Browsing by Author "Pal, H."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Parallel OpenMP and CUDA Implementations of the N-Body Problem
    (2019) Gangavarapu, T.; Pal, H.; Prakash, P.; Hegde, S.; Geetha, V.
    The N-body problem, in the field of astrophysics, predicts the movements of the planets and their gravitational interactions. This paper aims at developing efficient and high-performance implementations of two versions of the N-body problem. Adaptive tree structures are widely used in N-body simulations. Building and storing the tree and the need for work-load balancing pose significant challenges in high-performance implementations. Our implementations use various cores in CPU and GPU via efficient work-load balancing with data and task parallelization. The contributions include OpenMP and Nvidia CUDA implementations to parallelize force computation and mass distribution, and achieve competitive performance in terms of speedup and running time which is empirically justified and graphed. This research not only aids as an alternative to complex simulations but also to other big data applications requiring work-load distribution and computationally expensive procedures. � 2019, Springer Nature Switzerland AG.
  • No Thumbnail Available
    Item
    Parallel OpenMP and CUDA Implementations of the N-Body Problem
    (Springer Verlag service@springer.de, 2019) Gangavarapu, T.; Pal, H.; Prakash, P.; Hegde, S.; Geetha, V.
    The N-body problem, in the field of astrophysics, predicts the movements of the planets and their gravitational interactions. This paper aims at developing efficient and high-performance implementations of two versions of the N-body problem. Adaptive tree structures are widely used in N-body simulations. Building and storing the tree and the need for work-load balancing pose significant challenges in high-performance implementations. Our implementations use various cores in CPU and GPU via efficient work-load balancing with data and task parallelization. The contributions include OpenMP and Nvidia CUDA implementations to parallelize force computation and mass distribution, and achieve competitive performance in terms of speedup and running time which is empirically justified and graphed. This research not only aids as an alternative to complex simulations but also to other big data applications requiring work-load distribution and computationally expensive procedures. © 2019, Springer Nature Switzerland AG.