Travelled to:1 × Canada
1 × France
1 × Norway
1 × The Netherlands
1 × United Kingdom
12 × USA
2 × China
2 × Germany
Collaborated with:R.Jin ∅ W.Ma Q.Zhu Y.Su L.Chen L.Chen T.Liu F.Wang V.T.Ravi X.Li J.H.Saltz L.Guo J.Woodring Y.Wang M.Kutlu O.Kurt S.Krishnamoorthy K.Sinha K.Reddy J.Li Q.Su R.Das Y.Xia P.Jiang M.Becchi S.T.Chakradhar M.Zheng F.Qin A.Biswas H.Shen C.Wang D.Polshakov S.Parthasarathy B.Ren J.R.Larus T.Mytkowicz T.Poutanen W.Schulte K.Myers J.Wendelberger J.P.Ahrens L.Weng Ü.V.Çatalyürek T.M.Kurç S.Narayanan
Talks about:data (13) applic (5) distribut (4) parallel (4) stream (4) graph (4) construct (3) support (3) dataset (3) analysi (3)
Person: Gagan Agrawal
DBLP: Agrawal:Gagan
Contributed to:
20152014
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2012
2011
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20112010200920072005
2005200420032002
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1999
19952019Wrote 26 papers:
- HPDC-2015-SuWA #data analysis #generative #performance
- In-Situ Bitmaps Generation and Efficient Data Analysis based on Bitmaps (YS, YW, GA), pp. 61–72.
- HPDC-2014-SuAWBS #analysis #correlation #dataset #distributed #parallel
- Supporting correlation analysis on scientific datasets in parallel and distributed settings (YS, GA, JW, AB, HWS), pp. 191–202.
- CGO-2013-RenALMPS #data type #parallel
- SIMD parallelization of applications that traverse irregular data structures (BR, GA, JRL, TM, TP, WS), p. 10.
- HPDC-2013-SuAWMWA #dataset #distributed #using
- Taming massive distributed datasets: data sampling using bitmap indices (YS, GA, JW, KM, JW, JPA), pp. 13–24.
- HPDC-2012-ChenA #effectiveness #memory management #optimisation #pipes and filters
- Optimizing MapReduce for GPUs with effective shared memory usage (LC, GA), pp. 199–210.
- HPDC-2012-KutluAK #algorithm #data-driven #fault tolerance #parallel
- Fault tolerant parallel data-intensive algorithms (MK, GA, OK), pp. 133–134.
- KDD-2012-LiuA #clustering #data flow #web
- Stratified k-means clustering over a deep web data source (TL, GA), pp. 1113–1121.
- CC-2011-MaKA #memory management #multi
- Practical Loop Transformations for Tensor Contraction Expressions on Multi-level Memory Hierarchies (WM, SK, GA), pp. 266–285.
- CIKM-2011-WangA #data flow #effectiveness #query #web
- Effective stratification for low selectivity queries on deep web data sources (FW, GA), pp. 1455–1464.
- HPDC-2011-RaviBAC #framework #gpu #runtime
- Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework (VTR, MB, GA, STC), pp. 217–228.
- PPoPP-2011-ZhengRQA #detection #gpu #named #source code
- GRace: a low-overhead mechanism for detecting data races in GPU programs (MZ, VTR, FQ, GA), pp. 135–146.
- HPDC-2010-ZhuA #adaptation #constraints #resource management
- Resource provisioning with budget constraints for adaptive applications in cloud environments (QZ, GA), pp. 304–307.
- PPoPP-2009-MaA #compilation #data mining #mining #runtime
- A compiler and runtime system for enabling data mining applications on gpus (WM, GA), pp. 287–288.
- PPoPP-2007-ZhuCA #fault tolerance #grid #streaming
- Supporting fault-tolerance in streaming grid applications (QZ, LC, GA), pp. 156–157.
- KDD-2005-JinSA #information management #mining #optimisation
- Simultaneous optimization of complex mining tasks with a knowledgeable cache (RJ, KS, GA), pp. 600–605.
- KDD-2005-JinWPPA #dataset #graph
- Discovering frequent topological structures from graph datasets (RJ, CW, DP, SP, GA), pp. 606–611.
- VLDB-2005-LiA #evaluation #performance #streaming #xquery
- Efficient Evaluation of XQuery over Streaming Data (XL, GA), pp. 265–276.
- HPDC-2004-ChenRA #data type #distributed #middleware #named
- GATES: A Grid-Based Middleware for Processing Distributed Data Streams (LC, KR, GA), pp. 192–201.
- HPDC-2004-WengACKNS #approach #automation
- An Approach for Automatic Data Virtualization (LW, GA, ÜVÇ, TMK, SN, JHS), pp. 24–33.
- KDD-2003-JinA #performance #streaming
- Efficient decision tree construction on streaming data (RJ, GA), pp. 571–576.
- CC-2002-AgrawalLS #graph
- Evaluating a Demand Driven Technique for Call Graph Construction (GA, JL, QS), pp. 29–45.
- PASTE-2001-AgrawalG #slicing
- Evaluating explicitly context-sensitive program slicing (GA, LG), pp. 6–12.
- CC-2000-Agrawal #graph
- Demand-Driven Construction of Call Graphs (GA), pp. 125–140.
- ICSM-1999-Agrawal #analysis #data flow #graph
- Simultaneous Demand-Driven Data-Flow and Call Graph Analysis (GA), pp. 453–462.
- PLDI-1995-AgrawalSD #compilation #distributed #interprocedural #memory management
- Interprocedural Partial Redundancy Elimination and its Application to Distributed Memory Compilation (GA, JHS, RD), pp. 258–269.
- CC-2019-XiaJA #parallel #re-engineering
- Enabling prefix sum parallelism pattern for recurrences with principled function reconstruction (YX, PJ, GA), pp. 17–28.