{  
   "types" : {
      "Bookmark" : {
         "pluralLabel" : "Bookmarks"
      },
      "Publication" : {
         "pluralLabel" : "Publications"
      },
      "GoldStandardPublication" : {
         "pluralLabel" : "GoldStandardPublications"
      },
      "GoldStandardBookmark" : {
         "pluralLabel" : "GoldStandardBookmarks"
      },
      "Tag" : {
         "pluralLabel" : "Tags"
      },
      "User" : {
         "pluralLabel" : "Users"
      },
      "Group" : {
         "pluralLabel" : "Groups"
      },
      "Sphere" : {
         "pluralLabel" : "Spheres"
      }
   },
   
   "properties" : {
      "count" : {
         "valueType" : "number"
      },
      "date" : {
         "valueType" : "date"
      },
      "changeDate" : {
         "valueType" : "date"
      },
      "url" : {
         "valueType" : "url"
      },
      "id" : {
         "valueType" : "url"
      },
      "tags" : {
         "valueType" : "item"
      },
      "user" : {
         "valueType" : "item"
      }      
   },
   
   "items" : [
   	  
      {
         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2126f35b3dc5e36c0d63a461eb07e23c3/clausbraun",         
         "tags" : [
            "GPGPU","GPU","Markov-Chain","Monte-Carlo","SimTech","architectures","computer","heterogeneous","hybrid","molecular","myown","parallel","simulation","thermodynamics"
         ],
         
         "intraHash" : "126f35b3dc5e36c0d63a461eb07e23c3",
         "interHash" : "8b3986f798c6d3bc3d644b3a4e79b147",
         "label" : "Acceleration of Monte-Carlo Molecular Simulations on Hybrid Computing Architectures",
         "user" : "clausbraun",
         "description" : "",
         "date" : "2018-03-19 16:15:07",
         "changeDate" : "2018-03-19 15:32:04",
         "count" : 7,
         "pub-type": "inproceedings",
         "booktitle": "Proceedings of the 30th IEEE International Conference on Computer Design (ICCD'12)","publisher":"IEEE Computer Society",
         "year": "2012", 
         "url": "", 
         
         "author": [ 
            "Claus Braun","Stefan Holst","Hans-Joachim Wunderlich","Juan Manuel Castillo","Joachim Gross"
         ],
         "authors": [
         	
            	{"first" : "Claus",	"last" : "Braun"},
            	{"first" : "Stefan",	"last" : "Holst"},
            	{"first" : "Hans-Joachim",	"last" : "Wunderlich"},
            	{"first" : "Juan Manuel",	"last" : "Castillo"},
            	{"first" : "Joachim",	"last" : "Gross"}
         ],
         "pages": "207--212","abstract": "Markov-Chain Monte-Carlo (MCMC) methods are an important class of simulation techniques, which execute a sequence of simulation steps, where each new step depends on the previous ones. Due to this fundamental dependency, MCMC methods are inherently hard to parallelize on any architecture. The upcoming generations of hybrid CPU/GPGPU architectures with their multi-core CPUs and tightly coupled many-core GPGPUs provide new acceleration opportunities especially for MCMC methods, if the new degrees of freedom are exploited correctly. \r\nIn this paper, the outcomes of an interdisciplinary collaboration are presented, which focused on the parallel mapping of a MCMC molecular simulation from thermodynamics to hybrid CPU/GPGPU computing systems. While the mapping is designed for upcoming hybrid architectures, the implementation of this approach on an NVIDIA Tesla system already leads to a substantial speedup of more than 87x despite the additional communication overheads.",
         "file" : "http://www.iti.uni-stuttgart.de/fileadmin/rami/files/publications/2012/ICCD_BraunHWCG2012.pdf",
         
         "doi" : "http://dx.doi.org/10.1109/ICCD.2012.6378642",
         
         "bibtexKey": "BraunHWCG2012"

      }
,
      {
         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2b9d42307aff55f949dce3efdc063ee86/clausbraun",         
         "tags" : [
            "Euler-Maruyama","GPU","SimTech","adaptive","aggregation","approximation","computing","heterogeneous","ligand-receptor-model","multi-timescale","myown","parallel","particle","simulation"
         ],
         
         "intraHash" : "b9d42307aff55f949dce3efdc063ee86",
         "interHash" : "8b3950b9a31a28b554ce868d67598d14",
         "label" : "Adaptive Parallel Simulation of a Two-Timescale-Model for Apoptotic Receptor-Clustering on GPUs",
         "user" : "clausbraun",
         "description" : "",
         "date" : "2018-03-19 16:15:07",
         "changeDate" : "2018-03-19 15:21:25",
         "count" : 5,
         "pub-type": "inproceedings",
         "booktitle": "Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'14)",
         "year": "2014", 
         "url": "", 
         
         "author": [ 
            "Alexander Schöll","Claus Braun","Markus Daub","Guido Schneider","Hans-Joachim Wunderlich"
         ],
         "authors": [
         	
            	{"first" : "Alexander",	"last" : "Schöll"},
            	{"first" : "Claus",	"last" : "Braun"},
            	{"first" : "Markus",	"last" : "Daub"},
            	{"first" : "Guido",	"last" : "Schneider"},
            	{"first" : "Hans-Joachim",	"last" : "Wunderlich"}
         ],
         "pages": "424--431","abstract": "Computational biology contributes important solutions for major biological challenges. Unfortunately, most applications in computational biology are highly computeintensive and associated with extensive computing times. Biological problems of interest are often not treatable with traditional simulation models on conventional multi-core CPU systems. This interdisciplinary work introduces a new multi-timescale simulation model for apoptotic receptor-clustering and a new parallel evaluation algorithm that exploits the computational performance of heterogeneous CPU-GPU computing systems. For this purpose, the different dynamics involved in receptor-clustering are separated and simulated on two timescales. Additionally, the time step sizes are adaptively refined on each timescale independently.\r\n This new approach improves the simulation performance significantly and reduces computing times from months to hours for observation times of several seconds.",
         "file" : "http://www.iti.uni-stuttgart.de/fileadmin/rami/files/publications/2014/BIBM_SchoeBDSW2014.pdf",
         
         "doi" : "http://dx.doi.org/10.1109/BIBM.2014.6999195",
         
         "bibtexKey": "SchoeBDSW2014"

      }
	  
   ]
}