Introduction To Parallel Computing
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A practical student guide to scientific computing on parallel computers, based on the authors' lectures at ETH Zurich. It includes linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and, in some cases, Fortran.In the last few years, courses on parallel computation have been developed and offered in many institutions in the UK, Europe and US as a recognition of the growing significance of this topic in mathematics and computer science. There is a clear need for texts that meet the needs of students and lecturers and this book, based on the author's lect...
A practical student guide to scientific computing on parallel computers, based on the authors' lectures at ETH Zurich. It includes linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and, in some cases, Fortran.
In the last few years, courses on parallel computation have been developed and offered in many institutions in the UK, Europe and US as a recognition of the growing significance of this topic in mathematics and computer science. There is a clear need for texts that meet the needs of students and lecturers and this book, based on the author's lecture at ETH Zurich, is an ideal practical student guide to scientific computing on parallel computers working up from a hardware instruction level, to shared memory machines, and finally to distributed memory machines. Aimed at advanced undergraduate and graduate students in applied mathematics, computer science, and engineering, subjects covered include linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and, in some cases, Fortran. This book is also ideal for practitioners and programmers.
1. Basic issues; 2. Applications; 3. SIMD, Single Instruction Multiple Data; 4. Shared Memory Parallelism; 5. MIMD, Multiple Instruction Multiple Data; A. SSE Intrinsics for Floating Point; B. AltiVec Intrinsics for Floating Point; C. OpenMP commands; D. Summary of MPI commands; E. Fortran and C communication; F. Glossary of terms; G. Notation and symbols
In the last few years, courses on parallel computation have been developed and offered in many institutions in the UK, Europe and US as a recognition of the growing significance of this topic in mathematics and computer science. There is a clear need for texts that meet the needs of students and lecturers and this book, based on the author's lecture at ETH Zurich, is an ideal practical student guide to scientific computing on parallel computers working up from a hardware instruction level, to shared memory machines, and finally to distributed memory machines. Aimed at advanced undergraduate and graduate students in applied mathematics, computer science, and engineering, subjects covered include linear algebra, fast Fourier transform, and Monte-Carlo simulations, including examples in C and, in some cases, Fortran. This book is also ideal for practitioners and programmers.
1. Basic issues; 2. Applications; 3. SIMD, Single Instruction Multiple Data; 4. Shared Memory Parallelism; 5. MIMD, Multiple Instruction Multiple Data; A. SSE Intrinsics for Floating Point; B. AltiVec Intrinsics for Floating Point; C. OpenMP commands; D. Summary of MPI commands; E. Fortran and C communication; F. Glossary of terms; G. Notation and symbols