Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today’s computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm.
Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge with multiple examples, extensive case studies, and full source code. Using this book, you can develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting multicore machines.
- Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDA
- Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance
- Particular focus on the emerging area of divisible load theory and its impact on load balancing and distributed systems
- Download source code, examples, and instructor support materials on the book's companion website
Parallel computing has been given a fresh breath of life since the emergence of multicore architectures in the first decade of the new century. The new platforms demand a new approach to software development; one that blends the tools and established practices of the Network-of-Workstations (NoWs) era with emerging software platforms such as CUDA.
This book tries to address this need by covering the dominant contemporary tools and techniques, both in isolation but also most importantly, in combination with each other. We strive to provide examples where multiple platforms and programming paradigms (e.g. message passing and threads) are effectively combined. ``Hybrid'' computation, as it is usually called, is a new trend in high performance computing, one that could possibly allow software to scale to the ``millions of threads'' required for exascale performance. In that regard, this book is unique in the available literature.
All chapters are accompanied by extensive examples and practice problems with an emphasis on putting them to work, while comparing alternative design scenarios. All the little details that can make the difference between a productive software development and a stressed exercise in futility, are presented in a orderly fashion.
The book covers the latest advances in tools that have been inherited from the 1990s (e.g. the OpenMP and MPI standards), but also more cutting-edge platforms, such as the Qt library with its sophisticated thread management, and the Thrust template library, with its capability to deploy the same software over diverse multicore architectures, including both CPUs and Graphical Processing Units (GPUs).
