By Gerassimos Barlas
ISBN-10: 0124171370
ISBN-13: 9780124171374
Multicore and GPU Programming bargains extensive assurance of the most important parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. utilizing threads, OpenMP, MPI, and CUDA, it teaches the layout and improvement of software program in a position to profiting from today’s computing structures incorporating CPU and GPU and explains easy methods to transition from sequential programming to a parallel computing paradigm.
Presenting fabric subtle over greater than a decade of educating parallel computing, writer Gerassimos Barlas minimizes the problem with a number of examples, wide case stories, and entire resource code. utilizing this publication, you could advance courses that run over dispensed reminiscence machines utilizing MPI, create multi-threaded purposes with both libraries or directives, write optimized purposes that stability the workload among on hand computing assets, and profile and debug courses concentrating on multicore machines.
- Comprehensive assurance of all significant multicore programming instruments, together with threads, OpenMP, MPI, and CUDA
- Demonstrates parallel programming layout styles and examples of ways varied instruments and paradigms may be built-in for enhanced performance
- Particular specialise in the rising sector of divisible load thought and its effect on load balancing and dispensed systems
- Download resource code, examples, and teacher help fabrics at the books significant other website
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Extra info for Multicore and GPU Programming: An Integrated Approach
Sample text
One way to eliminate it is to group together tasks. Each group will be ultimately assigned to a single computational node, which means communication within the group is eliminated. The number of groups produced at this stage should be, as a rule of thumb, one order of magnitude bigger than the number of compute nodes available. 4. Mapping: For the application to execute, the task groups produced by the third step must be assigned/mapped to the available nodes. , they should all have more or less the same amount of work to do as measured by execution time, and (b) reduce communication overhead even further by mapping groups with expensive data exchange between them, to the same nodes: Communication over shared-memory is virtually free.
13 Speedup curves for different values of α as predicted by Gustafson-Barsis’ law. 14 Efficiency curves for different values of α as predicted by Gustafson-Barsis’ law. 14 the picture remains a rosy one. Even for α = 50%, efficiency does not drop below 50% for up to 16 CPUs. This is just too good to be true. Even for the so-called embarrassingly parallel problems, communication overheads become a defining factor when N increases, diminishing speedup gains and plummeting efficiency. In general, obtaining efficiency above 90% in practice is considered a worthwhile achievement.
A typical example of such a scenario is a discrete-event simulation of a system. 4 Program structure patterns by objects or software modules that interact by generating events. An event is a time-stamped message that can represent a status change in the state of a module, a trigger to change the state, a request to perform an action, a response to a previously generated request, or the like. 4 PROGRAM STRUCTURE PATTERNS Patterns can assist not only in the selection of an appropriate workload decomposition approach but also in the program development.
Multicore and GPU Programming: An Integrated Approach by Gerassimos Barlas
by Ronald
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