http://www.bibbase.orgPublications Feed. Showing publications since 2011.Wed, 19 Jun 2013 11:17:22 GMTPyRSS2Gen-1.0.0http://blogs.law.harvard.edu/tech/rsshttp://www.bibbase.org/cache/data.bibbase.org_author_dana-schaa__3Fformat_3Dbibtex/Mistry2011c.html<span class='bibbase_paper_titleauthoryear'><span class='bibbase_paper_title' onclick="window.location.href='http://www.bibbase.org/cache/data.bibbase.org_author_dana-schaa__3Fformat_3Dbibtex/Mistry2011c.html'" style="cursor: pointer; cursor: hand;">Data Structures and Transformations for Physically Based Simulation on a GPU.</span> <span class='bibbase_paper_author'>Mistry, P.; Schaa, D.; Jang, B.; Kaeli, D.; Dvornik, A.; Meglan, D.; Palma, J. M.; Dayd&#233;, M.; Marques, O.; and ao Correia, J.</span> <span class='bibbase_paper_year'>2011.</span></span> <span class='bibbase_paper_venue'><span class='bibbase_paper_book'>VECPAR 2010</span>, Volume6449, 1--8.</span><br class='bibbase_paper_content'/><span class='bibbase_paper_content'><a href="http://www.springerlink.com/index/10.1007/978-3-642-19328-6" onclick="var cframe = document.getElementById('bibbase_controlframe'); cframe.contentWindow.location.href = 'http://www.bibbase.org/cgi-bin/writelog.cgi?servername=www.bibbase.org&amp;followlink=www.springerlink.com/index/10.1007/978-3-642-19328-6';"><img src="http://www.bibbase.org/resources/filetypes/html.png" alt="Data Structures and Transformations for Physically Based Simulation on a GPU [http://www.springerlink.com/index/10.1007/978-3-642-19328-6]" style="width: 24px; height: 24px; border: 0px; vertical-align: text-top" class='bibbase_icon'/><span style="display: none;" class='bibbase_icon_text'>Data Structures and Transformations for Physically Based Simulation on a GPU</span></a> <a href="http://www.bibbase.org/cache/data.bibbase.org_author_dana-schaa__3Fformat_3Dbibtex/Mistry2011c.bib"><img src="http://www.bibbase.org/resources/filetypes/bib.png" alt="Data Structures and Transformations for Physically Based Simulation on a GPU [bib]" style="width: 24px; height: 24px; border: 0px; vertical-align: text-top" class='bibbase_icon' /><span style="display: none;" class='bibbase_icon_text'>Bibtex</span></a> &nbsp; <span class='bibbase_stats_paper' style='color: #777;'><span id='span_stats_paper_http___www_springerlink_com_index_10_1007_978_3_642_19328_6'></span></span> &nbsp;<span class="bibbase_amazon" style="color: #404040; font-family: monospace; font-weight: bold; padding: 3px; margin: 3px"><a type="amzn" search="VECPAR 2010" category="books">Buy</a></span><span class='bibbase_group' onclick='toggleFold("abstract_Mistry2011c")' title='fold/unfold' style="cursor: pointer; cursor: hand;">&nbsp;<img id="icon_abstract_Mistry2011c" src="http://www.bibbase.org/resources/folded.gif" border='0' alt="abstract_Mistry2011cfolded.gif" />&nbsp;<i>Abstract:</i></span><div id="abstract_Mistry2011c" name="foldable" style="display:none; " class="bibbase_group_body"><blockquote>As general purpose computing on Graphics Processing Units (GPGPU) matures, more complicated scientific applications are being targeted to utilize the data-level parallelism available on a GPU. Implementing physically-based sim- ulation on data-parallel hardware requires preprocessing overhead which affects application performance. We discuss our implementation of physics-based data structures that provide significant performance improvements when used on data- parallel hardware. These data structures allow us to maintain a physics-based abstraction of the underlying data, reduce programmer effort and obtain 6x-8x speedup over previously implemented GPU kernels.</blockquote></div></span>http://www.bibbase.org/cache/data.bibbase.org_author_dana-schaa__3Fformat_3Dbibtex/schaa2011.html<span class='bibbase_paper_titleauthoryear'><span class='bibbase_paper_title' onclick="window.location.href='http://www.bibbase.org/cache/data.bibbase.org_author_dana-schaa__3Fformat_3Dbibtex/schaa2011.html'" style="cursor: pointer; cursor: hand;">GPU Acceleration of Iterative Digital Breast Tomosynthesis.</span> <span class='bibbase_paper_author'>Schaa, D.; Brown, B.; Jang, B.; Mistry, P.; Dominguez, R.; Kaeli, D.; Moore, R.; and Kopans, D.</span> <span class='bibbase_paper_year'>2011.</span></span> <span class='bibbase_paper_venue'><span class='bibbase_paper_book'>GPU Computing Gems</span>.</span><br class='bibbase_paper_content'/><span class='bibbase_paper_content'><a href="http://dx.doi.org/10.1016/B978-0-12-384988-5.00040-1 http://www.sciencedirect.com/science/book/9780123849885" onclick="var cframe = document.getElementById('bibbase_controlframe'); cframe.contentWindow.location.href = 'http://www.bibbase.org/cgi-bin/writelog.cgi?servername=www.bibbase.org&amp;followlink=dx.doi.org/10.1016/B978-0-12-384988-5.00040-1 www.sciencedirect.com/science/book/9780123849885';"><img src="http://www.bibbase.org/resources/filetypes/html.png" alt="GPU Acceleration of Iterative Digital Breast Tomosynthesis [http://dx.doi.org/10.1016/B978-0-12-384988-5.00040-1 http://www.sciencedirect.com/science/book/9780123849885]" style="width: 24px; height: 24px; border: 0px; vertical-align: text-top" class='bibbase_icon'/><span style="display: none;" class='bibbase_icon_text'>GPU Acceleration of Iterative Digital Breast Tomosynthesis</span></a> <a href="http://www.bibbase.org/cache/data.bibbase.org_author_dana-schaa__3Fformat_3Dbibtex/schaa2011.bib"><img src="http://www.bibbase.org/resources/filetypes/bib.png" alt="GPU Acceleration of Iterative Digital Breast Tomosynthesis [bib]" style="width: 24px; height: 24px; border: 0px; vertical-align: text-top" class='bibbase_icon' /><span style="display: none;" class='bibbase_icon_text'>Bibtex</span></a> &nbsp; <span class='bibbase_stats_paper' style='color: #777;'><span id='span_stats_paper_http___dx_doi_org_10_1016_B978_0_12_384988_5_00040_1_http___www_sciencedirect_com_science_book_9780123849885'></span></span> &nbsp;<span class="bibbase_amazon" style="color: #404040; font-family: monospace; font-weight: bold; padding: 3px; margin: 3px"><a type="amzn" search="GPU Computing Gems" category="books">Buy</a></span><span class='bibbase_group' onclick='toggleFold("abstract_schaa2011")' title='fold/unfold' style="cursor: pointer; cursor: hand;">&nbsp;<img id="icon_abstract_schaa2011" src="http://www.bibbase.org/resources/folded.gif" border='0' alt="abstract_schaa2011folded.gif" />&nbsp;<i>Abstract:</i></span><div id="abstract_schaa2011" name="foldable" style="display:none; " class="bibbase_group_body"><blockquote>Iterative digital breast tomosynthesis (DBT) is a technology that mitigates many of the shortcomings associated with traditional mammography. Using multiple low-dose X-ray projections with an itera- tive maximum likelihood estimation method, DBT is able to create a high-quality, three-dimensional reconstruction of the breast. However, the usability of DBT depends largely on making the time for computation acceptable within a clinical setting. In this work we accelerate our DBT algorithm on multiple CUDA-enabled GPUs, reducing the execution time to under 20 seconds for eight iterations (the number usually required to obtain an acceptable quality reconstructed image). The algorithm studied in this work is representative of a general class of image reconstruction problems, as are the thread-mapping strategies, multi-GPU considerations, and optimizations employed in this work.</blockquote></div></span>