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Abstract
Computer modeling of physical phenomena plays an increasingly important role in science and engineering. Examples of such modeling include accelerator design, simulation of plasma fusion reactor, and nanostructure calculations. In such computations, a large fraction of the time is usually spent in the solution of sparse linear algebra problems, such as linear systems or eigenvalue problems. In this talk, we will show our experiences in developing scalable parallel sparse matrix algorithms and software that take advantage of the state-of-the-art parallel computer architectures. Examples of such algorithms include LU factorization, Conjugate Gradient and multigrid.
Bio Xiaoye Li is a Senior Scientist in Lawrence Berkeley National Laboratory, USA. She has worked on diverse problems in high performance scientific computations, including parallel computing, sparse matrix computations, high precision arithmetic, and combinatorial scientific computing. She has (co)authored over 90 publications, and contributed to several book chapters. She is the lead developer of SuperLU, a widely-used sparse direct solver, and has contributed to the development of several other mathematical libraries, including ARPREC, LAPACK, PDSLin, STRUMPACK, and XBLAS. She has collaborated with many domain scientists to deploy the advanced mathematical software in their application codes. She earned B.S. in Computer Science from Tsinghua University in 1986, and Ph.D. in Computer Science from UC Berkeley in 1996. She is a Senior Member of ACM and a SIAM Fellow.
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