TY - GEN
T1 - Parallelization of Pigeonhole Sort for Efficient Data Sorting
AU - Datta, Pasupuleti Rohith Sai
AU - Kamath, Chinmaya D.
AU - Kini, N. Gopalakrishna
AU - Rao, Ashwath B.
N1 - Publisher Copyright:
© Grenze Scientific Society, 2024.
PY - 2024
Y1 - 2024
N2 - The need for parallel sorting algorithms have been driven by the increasing need for large-scale datasets to be processed efficiently.Pigeonhole sorting is one of the sorting algorithms that carries sorting in linear time.This study focuses on enhancing the efficacy of the Pigeonhole Sorting method to improve the performance of the algorithm by employing parallel programming techniques specifically Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA).The primary objective is to develop and assess parallel solutions for Pigeonhole Sorting, with the aim of optimizing sorting efficiency in data-intensive applications.Commencing with a comprehensive analysis of the sequential design of the Pigeonhole Sorting algorithm, this work proceeds to create parallel implementations using CUDA for Graphics Processing Unit (GPU) acceleration and MPI for distributed memory parallelism.This work contributes valuable insights into adapting the Pigeonhole Sorting algorithm to parallel contexts.The findings emphasize the potential advantages of parallelization in reducing the overall computation time.
AB - The need for parallel sorting algorithms have been driven by the increasing need for large-scale datasets to be processed efficiently.Pigeonhole sorting is one of the sorting algorithms that carries sorting in linear time.This study focuses on enhancing the efficacy of the Pigeonhole Sorting method to improve the performance of the algorithm by employing parallel programming techniques specifically Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA).The primary objective is to develop and assess parallel solutions for Pigeonhole Sorting, with the aim of optimizing sorting efficiency in data-intensive applications.Commencing with a comprehensive analysis of the sequential design of the Pigeonhole Sorting algorithm, this work proceeds to create parallel implementations using CUDA for Graphics Processing Unit (GPU) acceleration and MPI for distributed memory parallelism.This work contributes valuable insights into adapting the Pigeonhole Sorting algorithm to parallel contexts.The findings emphasize the potential advantages of parallelization in reducing the overall computation time.
UR - https://www.scopus.com/pages/publications/85209071499
UR - https://www.scopus.com/pages/publications/85209071499#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85209071499
T3 - 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024
SP - 6016
EP - 6021
BT - 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024
A2 - Stephen, Janahanlal
A2 - Sharma, Parveen
A2 - Chaba, Yogesh
A2 - Abraham, K. U.
A2 - Anooj, P.K.
A2 - Mohammad, Noor
A2 - Thomas, Gylson
A2 - Srikiran, Satuluri
PB - Grenze Scientific Society
T2 - 15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024
Y2 - 21 June 2024 through 22 June 2024
ER -