CuThomasBatch and cuThomasVBatch, CUDA Routines to compute batch of tridiagonal systems on NVIDIA GPUs. (27th August 2018)

• Record Type: Journal Article
• Authors: Valero‐Lara, Pedro; Martínez‐Pérez, Ivan; Sirvent, Raül; Martorell, Xavier; Peña, Antonio J.
• Other Names: Laccetti Giuliano guestEditor.; Lapegna Marco guestEditor.; Montella Raffaele guestEditor.; Kosta Sokol guestEditor.; Wu Shaofei guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 24(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-27
• Subjects: CR -- CUDA -- cuSPARSE -- parallel processing -- PCR -- scalability -- Thomas algorithm -- tridiagonal linear systems; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4909
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The solving of tridiagonal systems is one of the most computationally expensive parts in many applications, so that multiple studies have explored the use of NVIDIA GPUs to accelerate such computation. However, these studies have mainly focused on using parallel algorithms to compute such systems, which can efficiently exploit the shared memory and are able to saturate the GPUs capacity with a low number of systems, presenting a poor scalability when dealing with a relatively high number of systems. The gtsvStridedBatch routine in the cuSPARSE NVIDIA package is one of these examples, which is used as reference in this article. We propose a new implementation ( cuThomasBatch ) based on the Thomas algorithm. Unlike other algorithms, the Thomas algorithm is sequential, and so a coarse‐grained approach is implemented where one CUDA thread solves a complete tridiagonal system instead of one CUDA block as in gtsvStridedBatch . To achieve a good scalability using this approach, it is necessary to carry out a transformation in the way that the inputs are stored in memory to exploit coalescence (contiguous threads access to contiguous memory locations). Different variants regarding the transformation of the data are explored in detail. We also explore some variants for the case of variable batch, when the size of the systems of the batch has different size ( cuThomasVBatch ). The results given in this study prove that the implementations carried out in this work are able to beat the reference code, being up to 5× (in double precision) and 6× (in single precision) faster using the latest NVIDIA GPU architecture, the Pascal P100.