{"title":"Mach-RT: a many chip architecture for ray tracing","authors":"Elena Vasiou, K. Shkurko, E. Brunvand, Cem Yuksel","doi":"10.2312/hpg.20191188","DOIUrl":"https://doi.org/10.2312/hpg.20191188","url":null,"abstract":"We propose an unconventional solution to high-performance ray tracing that combines a ray ordering scheme that minimizes access to the scene data with a large on-chip buffer acting as near-compute storage that is spread over multiple chips. We demonstrate the effectiveness of our approach by introducing Mach-RT (Many chip - Ray Tracing), a new hardware architecture for accelerating ray tracing. Extending the concept of dual streaming, we optimize the main memory accesses to a level that allows the same memory system to service multiple processor chips at the same time. While a multiple chip solution might seem to imply increased energy consumption as well, because of the reduced memory traffic we are able to demonstrate, performance increases while maintaining reasonable energy usage compared to academic and commercial architectures.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125343972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Andersson, J. Nilsson, Marco Salvi, J. Spjut, T. Akenine-Möller
{"title":"Temporally dense ray tracing","authors":"P. Andersson, J. Nilsson, Marco Salvi, J. Spjut, T. Akenine-Möller","doi":"10.2312/hpg.20191193","DOIUrl":"https://doi.org/10.2312/hpg.20191193","url":null,"abstract":"We present a technique for real-time ray tracing with the goal of reaching 240frames per second or more. The core idea is to trade spatial resolution for faster temporal updates in such a way that the display and human visual system aid in integrating high-quality images. We use a combination of frameless and interleaved rendering concepts together with ideas from temporal antialiasing algorithms and novel building blocks---the major one being adaptive selection of pixel orderings within tiles, which reduces spatiotemporal aliasing significantly. The image quality is verified with a user study. Our work can be used for esports or any kind of rendering where higher frame rates are needed.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Usta, L. Scandolo, M. Billeter, R. Marroquim, E. Eisemann
{"title":"A practical and efficient approach for correct Z-Pass stencil shadow volumes","authors":"B. Usta, L. Scandolo, M. Billeter, R. Marroquim, E. Eisemann","doi":"10.2312/hpg.20191195","DOIUrl":"https://doi.org/10.2312/hpg.20191195","url":null,"abstract":"Shadow volumes are a popular technique to compute pixel-accurate hard shadows in 3D scenes. Many variants exist that trade off accuracy and efficiency. In this work, we present an artifact-free, efficient, and easy-to-implement stencil shadow volume method. We compare our method to established stencil shadow volume techniques and show that it outperforms the alternatives.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120980577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Patch textures: hardware implementation of mesh colors","authors":"Ian Mallett, Larry Seiler, Cem Yuksel","doi":"10.2312/hpg.20191194","DOIUrl":"https://doi.org/10.2312/hpg.20191194","url":null,"abstract":"Mesh colors provide an effective alternative to standard texture mapping. They significantly simplify the asset production pipeline by removing the need for defining a mapping and eliminate rendering artifacts due to seams. This paper addresses the problem that using mesh colors for real-time rendering has not been practical, due to the absence of hardware support. We show that it is possible to provide full hardware texture filtering support for mesh colors with minimal changes to existing GPUs by introducing a hardware-friendly representation for mesh colors that we call patch textures. We discuss the hardware modifications needed for storing and filtering patch textures.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127672146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Dynamic many-light sampling for real-time ray tracing","authors":"Pierre Moreau, M. Pharr, Petrik Clarberg","doi":"10.2312/hpg.20191191","DOIUrl":"https://doi.org/10.2312/hpg.20191191","url":null,"abstract":"Monte Carlo ray tracing offers the capability of rendering scenes with large numbers of area light sources---lights can be sampled stochastically and shadowing can be accounted for by tracing rays, rather than using shadow maps or other rasterization-based techniques that do not scale to many lights or work well with area lights. Current GPUs only afford the capability of tracing a few rays per pixel at real-time frame rates, making it necessary to focus sampling on important light sources. While state-of-the-art algorithms for offline rendering build hierarchical data structures over the light sources that enable sampling them according to their importance, they lack efficient support for dynamic scenes. We present a new algorithm for maintaining hierarchical light sampling data structures targeting real-time rendering. Our approach is based on a two-level BVH hierarchy that reduces the cost of partial hierarchy updates. Performance is further improved by updating lower-level BVHs via refitting, maintaining their original topology We show that this approach can give error within 6% of recreating the entire hierarchy from scratch at each frame, while being two orders of magnitude faster, requiring less than 1 ms per frame for hierarchy updates for a scene with thousands of moving light sources on a modern GPU. Further, we show that with spatiotemporal filtering, our approach allows complex scenes with thousands of lights to be rendered with ray-traced shadows in 16.1 ms per frame.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117230917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carsten Benthin, I. Wald, Sven Woop, Attila T. Áfra
{"title":"Compressed-leaf bounding volume hierarchies","authors":"Carsten Benthin, I. Wald, Sven Woop, Attila T. Áfra","doi":"10.1145/3231578.3231581","DOIUrl":"https://doi.org/10.1145/3231578.3231581","url":null,"abstract":"We propose and evaluate what we call Compressed-Leaf Bounding Volume Hierarchies (CLBVH), which strike a balance between compressed and non-compressed BVH layouts. Our CLBVH layout introduces dedicated compressed multi-leaf nodes where most effective at reducing memory use, and uses regular BVH nodes for inner nodes and small, isolated leaves. We show that when implemented within the Embree ray tracing framework, this approach achieves roughly the same memory savings as Embree's compressed BVH layout, while maintaining almost the full performance of its fastest non-compressed BVH.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114472415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Brook GLES Pi: democratising accelerator programming","authors":"Matina Maria Trompouki, Leonidas Kosmidis","doi":"10.1145/3231578.3231582","DOIUrl":"https://doi.org/10.1145/3231578.3231582","url":null,"abstract":"Nowadays computing is heavily-based on accelerators, however, the cost of the hardware equipment prevents equal access to heterogeneous programming. In this work we present Brook GLES Pi, a port of the accelerator programming language Brook. Our solution, primarily focused on the educational platform Raspberry Pi, allows to teach, experiment and take advantage of heterogeneous programming on any low-cost embedded device featuring an OpenGL ES 2 GPU, democratising access to accelerator programming.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131747987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Correlation-aware semi-analytic visibility for antialiased rendering","authors":"C. Crassin, Chris Wyman, M. McGuire, A. Lefohn","doi":"10.1145/3231578.3231584","DOIUrl":"https://doi.org/10.1145/3231578.3231584","url":null,"abstract":"Geometric aliasing is a persistent challenge for real-time rendering. Hardware multisampling remains limited to 8x, analytic coverage fails to capture correlated visibility samples, and spatial and temporal postfiltering primarily target edges of superpixel primitives. We describe a novel semi-analytic representation of coverage designed to make progress on geometric antialiasing for subpixel primitives and pixels containing many edges while handling correlated subpixel coverage. Although not yet fast enough to deploy, it crosses three critical thresholds: image quality comparable to 256x MSAA, faster than 64x MSAA, and constant space per pixel.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125532503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the Conference on High-Performance Graphics","authors":"","doi":"10.1145/3231578","DOIUrl":"https://doi.org/10.1145/3231578","url":null,"abstract":"","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126155142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detecting aliasing artifacts in image sequences using deep neural networks","authors":"Anjul Patney, A. Lefohn","doi":"10.1145/3231578.3231580","DOIUrl":"https://doi.org/10.1145/3231578.3231580","url":null,"abstract":"In this short paper we present a machine learning approach to detect visual artifacts in rendered image sequences. Specifically, we train a deep neural network using example aliased and antialiased image sequences exported from a real-time renderer. The trained network learns to identify and locate aliasing artifacts in an input sequence, without comparing it against a ground truth. Thus, it is useful as a fully automated tool for evaluating image quality. We demonstrate the effectiveness of our approach in detecting aliasing in several rendered sequences. The trained network correctly predicts aliasing in 64 x 64 x 4 animated sequences with more than 90% accuracy for images it hasn't seen before. The output of our network is a single scalar between 0 and 1, which is usable as a quality metric for aliasing. It follows the same trend as (1-SSIM) for images with increasing sample counts.","PeriodicalId":354787,"journal":{"name":"Proceedings of the Conference on High-Performance Graphics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114602730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}