Lobeiras Blanco, JacoboAmor, MargaritaDoallo, Ramón2025-01-152025-01-152015Lobeiras, J., Amor, M. & Doallo, R. BPLG: A Tuned Butterfly Processing Library for GPU Architectures. Int J Parallel Prog 43, 1078–1102 (2015). https://doi.org/10.1007/s10766-014-0323-80885-7458http://hdl.handle.net/2183/40722This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10766-014-0323-8[Abstract]: In order to increase the efficiency of existing software many works are incorporating GPU processing. However, despite the current advances in GPU languages and tools, taking advantage of their parallel architecture is still far more complex than programming standard multi-core CPUs. In this work, we present a library based on a set of building blocks that enable to easily design well-known algorithms with little effort. More specifically, we implement butterfly algorithms with this library, that is, a set of orthogonal signal transforms and an algorithm to solve tridiagonal equations systems. Thanks to the parametrization of the building blocks, the library can be easily tuned depending on the desired GPU architecture. This generic approach can be used to easily design these GPU algorithms while obtaining competitive performance on two recent NVIDIA GPU architectures, which results specially interesting from the productivity point of view.engCopyright © 2014, Springer Science Business Media New YorkSignal processingFFTDCTHartleyTridiagonal equation systemGPGPUCUDAtuned libraryjournal articleopen access10.1007/s10766-014-0323-8