The PyGBe code solves the linearized Poisson-Boltzmann equation using a boundary-integral formulation. We use a boundary element method with a collocation approach, and solve it via a Krylov-subspace method. To do this efficiently, the matrix-vector multiplications in the Krylov iterations are accelerated with a treecode, achieving O(N log N) complexity. The code presents a Python environment for the user, while being efficient and fast. The core computational kernels are implemented in Cuda and interface with the user-visible code with PyCuda, for maximum ease-of-use combined with high performance on GPU hardware. This document provides background on the model and formulation of the numerical method, evidence of a validation exercise with well-known benchmarks – a spherical shell with a centered charge and one with an off-center charge- and a demonstration with a realistic biological geometry (lysozyme molecule).<\/p>\n","protected":false},"excerpt":{"rendered":"
The PyGBe code solves the linearized Poisson-Boltzmann equation using a boundary-integral formulation. We use a boundary element method with a collocation approach, and solve it via a Krylov-subspace method. To do this efficiently, the matrix-vector multiplications in the Krylov iterations are accelerated with a treecode, achieving O(N log N) complexity. The code presents a Python […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3,12],"tags":[123,29,668,98,1782,14,354,20,176,1783,227,513,1226],"class_list":["post-8867","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-physics","tag-bioinformatics","tag-biophysics","tag-boltzmann-equation","tag-computational-physics","tag-computer-science","tag-cuda","tag-electrostatics","tag-nvidia","tag-package","tag-physics","tag-poisson-boltzmann","tag-python","tag-tesla-c2075"],"views":2646,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8867","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8867"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8867\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8867"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8867"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8867"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}