The Impact of Address Arithmetic on the GPU Implementation of Fast Algorithms for the Vilenkin-Chrestenson Transform

Gajić, Dušan; Stanković, Radomir S.

doi:10.1109/ismvl.2013.59

Cited by 4 publications

(2 citation statements)

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“…In the proposed mappings, computations involved in the elementary butterflies of the considered fast algorithms are mapped to device programs, processed on the GPU, while the rest of the procedures are performed as parts of the CPU-processed host programs [6,9]. Each thread performs a radix-4 butterfly over different input function values and the number of threads in each algorithm step is, therefore, N/4.…”

Section: Mapping Of the Algorithmsmentioning

confidence: 99%

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Computation of Galois field expressions for quaternary logic functions on GPUs

Gajić

2014

Serb J Electr Eng

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Galois field (GF) expressions are polynomials used as representations of multiple-valued logic (MVL) functions. For this purpose, MVL functions are considered as functions defined over a finite (Galois) field of order p - GF(p). The problem of computing these functional expressions has an important role in areas such as digital signal processing and logic design. Time needed for computing GF-expressions increases exponentially with the number of variables in MVL functions and, as a result, it often represents a limiting factor in applications. This paper proposes a method for an accelerated computation of GF(4)-expressions for quaternary (four-valued) logic functions using graphics processing units (GPUs). The method is based on the spectral interpretation of GF-expressions, permitting the use of fast Fourier transform (FFT)-like algorithms for their computation. These algorithms are then adapted for highly parallel processing on GPUs. The performance of the proposed solutions is compared with referent C/C++ implementations of the same algorithms processed on central processing units (CPUs). Experimental results confirm that the presented approach leads to significant reduction in processing times (up to 10.86 times when compared to CPU processing). Therefore, the proposed approach widens the set of problem instances which can be efficiently handled in practice. [Projekat Ministarstva nauke Republike Srbije, br. ON174026 i br. III44006]

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Section: Mapping Of the Algorithmsmentioning

confidence: 99%

“…As a consequence, GPU implementations of these two algorithms require different amounts of GPU resources, in terms of registers, number of instructions, and occupancy [6,14].…”

Section: Implementation Of the Algorithmsmentioning

confidence: 99%