SNP systems necessitate the use of hardware
intended for parallel computations. The simulation algorithms, design
considerations, and implementation are presented. Finally, simulation results,
observations, and analyses using an SNP system that generates all numbers in
- {1} are discussed, as well as recommendations for future work.Comment: 19 pages in total, 4 figures, listings/algorithms, submitted at the
9th Brainstorming Week in Membrane Computing, University of Seville, Spai
Building a basic membrane computer
In this work, we present the building of two well-known membrane com-
puters (squares generator and divisor test). Although they are very basic machines they
present problems common to every P system (competition, parallel execution of rules,
membrane dissolution, etc.) that have to be solved in order to get real emulations for
them. The presented designs mimic the systems operation in a realistic way, by achieving
both maximum parallelism and non-determinism, and demonstrating for the rst time
that a membrane computer can actually be built in silico. Our architectures fully emu-
late the membranes behaviour yielding to a performance of one transition per clock cycle,
supposing a real physical realization of the mentioned machines
An Improved GPU Simulator For Spiking Neural P Systems
Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons 'compute' or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420
Researching Framework for Simulating/Implementating P Systems
Researching simulation/implementation of membranes systems is very recent. Present literature
gathers new publications frequently about software/hardware, data structures and algorithms for implementing P
system evolution.
In this context, this work presents a framework which goal is to make tasks of researchers of this field easier.
Hence, it establishes the set of cooperating classes that form a reusable and flexible design for the customizable
evaluation with new data structures and algorithms. Moreover, it includes customizable services for correcting,
monitoring and logging the evolution and edition, recovering, automatic generating, persistence and visualizing P
systems
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