TY - GEN
T1 - GPU Computing for Compute-Intensive Scientific Calculation
AU - Dubey, Sandhya Parasnath
AU - Kumar, M. Sathish
AU - Balaji, S.
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - GPU has emerged as a platform that off-loads computation intensive work from CPU and performs numerical computations in less time. One such mathematical operation is matrix multiplication. Matrix is one of the fundamental mathematical objects used in the scientific calculation, with applicability in various fields such as computer graphics, analysis of electrical circuits, computer networks, DNA sequence comparison, protein structure prediction, etc. This work presents a comparative analysis of scalar matrix multiplication in three modes, namely: (i) sequential programming in C language (ii) parallel implementations using OpenCL, and (iii) MPI. The testbed comprises of input matrices ranging from small size of 100 × 100 to a higher size of 800 × 12,800. We observe that parallel execution in OpenCL outperforms MPI and sequential C for higher dimensional matrices. In contrast, sequential C outperforms both MPI and OpenCL for small dimension matrices. Besides, we analyze that OpenCL program has attained a speedup of 9 ×. Therefore, we conclude that parallel execution of code is more efficient for data of computationally large sizes and hence provides a potentially useful solution to address NP-complete problems.
AB - GPU has emerged as a platform that off-loads computation intensive work from CPU and performs numerical computations in less time. One such mathematical operation is matrix multiplication. Matrix is one of the fundamental mathematical objects used in the scientific calculation, with applicability in various fields such as computer graphics, analysis of electrical circuits, computer networks, DNA sequence comparison, protein structure prediction, etc. This work presents a comparative analysis of scalar matrix multiplication in three modes, namely: (i) sequential programming in C language (ii) parallel implementations using OpenCL, and (iii) MPI. The testbed comprises of input matrices ranging from small size of 100 × 100 to a higher size of 800 × 12,800. We observe that parallel execution in OpenCL outperforms MPI and sequential C for higher dimensional matrices. In contrast, sequential C outperforms both MPI and OpenCL for small dimension matrices. Besides, we analyze that OpenCL program has attained a speedup of 9 ×. Therefore, we conclude that parallel execution of code is more efficient for data of computationally large sizes and hence provides a potentially useful solution to address NP-complete problems.
UR - https://www.scopus.com/pages/publications/85076828734
UR - https://www.scopus.com/pages/publications/85076828734#tab=citedBy
U2 - 10.1007/978-981-15-0184-5_12
DO - 10.1007/978-981-15-0184-5_12
M3 - Conference contribution
AN - SCOPUS:85076828734
SN - 9789811501838
T3 - Advances in Intelligent Systems and Computing
SP - 131
EP - 140
BT - Soft Computing for Problem Solving SocProS 2018, Volume 2
A2 - Das, Kedar Nath
A2 - Bansal, Jagdish Chand
A2 - Deep, Kusum
A2 - Nagar, Atulya K.
A2 - Pathipooranam, Ponnambalam
A2 - Naidu, Rani Chinnappa
PB - Springer Paris
T2 - 8th International Conference on Soft Computing for Problem Solving, SocProS 2018
Y2 - 17 December 2018 through 19 December 2018
ER -