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Rupesh Nasre
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2020 – today
- 2024
- [j20]Kevin Jude Concessao, Unnikrishnan Cheramangalath, MJ Ricky Dev, Rupesh Nasre:
Meerkat: A Framework for Dynamic Graph Algorithms on GPUs. Int. J. Parallel Program. 52(5): 400-453 (2024) - [c36]Anju Mongandampulath Akathoott
, Rupesh Nasre
FlexiGran: Flexible Granularity Locking in Hierarchies. Euro-Par (1) 2024: 3-17 - [i9]Ashwina Kumar, M. Venkata Krishna, Prasanna Bartakke, Rahul Kumar, Rajesh Pandian Muniasamy, Nibedita Behera, Rupesh Nasre:
Code Generation for a Variety of Accelerators for a Graph DSL. CoRR abs/2401.02472 (2024) - 2023
- [j19]Anju Mongandampulath Akathoott
Single-linkage clustering of dynamic data. Concurr. Comput. Pract. Exp. 35(1) (2023) - [c35]Rajesh Pandian Muniasamy
Effective Parallelization of the Vehicle Routing Problem. GECCO 2023: 1036-1044 - [c34]Ullas A, Rupesh Nasre, R. Govindarajan:
Reduce, Reuse, and Adapt: Accelerating Graph Processing on GPUs. HiPC 2023: 335-346 - [i8]Nibedita Behera, Ashwina Kumar, Ebenezer Rajadurai T, Sai Nitish, Rajesh Pandian Muniasamy, Rupesh Nasre:
StarPlat: A Versatile DSL for Graph Analytics. CoRR abs/2305.03317 (2023) - [i7]Kevin Jude Concessao, Unnikrishnan Cheramangalath, MJ Ricky Dev, Rupesh Nasre:
Meerkat: A framework for Dynamic Graph Algorithms on GPUs. CoRR abs/2305.17813 (2023) - [i6]Rohith Krishnan S, Venkata Kalyan Tavva, Rupesh Nasre:
A Graph Data Structure to Optimize Dynamic Graph Processing on GPUs. CoRR abs/2306.08252 (2023) - 2022
- [j18]Dileep Kumar Pattipati
BOLD: an ontology-based log debugger for C programs. Autom. Softw. Eng. 29(1): 2 (2022) - [j17]Rajesh Pandian Muniasamy
Accelerating Computation of Steiner Trees on GPUs. Int. J. Parallel Program. 50(1): 152-185 (2022) - [j16]Jash Khatri
Scaling the Maximum Flow Computation on GPUs. Int. J. Parallel Program. 50(5-6): 515-561 (2022) - [j15]Somesh Singh
ParTBC: Faster Estimation of Top-k Betweenness Centrality Vertices on GPU. ACM Trans. Design Autom. Electr. Syst. 27(2): 12:1-12:25 (2022) - [j14]M. A. Anju
Multi-Interval DomLock: Toward Improving Concurrency in Hierarchies. ACM Trans. Parallel Comput. 9(3): 12:1-12:27 (2022) - [j13]Shouvick Mondal
${{\sf Colosseum}}$Colosseum: Regression Test Prioritization by Delta Displacement in Test Coverage. IEEE Trans. Software Eng. 48(10): 4060-4073 (2022) - 2021
- [j12]Pravin Jagtap, Rupesh Nasre, V. S. Sanapala
Efficient parallelization of SPH algorithm on modern multi-core CPUs and massively parallel GPUs. Int. J. Model. Simul. Sci. Comput. 12(6): 2150054:1-2150054:27 (2021) - [j11]Shouvick Mondal
Hansie: Hybrid and consensus regression test prioritization. J. Syst. Softw. 172: 110850 (2021) - [c33]Shouvick Mondal
Summary of Hansie: Hybrid and consensus regression test prioritization. ICST 2021: 278-280 - 2020
- [b1]Unnikrishnan Cheramangalath, Rupesh Nasre, Y. N. Srikant:
Distributed Graph Analytics - Programming, Languages, and Their Compilation. Springer 2020, ISBN 978-3-030-41885-4, pp. 1-208 - [j10]Dileep Kumar Pattipati
OPAL: An extensible framework for ontology-based program analysis. Softw. Pract. Exp. 50(8): 1425-1462 (2020) - [c32]Diptanshu Kakwani, Rupesh Nasre:
Orion: time estimated causally consistent key-value store. PaPoC@EuroSys 2020: 3:1-3:6 - [c31]Chirayu Anant Haryan, G. Ramakrishna, Rupesh Nasre, Allam Dinesh Reddy:
A GPU Algorithm for Earliest Arrival Time Problem in Public Transport Networks. HiPC 2020: 171-180 - [c30]Somesh Singh
Graffix: Efficient Graph Processing with a Tinge of GPU-Specific Approximations. ICPP 2020: 23:1-23:11 - [c29]Bikash Gogoi, Unnikrishnan Cheramangalath
Custom code generation for a graph DSL. GPGPU@PPoPP 2020: 51-60 - [i5]Dileep Kumar Pattipati, Rupesh Nasre, Sreenivasa Kumar P:
BOLD: An Ontology-based Log Debugger for C Programs. CoRR abs/2004.11044 (2020) - [i4]Ashirbad Mishra, Sathish Vadhiyar, Rupesh Nasre, Keshav Pingali:
A Fine-Grained Hybrid CPU-GPU Algorithm for Betweenness Centrality Computations. CoRR abs/2008.05718 (2020)
2010 – 2019
- 2019
- [j9]Prema Soundararajan
A study on popular auto-parallelization frameworks. Concurr. Comput. Pract. Exp. 31(17) (2019) - [j8]Shouvick Mondal
Mahtab: Phase-wise acceleration of regression testing for C. J. Syst. Softw. 158 (2019) - [c28]Saurabh Kalikar, Rupesh Nasre:
Toggle: Contention-Aware Task Scheduler for Concurrent Hierarchical Operations. Euro-Par 2019: 142-155 - [c27]Aman Sharma, Rupesh Nasre:
QADroid: regression event selection for Android applications. ISSTA 2019: 66-77 - [c26]Somesh Singh
Optimizing graph processing on GPUs using approximate computing: poster. PPoPP 2019: 395-396 - [i3]Bikash Gogoi, Unnikrishnan Cheramangalath, Rupesh Nasre:
Custom Code Generation for a Graph DSL. CoRR abs/1903.01665 (2019) - [i2]Chirayu Anant Haryan, G. Ramakrishna, Rupesh Nasre, Allam Dinesh Reddy:
GPU Algorithm for Earliest Arrival Time Problem in Public Transport Networks. CoRR abs/1912.00966 (2019) - 2018
- [j7]Jyothi Krishna V. S
Optimizing Graph Algorithms in Asymmetric Multicore Processors. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 37(11): 2673-2684 (2018) - [j6]Jyothi Krishna Viswakaran Sreelatha
CHOAMP: Cost Based Hardware Optimization for Asymmetric Multicore Processors. IEEE Trans. Multi Scale Comput. Syst. 4(2): 163-176 (2018) - [j5]Somesh Singh
Scalable and Performant Graph Processing on GPUs Using Approximate Computing. IEEE Trans. Multi Scale Comput. Syst. 4(3): 190-203 (2018) - [c25]K. Ganesh, Saurabh Kalikar, Rupesh Nasre:
Multi-granularity Locking in Hierarchies with Synergistic Hierarchical and Fine-Grained Locks. Euro-Par 2018: 546-559 - [c24]Pravin Jagtap, Rupesh Nasre, B. S. V. Patnaik:
Effective Mapping of an SPH Algorithm on Massively Parallel GPU Architecture. HiPC Workshops 2018: 41-45 - [c23]Saurabh Kalikar, Rupesh Nasre:
NumLock: Towards Optimal Multi-Granularity Locking in Hierarchies. ICPP 2018: 75:1-75:10 - 2017
- [j4]Saurabh Kalikar, Rupesh Nasre:
DomLock: A New Multi-Granularity Locking Technique for Hierarchies. ACM Trans. Parallel Comput. 4(2): 7:1-7:29 (2017) - [c22]Unnikrishnan Cheramangalath
DH-Falcon: A Language for Large-Scale Graph Processing on Distributed Heterogeneous Systems. CLUSTER 2017: 439-450 - [c21]Akash Jain, Rupesh Nasre, Balaraman Ravindran
DCEIL: Distributed Community Detection with the CEIL Score. HPCC/SmartCity/DSS 2017: 146-153 - [c20]Gaurav Malhotra, Hitish Chappidi, Rupesh Nasre:
Fast Dynamic Graph Algorithms. LCPC 2017: 262-277 - [i1]Ananya Raval, Rupesh Nasre, Vivek Kumar, R. Vasudevan, Sathish Vadhiyar, Keshav Pingali:
Dynamic Load Balancing Strategies for Graph Applications on GPUs. CoRR abs/1711.00231 (2017) - 2016
- [j3]Fei Liu, Bixin Li, Rupesh Nasre
Efficient online cycle detection technique combining with Steensgaard points-to information. Softw. Pract. Exp. 46(5): 601-623 (2016) - [j2]Unnikrishnan C.
Falcon: A Graph Manipulation Language for Heterogeneous Systems. ACM Trans. Archit. Code Optim. 12(4): 54:1-54:27 (2016) - [c19]Abhinav, Rupesh Nasre
FastCollect: offloading generational garbage collection to integrated GPUs. CASES 2016: 21:1-21:10 - [c18]Sudhir Samrit, Rupesh Nasre
EagerMerge: an optimistic technique for efficient points-to analysis. ISSTA 2016: 47-58 - [c17]G. Shashidhar, Rupesh Nasre
LightHouse: An Automatic Code Generator for Graph Algorithms on GPUs. LCPC 2016: 235-249 - [c16]Shrinivas Devshatwar, Madhur Amilkanthwar, Rupesh Nasre
GPU centric extensions for parallel strongly connected components computation. GPGPU@PPoPP 2016: 2-11 - [c15]Saurabh Kalikar, Rupesh Nasre
DomLock: a new multi-granularity locking technique for hierarchies. PPoPP 2016: 23:1-23:12 - 2014
- [c14]Rupesh Nasre
Auto-parallelization of data structure operations for GPUs. CASES 2014: 7:1-7:10 - [c13]Bollu Ratnakar, Rupesh Nasre
Push-pull constraint graph for efficient points-to analysis. ISMM 2014: 25-33 - 2013
- [j1]Rupesh Nasre
Time- and space-efficient flow-sensitive points-to analysis. ACM Trans. Archit. Code Optim. 10(4): 39:1-39:27 (2013) - [c12]Rupesh Nasre
Atomic-free irregular computations on GPUs. GPGPU@ASPLOS 2013: 96-107 - [c11]Rupesh Nasre
Data-Driven Versus Topology-driven Irregular Computations on GPUs. IPDPS 2013: 463-474 - [c10]Rupesh Nasre
Morph algorithms on GPUs. PPoPP 2013: 147-156 - 2012
- [c9]Sandeep Putta, Rupesh Nasre
Parallel Replication-Based Points-To Analysis. CC 2012: 61-80 - [c8]Martin Burtscher, Rupesh Nasre
A quantitative study of irregular programs on GPUs. IISWC 2012: 141-151 - [c7]Rupesh Nasre
Exploiting the structure of the constraint graph for efficient points-to analysis. ISMM 2012: 121-132 - 2011
- [c6]Rupesh Nasre
Prioritizing constraint evaluation for efficient points-to analysis. CGO 2011: 267-276 - [c5]Arnab De, Deepak D'Souza, Rupesh Nasre
Dataflow Analysis for Datarace-Free Programs. ESOP 2011: 196-215 - [c4]Rupesh Nasre
Approximating inclusion-based points-to analysis. MSPC 2011: 66-73 - 2010
- [c3]Rupesh Nasre
Points-to Analysis as a System of Linear Equations. SAS 2010: 422-438
2000 – 2009
- 2009
- [c2]Rupesh Nasre
Scalable Context-Sensitive Points-to Analysis Using Multi-dimensional Bloom Filters. APLAS 2009: 47-62 - 2003
- [c1]B. Aditya, Soumen Chakrabarti, Rushi Desai, Arvind Hulgeri, Hrishikesh Karambelkar
User Interaction in the BANKS System. ICDE 2003: 786-788
Coauthor Index
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last updated on 2024-10-07 21:22 CEST by the dblp team
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