8 research outputs found
On the efficient parallel computation of Legendre transforms
In this article, we discuss a parallel implementation of efficient algorithms for computation of Legendre polynomial transforms and other orthogonal polynomial transforms. We develop an approach to the Driscoll-Healy algorithm using polynomial arithmetic and present experimental results on the accuracy, efficiency, and scalability of our implementation. The algorithms were implemented in ANSI C using the BSPlib communications library. We also present a new algorithm for computing the cosine transform of two vectors at the same time
Parallel Spherical Harmonic Transforms on heterogeneous architectures (GPUs/multi-core CPUs)
Spherical Harmonic Transforms (SHT) are at the heart of many scientific and
practical applications ranging from climate modelling to cosmological
observations. In many of these areas new, cutting-edge science goals have been
recently proposed requiring simulations and analyses of experimental or
observational data at very high resolutions and of unprecedented volumes. Both
these aspects pose formidable challenge for the currently existing
implementations of the transforms.
This paper describes parallel algorithms for computing SHT with two variants
of intra-node parallelism appropriate for novel supercomputer architectures,
multi-core processors and Graphic Processing Units (GPU). It also discusses
their performance, alone and embedded within a top-level, MPI-based
parallelisation layer ported from the S2HAT library, in terms of their
accuracy, overall efficiency and scalability. We show that our inverse SHT run
on GeForce 400 Series GPUs equipped with latest CUDA architecture ("Fermi")
outperforms the state of the art implementation for a multi-core processor
executed on a current Intel Core i7-2600K. Furthermore, we show that an
MPI/CUDA version of the inverse transform run on a cluster of 128 Nvidia Tesla
S1070 is as much as 3 times faster than the hybrid MPI/OpenMP version executed
on the same number of quad-core processors Intel Nahalem for problem sizes
motivated by our target applications. Performance of the direct transforms is
however found to be at the best comparable in these cases. We discuss in detail
the algorithmic solutions devised for major steps involved in the transforms
calculation, emphasising those with a major impact on their overall
performance, and elucidates the sources of the dichotomy between the direct and
the inverse operations
On the Efficient Parallel Computation of Legendre Transforms
In this article, we discuss a parallel implementation of efficient algorithms for computation of Legendre polynomial transforms and other orthogonal polynomial transforms. We develop an approach to the Driscoll-Healy algorithm using polynomial arithmetic and present experimental results on the accuracy, efficiency, and scalability of our implementation. The algorithms were implemented in ANSI C using the BSPlib communications library. We also present a new algorithm for computing the cosine transform of two vectors at the same time
On the efficient parallel computation of Legendre transforms
In this article we discuss a parallel implementation of efficient algorithms for computation of Legendre polynomial transforms and other orthogonal polynomial transforms. We develop an approach to the Driscoll-Healy algorithm using polynomial arithmetic and present experimental results on the accuracy efficiency and scalability of our implementation. The algorithms were implemented in ANSI C using the BSPlib communications library. We also present a new algorithm for computing the cosine transform of two vectors at the same tim
On The Efficient Parallel Computation Of Legendre Transforms
In this article, we discuss a parallel implementation of efficient algorithms for computation of Legendre polynomial transforms and other orthogonal polynomial transforms. We develop an approach to the Driscoll-Healy algorithm using polynomial arithmetic and present experimental results on the accuracy, efficiency, and scalability of our implementation. The algorithms were implemented in ANSI C using the BSPlib communications library. We also present a new algorithm for computing the cosine transform of two vectors at the same time