Filters








11 Hits in 0.41 sec

IBM Deep Learning Service [article]

Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren (+5 others)
2017 arXiv   pre-print
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision. At the same time, the "as-a-Service"-based business model on the cloud is fundamentally transforming the information technology industry. These two trends: deep learning, and "as-a-service" are colliding to give rise to a new business model for cognitive application delivery: deep learning as a service
more » ... in the cloud. In this paper, we will discuss the details of the software architecture behind IBM's deep learning as a service (DLaaS). DLaaS provides developers the flexibility to use popular deep learning libraries such as Caffe, Torch and TensorFlow, in the cloud in a scalable and resilient manner with minimal effort. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes. A resource provisioning layer enables flexible job management on heterogeneous resources, such as graphics processing units (GPUs) and central processing units (CPUs), in an infrastructure as a service (IaaS) cloud.
arXiv:1709.05871v1 fatcat:wgifwcuqjfghxj7ounvapkh3du

Exploratory Study of Scientific Visualization Techniques for Program Visualization [chapter]

Brian J. d'Auriol, Claudia V. Casas, Pramod Kumar Chikkappaiah, L. Susan Draper, Ammar J. Esper, Jorge López, Rajesh Molakaseema, Seetharami R. Seelam, René Saenz, Qian Wen, Zhengjing Yang
2001 Lecture Notes in Computer Science  
This paper presents a unique point-of-view for program visualization, namely, the use of scientific visualization techniques for program visualization. This paper is exploratory in nature. Its primary contribution is to re-examine program visualization from a scientific visualization point-of-view. This paper reveals that specific visualization techniques such as animation, isolines, program slicing, dimensional reduction, glyphs and color maps may be considered for program visualization. In
more » ... ition, some features of AVS/Express that may be used for program visualization are discussed. Lastly, comments regarding emotional color spaces are made.
doi:10.1007/3-540-45718-6_75 fatcat:ke5ycykhw5eixkqctha6gjyyli

Statistical and Dempster-Shafer techniques in testing structural integrity of aerospace structures

Roberto A. Osegueda, Seetharami R. Seelam, Bharat Mulupuru, Vladik Kreinovich, Tribikram Kundu
2003 Smart Nondestructive Evaluation and Health Monitoring of Structural and Biological Systems II  
a c b e d f h g p i q b 4 r P s u t H v q f x w y f x ¤ f x f h i q g q 9 a s 1 f x f x ) g q i ¥ y d y e y g p y 1 f h g H d i j R g q f h f b l k m S n r R o q p ) f x f r i s f h g x u t ¥ f u k e f  ...  ) ¬ ¶ ¸ h µ § ¥ l q ¥ % § § ¶ ¾ p ¥ r ³ y ¢ © H 8 ¥ l q D § R © r p F ± ) 6 l ¥ 6 p ¿ u 6 ¸ h ¼ s h © ª D h l © ¥ H Ð e ³ Þ ß R © r 6 p AE h © q l ¡ ¤ F ª D G · © G ¢ u D 6 ¥ l p ¬ ¨ D ¼ r · l © U © y  ... 
doi:10.1117/12.483959 fatcat:3ckbg2ckl5gpjdmh42dht3zpri

STATISTICAL AND DEMPSTER-SHAFER TECHNIQUES IN TESTING STRUCTURAL INTEGRITY OF AEROSPACE STRUCTURES

ROBERTO A. OSEGUEDA, SEETHARAMI R. SEELAM, ANA C. HOLGUIN, VLADIK KREINOVICH, CHIN-WANG TAO, HUNG T. NGUYEN
2001 International Journal of Uncertainty Fuzziness and Knowledge-Based Systems  
Osegueda, Seetharami R. Seelam, Ana C. Holguin, Vladik Kreinovich, Chin-Wang Tao, and Hung T. Nguyen This article is available at DigitalCommons@UTEP: https://digitalcommons.utep.edu/cs_techrep/496  ...  i R u D R u d A e g f t D R d h j i D k l U A h m R n k u o q p q n ) r t D R ) t s u i t h v p R p q h w ¤ R s q h d ) n x y j l z A h j y D f { j | i t h v p R p q h n } ĩ 8 H } ' t { X 3 Q u 8 % ' i  ... 
doi:10.1142/s0218488501001204 fatcat:d3kyb3sawvettnltyw2zkl2gdq

Tools for scalable performance analysis on Petascale systems

I-Hsin Chung, S.R. Seelam, B. Mohr, J. Labarta
2009 2009 IEEE International Symposium on Parallel & Distributed Processing  
Seetharami R. Seelam, Ph.D., is a post-doctoral research staff member at the ffiM T. J. Watson Research Center.  ... 
doi:10.1109/ipdps.2009.5160865 dblp:conf/ipps/ChungSML09 fatcat:2y426rdprbb4pa2u4m5g4oahza

Author index

2006 2006 IEEE International Conference on Cluster Computing  
, Seetharami R.  ...  R.  ... 
doi:10.1109/clustr.2006.311921 fatcat:vmbbimypuze7ncjqfonu4po5l4

Topology-aware GPU scheduling for learning workloads in cloud environments

Marcelo Amaral, Jordà Polo, David Carrera, Seetharami Seelam, Malgorzata Steinder
2017 Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '17  
Job's slowdown relative to the best performing con guration.SC17, November 12-17, 2017, Denver, CO, USA Marcelo Amaral, Jordà Polo, David Carrera, Seetharami Seelam, and Malgorzata Steinder 5.5.2 Scenario  ...  0 , P 1 , C) 5: (P 0 .I b , P 1 .I b ) ← getInter(t ask, P 0 , P 1 , A.pr of il e) 6: (P 0 .ω d , P 1 .ω d ) ← getFragmentation(P 0 , P 1 , A) 7: if (U(t ask , P 0 ) ≥ U(t ask , P 1 )) and (const r  ... 
doi:10.1145/3126908.3126933 dblp:conf/sc/AmaralPCSS17 fatcat:vu4i6hn7jbbtjou3f64bkbg4xa

Composing Model-Based Analysis Tools (Dagstuhl Seminar 19481)

Francisco Durán, Robert Heinrich, Diego Pérez-Palacín, Carolyn L. Talcott, Steffen Zschaler
2020 Dagstuhl Reports  
In Seetharami Seelam, Petr Tuma, Giuliano Casale, Tony Field, and José Nelson Amaral, editors, ACM/SPEC International Conference on Performance Engineering, ICPE, pages 311-314.  ...  In Michael Kohlhase, Moa Johansson, Bruce R. Miller, Leonardo de Moura, and Frank Wm.  ... 
doi:10.4230/dagrep.9.11.97 dblp:journals/dagstuhl-reports/DuranHPTZ19 fatcat:f3noqtkj3bg6zdy5fym5yrzuda

Throttling I/O Streams to Accelerate File-IO Performance [chapter]

Seetharami Seelam, Andre Kerstens, Patricia J. Teller
2007 Lecture Notes in Computer Science  
Araunagiri, R. Portillo, and M. Ruiz, for their valuable feedback.  ... 
doi:10.1007/978-3-540-75444-2_67 fatcat:ivn3fummmveb3oy5j2p6tuyoue

Statistical and Dempster-Shafer Techniques in Testing Structural Integrity of Aerospace Structures

R Osegueda
2001 International Journal of Uncertainty Fuzziness and Knowledge-Based Systems  
Osegueda, Seetharami R. Seelam, Ana C. Holguin, Vladik Kreinovich, Chin-Wang Tao, and Hung T. Nguyen This article is available at DigitalCommons@UTEP: https://digitalcommons.utep.edu/cs_techrep/496  ...  i R u D R u d A e g f t D R d h j i D k l U A h m R n k u o q p q n ) r t D R ) t s u i t h v p R p q h w ¤ R s q h d ) n x y j l z A h j y D f { j | i t h v p R p q h n } ĩ 8 H } ' t { X 3 Q u 8 % ' i  ... 
doi:10.1016/s0218-4885(01)00120-4 fatcat:kekonfncxjhtvjefn3wypsmngm

Railgun: managing large streaming windows under MAD requirements [article]

Ana Sofia Gomes, João Oliveirinha, Pedro Cardoso, Pedro Bizarro
2021 arXiv   pre-print
Ter- public - April 21 - 24, 2013, Seetharami Seelam, Petr Tuma, Giuliano Casale, Tony williger. 2015. Trill: Engineering a Library for Diverse Analytics.  ...  Spark Closer to Bare Metal. https://databricks.com/blog/2015/04/28/project- [46] Georgios Theodorakis, Alexandros Koliousis, Peter R. Pietzuch, and Holger Pirk.  ... 
arXiv:2106.12626v1 fatcat:otrohxszszay7dwp4cdmcrfqke