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
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
2 Multicore and Parallel Program Design
3 Shared-memory programming: Threads
4 Shared-memory programming: OpenMP
5 Distributed memory programming
6 GPU Programming
7 The Thrust Template Library
8 Load Balancing
A Compiling Qt programs
B RunningMPI Programs: Preparatory and Configuration Steps
C Time Measurement
E Setting up CUDA
Gerassimos Barlas is a Professor with the Computer Science & Engineering Department, American University of Sharjah, Sharjah, UAE. His research interest includes parallel algorithms, development, analysis and modeling frameworks for load balancing, and distributed Video on-Demand. Prof. Barlas has taught parallel computing for more than 12 years, has been involved with parallel computing since the early 90s, and is active in the emerging field of Divisible Load Theory for parallel and distributed systems.