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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2a4a5c9728db0683dda1b2b3318fa0c9d/amerwafai",         
         "tags" : [
            "Back","CUDA","GPGPU","GPU","HLRS","Network","Neural","Parallelisation","Propagation","SCOPE","myown"
         ],
         
         "intraHash" : "a4a5c9728db0683dda1b2b3318fa0c9d",
         "interHash" : "0f4f1b3c77004231f7c17f1c80480dfe",
         "label" : "Optimization of industrial Neural Network simulators for GPGPUs",
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         "date" : "2016-01-29 09:34:55",
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         "pub-type": "inproceedings",
         "booktitle": "New Horizons in Web Based Learning","series": "LNCS","publisher":"Springer Berlin Heidelberg",
         "year": "2011", 
         "url": "", 
         
         "author": [ 
            "Mhd. Amer Wafai","Zaheer Ahmed","Rainer Keller","Sven Holzmann","Björn Sander","Michael Resch"
         ],
         "authors": [
         	
            	{"first" : "Mhd. Amer",	"last" : "Wafai"},
            	{"first" : "Zaheer",	"last" : "Ahmed"},
            	{"first" : "Rainer",	"last" : "Keller"},
            	{"first" : "Sven",	"last" : "Holzmann"},
            	{"first" : "Björn",	"last" : "Sander"},
            	{"first" : "Michael",	"last" : "Resch"}
         ],
         
         "editor": [ 
            "Dickson K. W. Chiu","Minhong Wang","Elvira Popescu","Qing Li","Rynson Lau"
         ],
         "editors": [
         	
            	{"first" : "Dickson K. W.",	"last" : "Chiu"},
            	{"first" : "Minhong",	"last" : "Wang"},
            	{"first" : "Elvira",	"last" : "Popescu"},
            	{"first" : "Qing",	"last" : "Li"},
            	{"first" : "Rynson",	"last" : "Lau"}
         ],
         "volume": "7697","pages": "21-29","abstract": "This paper introduces the porting of an industrial neural network simulator onto GPUs used in a tool-chain to sort massive amounts of E-mails and other textual data. Compared to other previous work, all steps are being executed on the GPU, achieving overall up to 33× speedup without using any cuBLAS functionality. All the time-consuming routines have been ported onto the GPU, i.e. the training-, the simulation- and the verification-phases, the training being the most time-consuming. It is planned to include these GPU-kernels into the product for special costumer's demands.",
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