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<p><it>Abstract—</it>Although fast Hartley transform (FHT) provides efficient spectral analysis of real discrete signals, the literature that addresses the parallelization of FHT is extremely rare. FHT is a real transformation and does not necessitate any complex arithmetics. On the other hand, FHT algorithm has an irregular computational structure which makes efficient parallelization harder. In this paper, we propose a efficient restructuring for the sequential FHT algorithm which brings regularity and symmetry to the computational structure of the FHT. Then, we propose an efficient parallel FHT algorithm for medium-to-coarse grain hypercube multicomputers by introducing a dynamic mapping scheme for the restructured FHT. The proposed parallel algorithm achieves perfect load-balance, minimizes both the number and volume of concurrent communications, allows only nearest-neighbor communications and achieves in-place computation and communication. The proposed algorithm is implemented on a 32-node iPSC/2<ref type="fn" fid="FD05611">1</ref> hypercube multicomputer. High-efficiency values are obtained even for small size FHT problems.</p><p><it>Index Terms—</it>Digital signal processing, fast Hartley transform, parallel computing, multicomputer, hypercube, load balance, nearest-neighbor communication.</p>

A. Dervic s and C. Aykanat, "Efficient Fast Hartley Transform Algorithms for Hypercube-Connected Multicomputers," in IEEE Transactions on Parallel & Distributed Systems, vol. 6, no. , pp. 561-577, 1995.
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