Doallo, RamónFraguela, Basilio B.Touriño, JuanZapata, Emilio L.2019-03-262019-03-261996Doallo R., Fraguela B.B., Touriño J., Zapata E.L. (1996) Parallel sparse modified Gram-Schmidt QR decomposition. In: Liddell H., Colbrook A., Hertzberger B., Sloot P. (eds) High-Performance Computing and Networking. HPCN-Europe 1996. Lecture Notes in Computer Science, vol 1067. Springer, Berlin, Heidelberg978-3-540-61142-4978-3-540-49955-80302-97431611-3349http://hdl.handle.net/2183/22369This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/3-540-61142-8_609[Abstract] We present a parallel computational method for the QR decomposition with column pivoting of a sparse matrix by means of Modified Gram-Schmidt orthogonalization. Nonzero elements of the matrix M to be decomposed are stored in a one-dimensional doubly linked list data structure. We discuse a strategy to reduce fill-in in order to get memory savings and decrease the computation times. As an application of QR decomposition, we describe the least squares problem. This algorithm was designed for a message passing multiprocessor and has been evaluated on a Cray T3D, using the Harwell-Boeing sparse matrix collection.engNonzero elementSequential algorithmSparse matriceLittle square problemPivot elementconference outputopen access10.1007/3-540-61142-8_609