{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,20]],"date-time":"2025-02-20T05:16:15Z","timestamp":1740028575405,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>The graphics processing unit (GPU) is an excellent accelerator and it can realize speedup with appropriate tuning. In this paper, we present a tuning technique for the exact diagonalization method, which is widely used as a numerical tool to obtain the ground state (the smallest eigenvalue and the corresponding eigenvector) of the Hamiltonian derived from the Hubbard model, on the GPU architecture. Since the Hamiltonian is a sparse matrix, an iteration method is used for solving the eigenvalue problems. We mainly tune the code for the multiplication of the Hamiltonian and a vector, which is the most time-consuming operation in the iteration method. The numerical test shows that the tuned code is faster than the one with using the routine &amp;ldquo;cusparseDcsrmm&amp;rdquo; of cuSPARSE library. Moreover, the tuned method on NVIDIA Tesla M2075 achieves about 3&amp;times; speedup as compared with the thread-parallelized code on six threads of Intel Xeon 5650 for the multiplication.<\/jats:p>","DOI":"10.3233\/978-1-61499-621-7-361","type":"book-chapter","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:30:51Z","timestamp":1739979051000},"source":"Crossref","is-referenced-by-count":0,"title":["High Performance Eigenvalue Solver in Exact-diagonalization Method for Hubbard Model on CUDA GPU"],"prefix":"10.3233","author":[{"family":"Yamada Susumu","sequence":"additional","affiliation":[]},{"family":"Imamura Toshiyuki","sequence":"additional","affiliation":[]},{"family":"Machida Masahiko","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Advances in Parallel Computing","Parallel Computing: On the Road to Exascale"],"original-title":[],"deposited":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T15:33:08Z","timestamp":1739979188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-620-0&spage=361&doi=10.3233\/978-1-61499-621-7-361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-621-7-361","relation":{},"ISSN":["0927-5452"],"issn-type":[{"value":"0927-5452","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}