Written by high performance computing (HPC) experts, Introduction to
High Performance Computing for Scientists and Engineers provides a solid
introduction to current mainstream computer architecture, dominant parallel
programming models, and useful optimization strategies for scientific HPC.
From working in a scientific computing center, the authors gained a unique
perspective on the requirements and attitudes of users as well as
manufacturers of parallel computers.

The text first introduces the architecture of modern cache-based
microprocessors and discusses their inherent performance limitations,
before describing general optimization strategies for serial code on cache-
based architectures. It next covers shared- and distributed-memory parallel
computer architectures and the most relevant network topologies. After
discussing parallel computing on a theoretical level, the authors show how
to avoid or ameliorate typical performance problems connected with OpenMP.
They then present cache-coherent nonuniform memory access (ccNUMA)
optimization techniques, examine distributed-memory parallel programming
with message passing interface (MPI), and explain how to write efficient
MPI code. The final chapter focuses on hybrid programming with MPI and
OpenMP.

Users of high performance computers often have no idea what factors limit
time to solution and whether it makes sense to think about optimization at
all. This book facilitates an intuitive understanding of performance
limitations without relying on heavy computer science knowledge. It also
prepares readers for studying more advanced literature.

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