Ronald W. Shonkwiler is a Professor in the School of Mathematics at the Georgia Institute of Technology. He has authored or co-authored over 50 research papers in areas of functional analysis, mathematical biology, image processing algorithms, fractal geometry, neural networks and Monte Carlo optimization methods. His algorithm for monochrome image comparison is part of a US patent for fractal image compression. He has co-authored two other books, An Introduction to the Mathematics of Biology and The Handbook of Stochastic Analysis and Applications. Lew Lefton is the Director of Information Technology at the Georgia Institute of Technology where he has built and maintained several computing clusters which are used to implement parallel computations. Prior to that he was a tenured faculty member in the Department of Mathematics at the University of New Orleans. His academic interests are in differential equations, applied mathematics, numerical analysis (in particular, finite element methods) and scientific computing.

'… in spite of the clear need to present these concepts to a much broader technical audience, there is a perplexing dearth of training material and textbooks in the field, particularly at the introductory level. … it is indeed refreshing to see the publication of the book An Introduction to Parallel and Vector Scientific Computing, written by Ronald W. Shonkwiler and Lew Lefton, both of the Georgia institute of Technology. They have taken the bull by the horns and produced a book that appears to be entirely satisfactory as an introductory textbook for use in such a course.' Scientific Programming

• Part I. Machines and Computation: 1. Introduction - the nature of high performance computation; 2. Theoretical considerations - complexity; 3. Machine implementations; Part II. Linear Systems: 4. Building blocks - floating point numbers and basic linear algebra; 5. Direct methods for linear systems and LU decomposition; 6. Direct methods for systems with special structure; 7. Error analysis and QR decomposition; 8. Iterative methods for linear systems; 9. Finding eigenvalues and eigenvectors; Part III. Monte Carlo Methods: 10. Monte Carlo simulation; 11. Monte Carlo optimization; Appendix: programming examples.

Ronald W. Shonkwiler is a Professor in the School of Mathematics at the Georgia Institute of Technology. He has authored or co-authored over 50 research papers in areas of functional analysis, mathematical biology, image processing algorithms, fractal geometry, neural networks and Monte Carlo optimization methods. His algorithm for monochrome image comparison is part of a US patent for fractal image compression. He has co-authored two other books, An Introduction to the Mathematics of Biology and The Handbook of Stochastic Analysis and Applications. Lew Lefton is the Director of Information Technology at the Georgia Institute of Technology where he has built and maintained several computing clusters which are used to implement parallel computations. Prior to that he was a tenured faculty member in the Department of Mathematics at the University of New Orleans. His academic interests are in differential equations, applied mathematics, numerical analysis (in particular, finite element methods) and scientific computing.

'… in spite of the clear need to present these concepts to a much broader technical audience, there is a perplexing dearth of training material and textbooks in the field, particularly at the introductory level. … it is indeed refreshing to see the publication of the book An Introduction to Parallel and Vector Scientific Computing, written by Ronald W. Shonkwiler and Lew Lefton, both of the Georgia institute of Technology. They have taken the bull by the horns and produced a book that appears to be entirely satisfactory as an introductory textbook for use in such a course.' Scientific Programming

• Part I. Machines and Computation: 1. Introduction - the nature of high performance computation; 2. Theoretical considerations - complexity; 3. Machine implementations; Part II. Linear Systems: 4. Building blocks - floating point numbers and basic linear algebra; 5. Direct methods for linear systems and LU decomposition; 6. Direct methods for systems with special structure; 7. Error analysis and QR decomposition; 8. Iterative methods for linear systems; 9. Finding eigenvalues and eigenvectors; Part III. Monte Carlo Methods: 10. Monte Carlo simulation; 11. Monte Carlo optimization; Appendix: programming examples.