The use of GPU computing is today an active research field due to the enormous parallel processing power of GPU. For more calculation efficiency, we integrate the GPU computing into an agent-based distributed computing, the result being a multi-agent model for distributed GPGPU computing. The computation distribution is based on a multi-agent system designed in accordance with the standards of the Foundation for Intelligent Physical Agents (FIPA). The interactions between the agents are based on Agent Communication Language (ACL). These agents are deployed into a multitude of interconnected computing nodes where each node is accelerated by a GPU chip. This multi agent system is implemented with the Java Agent DEvelopment Framework (JADE). Additionally, we provide a Java programming library to allow programmers to easily use this system to optimize their algorithms. An illustrative example with a K-means algorithm is given to show how cumbersome algorithms can be accelerated by this model.
MacQueenJ., Some methods for classification and analysis of multivariate observations, in: Proc of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, University of California, Berkeley, 1967.
2.
FangW.LauK.K.LuM. and YangK., Parallel data mining on graphics processors, Technical Report HKUST-CS08-07, Hong Kong University of Science and Technology, 2008.
3.
CheS. et al., A performance study of general-purpose applications on graphics processors using CUDA, Journal of Parallel and Distributed Computing68(10) (2008), 1370–138.
4.
WuR.ZhangB. and HsuM., Clustering billions of data points using GPUs, in: Proc of the Combined Workshops on UnConventional High Performance Computing Workshop Plus Memory Access Workshop, Ischia, Italy, (2009).
5.
WangX. and LeeserM., K-means clustering for multispectral images using floating-point divide, in: Proc of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, Napa, California, (2007).
6.
ZhouH. and LiuY., Accurate integration of multi-view range images using K-means clustering, Pattern Recognition41 (2008), 152–175.
7.
ZhouN.ZhangA.ZhengF. and GongL., Novel image compression – encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing, Optics & Laser Technology62 (2014), 152–160.
8.
EklundA.DufortP.ForsbergD. and LaConteS.M., Medical image processing on the GPU – Past, present and future, Medical Image Analysis17(8) (2013), 1073–1094.
9.
ChristensenG.E., MIMD vs SIMD parallel processing: A case study in 3D medical image registration, Parallel Computing24(9) (1998), 1369–1383.
10.
ClausB.L. and JohnsonS.R., Grid computing in large pharmaceutical molecular modeling, Drug Discovery Today13(13–14) (2008), 578–583.
11.
DebattistaK.ChalmersA.GillibrandR.LonghurstaP.MastoropoulouaG. and SundstedtV., Parallel selective rendering of high-fidelity virtual environments, Parallel Computing33(6) (2007), 361–376.
12.
CookS., CUDA programming: A developer’s guide to parallel computing with GPUs, Newnes (2012).
13.
ReañoC.SillaF.CastellóA.PeñaA.J.MayoR.Quintana-OrtíE.S. and DuatoJ., Improving the user experience of the rCUDA remote GPU virtualization framework, Concurrency and Computation: Practice and Experience27(14) (2015), 3746–3770.
BrodtkorbA.R.HagenT.R. and SætraM.L., Graphics processing unit (GPU) programming strategies and trends in GPU computing, Journal of Parallel and Distributed Computing73(1) (2013), 4–13.
16.
Nvidia CUDA C programming guide, Nvidia Corporation, 2011.
17.
KirkD., NVIDIA Cuda software and GPU parallel computing architecture, in: Proc of the 6th International Symposium on Memory Management, New York, USA, (2007).
BellifemineF.CaireG. and GreenwoodD., Developing Multi-agent Systems with JADE, Wiley, (2007).
20.
BouattaneO.CherradiB.YoussfiM. and BensalahM.O., Parallel c-means algorithm for image segmentation on a reconfigurable mesh computer, Parallel Computing37(4) (2011), 230–243.
21.
GaliziaA.D’AgostinoD. and ClematisA., An MPI – CUDA library for image processing on HPC architectures, Journal of Computational and Applied Mathematics273 (1 January 2015), 414–427.
22.
AugonnetC.AumageO.FurmentoN.NamystR. and ThibaultS., StarPU-MPI: Task programming over clusters of machines enhanced with accelerators, Ph.D. Dissertation Institut National de Recherche en Informatique et en Automatique INRIA, (2012).
23.
KaminskyA., The parallel java 2 library, in: The International Conference for High Performance Computing, Networking, Storage and Analysis, New Orleans, LA, USA, (November 18 2014).
24.
https://www.top500.org/.
25.
KahanwalB. and SinghT.P., The distributed computing paradigms: P2P, grid, cluster, cloud, and jungle, International Journal of Latest Research in Science and Technology1(2) (2012), 183–187.
26.
GiefersH. and PlatznerM., An FPGA-based reconfigurable mesh many-core, IEEE Transactions on Computers63(12) (2014), 2919–2932.
27.
SoukainaI.AhmedE. and MohamedK., Parallel and reconfigurable mesh architecture for low and medium level image processing applications, in: Proc of the Advances in Ubiquitous Networking 2, Casablanca, Morocco, (2016).
28.
KaiserH.HellerT.Adelstein-LelbachB.SerioA. and FeyD., Hpx: A task based programming model in a global address space, in: Proc of the 8th International Conference on Partitioned Global Address Space Programming Models, Eugene, Oregon, USA, (2014).
SnirM.OttoS.Huss-LedermanS.WalkerD.DongarraJ., MPI-The Complete Reference: Volume 1, the MPI Core Second Edition, MIT press Cambridge USA, 1998.
31.
GroppW.LuskE. and SkjellumA., Using MPI: Portable Parallel Programming with the Message-passing Interface Second Edition, MIT press, 1999.
32.
DagumL. and MenonR., OpenMP: An industry-standard API for shared-memory programming, IEEE Computational Science and Engineering5(1) (1998), 46–55.
33.
ChandraR.MenonR.DagumL.KohrD.MaydanD. and McDonaldJ., Parallel Programming in OpenMP, Morgan kaufmann, (2001).
34.
RabenseifnerR.HagerG. and JostG., Hybrid MPI/OpenMP parallel programming on clusters of multi-core SMP nodes, in: Proc of the 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Weimar, Germany, (2009).
35.
JinH.JespersenD.MehrotraP.BiswasR.HuangL. and ChapmanB., High performance computing using MPI and OpenMP on multi-core parallel systems, Parallel Computing37(9) (2011), 562–575.
36.
GilbertN. and TroitzschK.G., Simulation for the social scientist, McGraw-Hill Education, UK, 2005.
37.
RossetL.M.NardinL.G. and SichmanJ.S., Use of high performance computing in agent-based social simulation: A case study on trust-based coalition formation, in: Proc of the 7th Workshop-School on Agent Systems, Environments and Applications, (2013).
38.
KimJ.SeoS.LeeJ. and LeeJ.J., SnuCL: An OpenCL framework for heterogeneous CPU/GPU clusters, in: Proc of the 26th ACM International Conference on Supercomputing, Venice, Italy, (2012).
39.
BednárekD.DokulilJ.YaghobJ. and ZavoralF., Data-flow awareness in parallel data processing, in: Proc of the 6th International Symposium on Intelligent Distributed Computing, Calabria, Italy, (2013).
40.
BednarekD.DokulilJ.YaghobJ., and ZavoralF., Bobox: Parallelization framework for data processing, Advances in Information Technology and Applied Computing1 (2012), 189–194.
41.
CermakM. and ZavoralF., Achieving high availability in D-bobox, in: Proc of the 6th International Conference on Advances in Databases, Knowledge, and Data Applications, Chamonix, France, (2014).
42.
BednárekD.KrulišM.MalýP.YaghobJ.ZavoralF. and PokornyJ., Combining distributed computing and massively parallel accelerators to accelerate stream data processing, in: Proc of the 7th International Conference on Advances in Databases, Knowledge, and Data Applications, Rome, Italy, (2015).
43.
HermellinE.MichelF. and FerberJ., Systèmes multi-agents et GPGPU: État des lieux et directions pour l’avenir, in: Journées Francophones sur les Systèmes Multi-Agents, Loriol-sur-drôme, France, (2014).
44.
StrengertM.MüllerC.DachsbacherC. and ErtlT., CUDASA: Compute unified device and systems architecture, in: Proc of the Eurographics Symposium on Parallel Graphics and Visualization, Crete, Greece, (2008).
45.
ViñasM.FraguelaB.B.AndradeD. and DoalloR., Towards a high level approach for the programming of heterogeneous clusters, in: Proc of the 45th International Conference on Parallel Processing Workshops, Philadelphia, PA, USA, (2016).
46.
DuatoJ.PeñaA.J.SillaF.MayoR. and Quintana-OrtíE.S., rCUDA: Reducing the number of GPU-based accelerators in high performance clusters, in: Proc of the The International Conference on High Performance Computing & Simulation, Caen, France, (2010), 224–231.
47.
CastellóA.PeñaA.J.MayoR.PlanasJ.Quintana-OrtíE.S. and BalajiP., Exploring the interoperability of remote GPGPU virtualization using rCUDA and directive-based programming models, The Journal of Supercomputing, (2016), 1–15.
48.
YanY.GrossmanM.SarkarV., JCUDA: A programmer-friendly interface for accelerating java programs with CUDA, in: Proc of the 15th International European Conference on Parallel Processing, Delft, Netherlands, (2009).
