2007.94: Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy
2007.94: Alfredo Buttari, Jack Dongarra, Jakub Kurzak, Piotr Luszczek and Stanimire Tomov (2007) Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy.
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By using a combination of 32-bit and 64-bit floating point arithmetic the performance of many sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. These ideas can be applied to sparse multifrontal and supernodal direct techniques, and sparse iterative techniques such as Krylov subspace methods. The approach presented here can apply not only to conventional processors but also to exotic technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the Cell BE processor.
|Item Type:||MIMS Preprint|
Appears also as Technical Report UT-CS-06-584, Department of Computer Science, University of Tennessee, Knoxville, TN, USA, November 2006 and LAPACK Working Note 180
|Subjects:||MSC 2000 > 65 Numerical analysis|
MSC 2000 > 68 Computer science
|Deposited By:||Miss Louise Stait|
|Deposited On:||03 July 2007|