Proper representation of data in graphical visualizations becomes challenging when high accuracy in data types is required, especially in those situations where the difference between double-precision floating-point and single-precision floating-point values makes a significant difference. Some of the limitations of using single-precision over double-precision include lesser accuracy, which accumulates errors over time, and poor modeling of large or small numbers. In such scenarios, emulated double precision is often used as a solution. The proposed methodology uses a modern GPU pipeline and graphics library API specifications to use native double precision. In this research, the approach is implemented using the Vulkan API, C++, and GLSL. Experimental evaluation with a series of experiments on 2D and 3D point datasets is proposed to indicate the effectiveness of the approach. This evaluates performance comparisons between native double-precision implementations against their emulated double-precision approaches with respect to rendering performance and accuracy. This study provides insight into the benefits of using native double-precision in graphical applications, denoting limitations and problems with emulated double-precision usages. These results improve the general understanding of the precision involved in graphical visualizations and assist developers in making decisions about which precision methods to use during their applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
Proper representation of data in graphical visualizations becomes challenging when high accuracy in data types is required, especially in those situations where the difference between double-precision floating-point and single-precision floating-point values makes a significant difference. Some of the limitations of using single-precision over double-precision include lesser accuracy, which accumulates errors over time, and poor modeling […]<\/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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[1782,889,187,20,2082,176,144,134,1847],"class_list":["post-29359","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-extended-precision","tag-glsl","tag-nvidia","tag-nvidia-geforce-rtx-3090","tag-package","tag-rendering","tag-visualization","tag-vulkan"],"views":1758,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29359","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=29359"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29359\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29359"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29359"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29359"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}