D’Ambra, PasquaFilippone, Salvatore2020-01-202020-01-202015-05-01Filippone S, D'Ambra P. (2016) A parallel generalized relaxation method for high-performance image segmentation on GPUs. Journal of Computational and Applied Mathematics, Volume 293, February 2016, pp. 35-440377-0427https://doi.org/10.1016/j.cam.2015.04.035https://dspace.lib.cranfield.ac.uk/handle/1826/14959Fast and scalable software modules for image segmentation are needed for modern high-throughput screening platforms in Computational Biology. Indeed, accurate segmentation is one of the main steps to be applied in a basic software pipeline aimed to extract accurate measurements from a large amount of images. Image segmentation is often formulated through a variational principle, where the solution is the minimum of a suitable functional, as in the case of the Ambrosio–Tortorelli model. Euler–Lagrange equations associated with the above model are a system of two coupled elliptic partial differential equations whose finite-difference discretization can be efficiently solved by a generalized relaxation method, such as Jacobi or Gauss–Seidel, corresponding to a first-order alternating minimization scheme. In this work we present a parallel software module for image segmentation based on the Parallel Sparse Basic Linear Algebra Subprograms (PSBLAS), a general-purpose library for parallel sparse matrix computations, using its Graphics Processing Unit (GPU) extensions that allow us to exploit in a simple and transparent way the performance capabilities of both multi-core CPUs and of many-core GPUs. We discuss performance results in terms of execution times and speed-up of the segmentation module running on GPU as well as on multi-core CPUs, in the analysis of 2D gray-scale images of mouse embryonic stem cells colonies coming from biological experimentsenAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Image segmentationVariational modelsRelaxation methodsGPUArticle