Vivek Pallipuram
Cited by
Cited by
Acceleration of spiking neural networks in emerging multi-core and GPU architectures
MA Bhuiyan, VK Pallipuram, MC Smith, T Taha, R Jalasutram
2010 IEEE International Symposium on Parallel & Distributed Processing …, 2010
A comparative study of GPU programming models and architectures using neural networks
VK Pallipuram, M Bhuiyan, MC Smith
The Journal of Supercomputing 61 (3), 673-718, 2012
Data migration algorithms in heterogeneous storage systems: A comparative performance evaluation
G Roberts, S Chen, C Kari, V Pallipuram
2017 IEEE 16th International Symposium on Network Computing and Applications …, 2017
Dynamic localization of HmpF regulates type IV pilus activity and directional motility in the filamentous cyanobacterium Nostoc punctiforme
YW Cho, A Gonzales, TV Harwood, J Huynh, Y Hwang, JS Park, AQ Trieu, ...
Molecular microbiology 106 (2), 252-265, 2017
Optimization and performance study of large-scale biological networks for reconfigurable computing
MA Bhuiyan, A Nallamuthu, MC Smith, VK Pallipuram
2010 Fourth International Workshop On High-Performance Reconfigurable …, 2010
Evaluation of GPU architectures using spiking neural networks
VK Pallipuram, MA Bhuiyan, MC Smith
2011 Symposium on Application Accelerators in High-Performance Computing, 93-102, 2011
Acceleration of spiking neural networks on single-GPU and multi-GPU systems
VK Pallipuram
Master's Thesis, 2010
Acceleration of spiking neural networks on single-gpu and multi-gpu systems
V Vivek, P Krishnamani
ProQuest document ID 204037751, 2010
A multi-node GPGPU implementation of non-linear anisotropic diffusion filter
VK Pallipuram, N Raut, X Ren, MC Smith, S Naik
2012 Symposium on Application Accelerators in High Performance Computing, 11-18, 2012
Performance, optimization, and fitness: Connecting applications to architectures
MA Bhuiyan, MC Smith, VK Pallipuram
Concurrency and Computation: Practice and Experience 23 (10), 1066-1100, 2011
A best-features based digital rotoscope
I Murphy, T Norlund, VK Pallipuram
2017 51st Asilomar Conference on Signals, Systems, and Computers, 243-247, 2017
From HPC performance to climate modeling: Transforming methods for HPC predictions into models of extreme climate conditions
R McKinney, VK Pallipuram, R Vargas, M Taufer
2015 IEEE 11th International Conference on e-Science, 108-117, 2015
Exploring multi-level parallelism for large-scale spiking neural networks
VK Pallipuram, MC Smith, N Raut, X Ren
Proceedings of the International Conference on Parallel and Distributed …, 2012
A2cloud: An analytical model for application-to-cloud matching to empower scientific computing
C Balos, D De La Vega, Z Abuelhaj, C Kari, D Mueller, VK Pallipuram
2018 IEEE 11th International Conference on Cloud Computing (CLOUD), 548-555, 2018
A regression‐based performance prediction framework for synchronous iterative algorithms on general purpose graphical processing unit clusters
VK Pallipuram, MC Smith, N Raut, X Ren
Concurrency and Computation: Practice and Experience 26 (2), 532-560, 2014
Subjective versus objective: classifying analytical models for productive heterogeneous performance prediction
VK Pallipuram, MC Smith, N Sarma, R Anand, E Weill, K Sapra
The Journal of Supercomputing 71 (1), 162-201, 2015
Exploring multiple levels of performance modeling for heterogeneous systems
VVP Krishnamani
Clemson University, 2013
Data migration in large scale heterogeneous storage systems with nodes to spare
C Kari, S Chen, S Amir-Mohammadian, V Pallipuram
2019 International Conference on Computing, Networking and Communications …, 2019
Applying frequency analysis techniques to dag-based workflows to benchmark and predict resource behavior on non-dedicated clusters
VK Pallipuram, J DiMarco, M Taufer
2014 IEEE International Conference on Cluster Computing (CLUSTER), 29-37, 2014
A2Cloud‐RF: A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud
D Samuel, S Khan, CJ Balos, Z Abuelhaj, AD Dutoi, C Kari, D Mueller, ...
Concurrency and Computation: Practice and Experience 32 (24), e5942, 2020
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