In the course of less than a decade, Graphics Processing Units (GPUs) have evolved from narrowly scoped application specific accelerators to general-purpose parallel machines capable of accommodating an ever-growing set of algorithms. At the same time, programming GPUs appears to have become trapped around an attractor characterised by ad-hoc practices, non-portable implementations and inexact, uninformative performance reporting. The purpose of this paper is two-fold, on one hand pursuing an in-depth look at GPU hardware and its characteristics, and on the other demonstrating that portable, generic, mathematically grounded programming of these machines is possible and desirable. An agent-based meta-heuristic, the Max-Min Ant System (MMAS), provides the context. The major contributions brought about by this article are the following: (1) an optimal, portable, generic-algorithm based MMAS implementation is derived; (2) an in-depth analysis of AMD’s Graphics Core Next (GCN) GPU and the C++ AMP programming model is supplied; (3) a more robust approach to performance reporting is presented; (4) novel techniques for raising the abstraction level without sacrificing performance are employed. This represents the first implementation of an algorithm from the Ant Colony Optimisation (ACO) family using C++ AMP, whilst at the same time being one of the first uses of the latter programming environment.<\/p>\n","protected":false},"excerpt":{"rendered":"
In the course of less than a decade, Graphics Processing Units (GPUs) have evolved from narrowly scoped application specific accelerators to general-purpose parallel machines capable of accommodating an ever-growing set of algorithms. At the same time, programming GPUs appears to have become trapped around an attractor characterised by ad-hoc practices, non-portable implementations and inexact, uninformative […]<\/p>\n","protected":false},"author":584,"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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[3],"tags":[39,1604,1641],"class_list":["post-12751","post","type-post","status-publish","format-standard","hentry","category-paper","tag-algorithm-optimization","tag-ant-colony-optimization","tag-c-amp"],"views":2634,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12751","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\/584"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12751"}],"version-history":[{"count":2,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12751\/revisions"}],"predecessor-version":[{"id":12753,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12751\/revisions\/12753"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12751"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12751"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12751"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}