A parallel generalized relaxation method for high-performance image segmentation on GPUs

• Pasqua D'Ambra ^[1] ; Salvatore Filippone ^[2]
  1. [1] Institute for High-Performance Computing and Networking, Naples, Italy
  2. [2] University of Rome “Tor Vergata”, Italy
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 293, Nº 1 (February 2016), 2016, págs. 35-44
• Idioma: inglés
• DOI: 10.1016/j.cam.2015.04.035
• Enlaces
  • Texto completo
• Resumen

  • Fast 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 experiments.