{"title":"GPU集群上不同稀疏矩阵格式和互连的自调Krylov基计算","authors":"Langshi Chen, Serge G. Petition","doi":"10.1109/CLUSTER.2015.153","DOIUrl":null,"url":null,"abstract":"Krylov subspace methods (KSMs) are widely used in solving large-scale sparse linear problems. The orthogonalization process in methods like GMRES would consume a majority of the time. Since modern manycore architecture based accelerators have provided great horsepowers for computations,communication overheads remain a bottleneck, especially in clusters with a great number of nodes. The HA-PACS/TCA of Tsukuba University is a CPU-GPU hybrid cluster equipped with different interconnects for communications among GPUs. We testa group of Krylov basis computation methods with different sparse matrices and interconnects on HA-PACS/TCA. Results show that an auto-tuning scheme is required to deal with various types of matrices.","PeriodicalId":187042,"journal":{"name":"2015 IEEE International Conference on Cluster Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward Auto-tuned Krylov Basis Computation for Different Sparse Matrix Formats and Interconnects on GPU Clusters\",\"authors\":\"Langshi Chen, Serge G. Petition\",\"doi\":\"10.1109/CLUSTER.2015.153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Krylov subspace methods (KSMs) are widely used in solving large-scale sparse linear problems. The orthogonalization process in methods like GMRES would consume a majority of the time. Since modern manycore architecture based accelerators have provided great horsepowers for computations,communication overheads remain a bottleneck, especially in clusters with a great number of nodes. The HA-PACS/TCA of Tsukuba University is a CPU-GPU hybrid cluster equipped with different interconnects for communications among GPUs. We testa group of Krylov basis computation methods with different sparse matrices and interconnects on HA-PACS/TCA. Results show that an auto-tuning scheme is required to deal with various types of matrices.\",\"PeriodicalId\":187042,\"journal\":{\"name\":\"2015 IEEE International Conference on Cluster Computing\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Cluster Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CLUSTER.2015.153\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Cluster Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CLUSTER.2015.153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Toward Auto-tuned Krylov Basis Computation for Different Sparse Matrix Formats and Interconnects on GPU Clusters
Krylov subspace methods (KSMs) are widely used in solving large-scale sparse linear problems. The orthogonalization process in methods like GMRES would consume a majority of the time. Since modern manycore architecture based accelerators have provided great horsepowers for computations,communication overheads remain a bottleneck, especially in clusters with a great number of nodes. The HA-PACS/TCA of Tsukuba University is a CPU-GPU hybrid cluster equipped with different interconnects for communications among GPUs. We testa group of Krylov basis computation methods with different sparse matrices and interconnects on HA-PACS/TCA. Results show that an auto-tuning scheme is required to deal with various types of matrices.