Jeremias M Gomes, George Teodoro, Alba de Melo, Jun Kong, Tahsin Kurc, Joel H Saltz
{"title":"Intel®Xeon Phi™上高效的不规则波前传播算法。","authors":"Jeremias M Gomes, George Teodoro, Alba de Melo, Jun Kong, Tahsin Kurc, Joel H Saltz","doi":"10.1109/SBAC-PAD.2015.13","DOIUrl":null,"url":null,"abstract":"<p><p>We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel<sup>®</sup> Xeon Phi<sup>™</sup> co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63<i>×</i> on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7<i>×</i> and 1.62<i>×</i>, respectively, as compared to efficient CPU and GPU implementations.</p>","PeriodicalId":91389,"journal":{"name":"Proceedings. Symposium on Computer Architecture and High Performance Computing","volume":"2015 ","pages":"25-32"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/SBAC-PAD.2015.13","citationCount":"10","resultStr":"{\"title\":\"Efficient irregular wavefront propagation algorithms on Intel<sup>®</sup> Xeon Phi<sup>™</sup>.\",\"authors\":\"Jeremias M Gomes, George Teodoro, Alba de Melo, Jun Kong, Tahsin Kurc, Joel H Saltz\",\"doi\":\"10.1109/SBAC-PAD.2015.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel<sup>®</sup> Xeon Phi<sup>™</sup> co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63<i>×</i> on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7<i>×</i> and 1.62<i>×</i>, respectively, as compared to efficient CPU and GPU implementations.</p>\",\"PeriodicalId\":91389,\"journal\":{\"name\":\"Proceedings. Symposium on Computer Architecture and High Performance Computing\",\"volume\":\"2015 \",\"pages\":\"25-32\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/SBAC-PAD.2015.13\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Symposium on Computer Architecture and High Performance Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBAC-PAD.2015.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Symposium on Computer Architecture and High Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBAC-PAD.2015.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient irregular wavefront propagation algorithms on Intel® Xeon Phi™.
We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel® Xeon Phi™ co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63× on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7× and 1.62×, respectively, as compared to efficient CPU and GPU implementations.