Dr. Chandrashekhar B N

@nmit.ac.in

ASSOCIATE PROFESSOR AND INFORMATION SCIENCE AND ENGINEERING
Nitte Meenakshi Institute of Technology.

Dr. Chandrashekhar B N


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https://researchid.co/cnaikodi
Dr. B.N Chandrashekhar is an associate professor at Nitte Meenakshi Institute of Technology. He received a BE degree in computer science and engineering from the Visvesvaraya Technological University, India, in 2004 and an M.Tech degree in computer science and engineering from Visvesvaraya Technological University, India, in 2010. He obtained his Ph.D. in computer science engineering from Visvesvaraya Technological University, India, in 2021. His research interests include Hybrid (CPU-GPU) computing, parallel and distributed systems, and performance modeling of parallel HPC applications. He has published papers in peer-reviewed journals and conference proceedings and book chapters. Currently, he is working as an Associate Professor in the Department of Information Science and Engineering at Nitte Meenakshi Institute of Technology, Bengaluru, India.
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EDUCATION

Ph.D. awarded Hybrid (CPU+GPU) Computing, “Performance Driven framework on HPC Application on CPU-GPU hybrid platform” in the year of 2021, from the research center of Nitte Meenakshi Institute of Technologies affiliated to Visveshwaraya Technological University (VTU), Belagavi, Karnataka.

 Master’s Degree in Engineering (First Class - M. Tech) in Computer Science in the year of 2010, from Nitte Meenakshi Institute of Technologies, Bangalore under Visveshwaraya Technological University, Belgaum, Karnataka.

 Bachelor’s Degree in Engineering (First Class -B. E) in Computer Science in the year 2004, from P.D.A. college of engineering, Gulbarga under Visveshwaraya Technological University, Belgaum, Karnataka.

 3 years Diploma Computer science Engineering in the year of 1999 Government polytechnic Gulbarga, under Board of Technical Education, Karnataka.

RESEARCH INTERESTS

 Hybrid [CPU-GPU] Computing, Parallel Computing, Cloud Computing
 Performance Modeling of parallel HPC applications, workload division and scheduling of HPC applications in Hybrid computing
 Artificial Intelligence and Machine learning in Hybrid computing

FUTURE PROJECTS

Balancing of Web Applications Workload Using Hybrid Computing (CPU-GPU) Architecture

In the current network system, there is no proper workload management of web applications and monitor the collaboration between users and web applications. Due to the truancy of centralized management in the mainstream network system, misgivings faced are proper memory ratio, data transfer between user and device, host outstanding methods, and tenure utilization of CPUs and GPUs. In order to make the graphical resources highly available to the network environment, it is important to have an efficient and enhanced service of the hybrid web application workload balancing model. It automates tasks that should be processed and reduces the overall time in processing, mitigating administrative costs and lesser Processing time. Service delivery is also a part of hybrid computing it governs where each job provisions the respective services with respect to each user. Each user’s information is stored in the database and used for authentication which will reduce the time of login each time users


Applications Invited
20

Scopus Publications

Scopus Publications

  • Impact of Hybrid [CPU+GPU] HPC Infrastructure on AI/ML Techniques in Industry 4.0
    B. N. Chandrashekhar, H. A. Sanjay, V. Geetha
    AI Driven Digital Twin and Industry 4 0 A Conceptual Framework with Applications, 2024
  • Adaptive Inertia Weight with Transient Search Optimization Based Feature Selection for Intrusion Detection in Internet of Things
    International Journal of Intelligent Engineering and Systems, 2024
  • Impact of Hybrid [CPU-GPU] Architecture on Machine Learning-based Image-to-Image Translation Using HiDT
    Kantharaju V, Chandrashekhar B N, Niranjanamurthy M, Murthy Svn
    2024 International Conference on Knowledge Engineering and Communication Systems Ickecs 2024, 2024
    Image-to-image translation is the process of transforming an image from one domain to another, where the goal is to learn the mapping between an input image and an output image. This task has been generally performed by using a training set of aligned image pairs on fewer cores-based CPU-based architecture, which mainly aims to transfer images from a source domain to a target domain while preserving the content representations by consuming more execution time. Due to its broad range of applications in numerous computer vision and image processing problems, including image synthesis, segmentation, style transfer, restoration, and pose estimation, GPU-based Image-to-image has attracted growing attention and made enormous progress in recent years. It can be utilized for a variation of principles, including photo enhancement, object transformation, season transfer, and collection style transfer. Only CPU and only GPU-based architecture are difficult in order to speed up the image processing task, especially during re-rendering the same scene under various illuminations characteristic for day, night, or dawn. To address this issue, in this work, we are proposing the Hybrid CPU-GPU-based architecture with HiDT technology for implementing the image translation works at tremendous speed. On the hybrid CPU-GPU-based architecture, it is possible to train a multi-domain image-to-image translation model with HiDT on variable size of dataset unaligned images without domain labels using this technology when it is integrated into an application. The speed of the mentioned application can be achieved by using emerging technologies such as pix2pixHD and HiDT on hybrid architecture, where pix2pixHD is a deep learning-based technique for high-resolution photorealistic image-to-image translation, and it is implemented in PyTorch. This article represents Impact of Hybrid Architecture on Machine Learning-based Image-toImage Translation Using HiDT.
  • Elliptic Curve Cryptography and Password Based Key Derivation Function with Advanced Encryption Standard Method for Cloud Data Security
    International Journal of Intelligent Engineering and Systems, 2024
  • Magnetic Coupling Resonant Wireless Power Transmission
    B. A. Manjunatha, K. Aditya Shatry, P. Kishor Kumar Naik, B. N. Chandrashekhar
    Lecture Notes in Electrical Engineering, 2024
  • Balancing of Web Applications Workload Using Hybrid Computing (CPU–GPU) Architecture
    B. N. Chandrashekhar, V. Kantharaju, N. Harish Kumar, Lithin Kumble
    SN Computer Science, 2024
  • High-Performance Computing with Artificial Intelligence Benefits for the Civilization Impacted by the COVID-19 Pandemic
    B. N. Chandrashekhar, H. A. Sanjay
    Lecture Notes in Networks and Systems, 2023
  • Accelerating Real-Time Face Detection Using Cascade Classifier on Hybrid [CPU-GPU] HPC Infrastructure
    B. N. Chandrashekhar, H. A. Sanjay
    Lecture Notes in Electrical Engineering, 2023
  • Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture
    Chandrashekhar B N, Mohan M, Geetha V
    2023 3rd International Conference on Intelligent Technologies Conit 2023, 2023
    Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases.
  • Performance Model of HPC Application On CPU-GPU Platform*
    B. N Chandrashekar, K. Aditya Shastry, B.A Manjunath, V. Geetha
    Mysurucon 2022 2022 IEEE 2nd Mysore Sub Section International Conference, 2022
    In recent years, the world of high-performance computing has been developing rapidly with enormous efforts in the integration of information technology and research. The emergence of CPU-GPU platform computing has made this possible in a very efficient manner. Nowadays, the graphic processing unit (GPU) delivers much better performance than the CPU, because of a few cores with lots of cache memory on the CPU that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously. The CPU-GPU hybrid platform is becoming increasingly important in high-performance computing (HPC) domains such as deep learning, artificial intelligence, etc., because of its tremendous computing power. In this work, we have proposed a performance model to accelerate the performance of HPC applications on a hybrid CPU-GPU platform. We have tested and analyzed the proposed performance model using different HPC benchmark applications such as Merge sort and Matrix multiplication on different platforms such as sequential, OpenMP, MPI in a single system, MPI in the cluster, and CUDA. We have observed that parallel computing in a shared and distributed memory architecture gives better performance than sequential computing. After analyzing we have represented it in the terms of graphs for a better view of the results. Index Terms—hybrid computing, parallel computing, sequential computing, CUDA, MPI, OpenMP, CPU, GPU.
  • Performance Analysis of Parallel Programming Paradigms on CPU-GPU Clusters
    B N Chandrashekhar, H A Sanjay, Tulasi Srinivas
    Proceedings International Conference on Artificial Intelligence and Smart Systems Icais 2021, 2021
  • Performance analysis of sequential and parallel programming paradigms on CPU-GPUs Cluster
    B N Chandrashekhar, H A Sanjay
    Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks Icicv 2021, 2021
  • Prediction Model of an HPC Application on CPU-GPU Cluster using Machine Learning Techniques
    B N Chandrashekhar, H.A Sanjay
    2nd International Conference on Innovative Mechanisms for Industry Applications Icimia 2020 Conference Proceedings, 2020
  • Prediction Model for Scheduling an Irregular Graph Algorithms on CPU-GPU Hybrid Cluster Framework
    B.N. Chandrashekhar, H.A. Sanjay, H. Lakshmi
    Proceedings of the 5th International Conference on Inventive Computation Technologies Icict 2020, 2020
  • Performance Framework for HPC Applications on Homogeneous Computing Platform
    Chandrashekhar B. N, Sanjay H. A
    International Journal of Image Graphics and Signal Processing, 2019
  • Performance study of openMP and hybrid programming models on CPU–GPU cluster
    B. N. Chandrashekhar, H. A. Sanjay
    Advances in Intelligent Systems and Computing, 2019
  • Dynamic work load balancing for compute intensive application using parallel and hybrid programming models on CPU-GPU cluster
    B. N Chandrashekhar, H. A Sanjay
    Journal of Computational and Theoretical Nanoscience, 2018
  • Implementation of image inpainting using OpenCV and CUDA on CPU-GPU environment
    9th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2018, 2018
  • Fully anonymous attribute-based encryption with privacy and access privilege
    Kartik, Chandrasekhar B N, Lakshmi H
    2016 International Conference on Computation System and Information Technology for Sustainable Solutions Csitss 2016, 2016
  • Parameters tuning of OLSR routing protocol with metaheuristic algorithm for VANET
    Anusha Bandi, Chandrashekhar B. N
    Souvenir of the 2015 IEEE International Advance Computing Conference Iacc 2015, 2015

Publications

1) B. N. Chandrashekhar and H. A. Sanjay, “Accelerating Real-Time Face detection using Cascade Classifier on Hybrid [CPU-GPU] HPC infrastructure”, Seventh International Conference on “Emerging Research in Computing, Information, Communication, and Applications”, (ERCICA-2022) was held in BLENDED MODE during 25th-26th February 2022 at Nitte Meenakshi Institute of Technology (NMIT), Bangalore Springer Singapore. ISBN 978-981-19-5481-8 [SCOPUS]

2) B. N. Chandrashekhar, H. A. Sanjay and T. Srinivas, "Performance Analysis of Parallel Programming Paradigms on CPU-GPU Clusters," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), ©2021 IEEE, pp. 646-651, doi: 10.1109/. [SCOPUS]
3) B. N. Chandrashekhar, H. A. Sanjay, " Performance Analysis of Sequential and Parallel Programming Paradigms on CPU-GPUs Cluster", IEEE 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV 2021) India | 978-1-6654-1960-4/20/$31.00 pp. 1205-1213 ©2021 IEEE | DOI: 10.1109/ [SCOPUS]
4) B. N. Chandrashekhar, H. A. Sanjay, and H. Lakshmi, "Prediction Model for Scheduling an Irregular Graph Algorithms on CPU–GPU Hybrid Cluster Framework", 2020 IEEE International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 584-589, DOI: 10.1109/. [SCOPUS]
5) B. N. Chandrashekhar and H. A. Sanjay, "Prediction Model of an HPC Application

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

conferences and journals

Industry, Institute, or Organisation Collaboration

loadsaharing technology

INDUSTRY EXPERIENCE

 Worked as a Software Engineer for HexawareTechnology, Chennai. From Feb 2005 to Mar 2006

 Worked as a Software Engineer for Global Softech, Bangalore. From June 2004 to Feb 2005.