By Gerassimos Barlas
Multicore and GPU Programming bargains vast insurance of the main 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 able to benefiting from today’s computing systems incorporating CPU and GPU and explains the right way to transition from sequential programming to a parallel computing paradigm.
Presenting fabric subtle over greater than a decade of training parallel computing, writer Gerassimos Barlas minimizes the problem with a number of examples, large case experiences, and whole resource code. utilizing this e-book, you could strengthen courses that run over allotted reminiscence machines utilizing MPI, create multi-threaded functions with both libraries or directives, write optimized functions that stability the workload among to be had computing assets, and profile and debug courses focusing on multicore machines.
- Comprehensive insurance of all significant multicore programming instruments, together with threads, OpenMP, MPI, and CUDA
- Demonstrates parallel programming layout styles and examples of the way various instruments and paradigms should be built-in for more desirable performance
- Particular concentrate on the rising region of divisible load thought and its influence on load balancing and allotted systems
- Download resource code, examples, and teacher help fabrics at the books significant other website
Read Online or Download Multicore and GPU Programming: An Integrated Approach PDF
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Additional info for Multicore and GPU Programming: An Integrated Approach
As an example of how PCAM can be applied, let’s consider the problem of parallelizing a low-level image-processing algorithm such as image convolution, which can be used for noise filtering, edge detection, or other applications, based on the kernel used. The kernel is a square matrix with weights that are used in the calculation of the new pixel data. 1. 1) i=−n2 j=−n2 where n2 = n 2 . 3) where v are original pixel values. 1 An illustation of how a 3x3 kernel is applied to the pixel values of an image to produce a desired effect.
5. A parallel application running on 5 identical machines, has a 10% sequential part. What is the speedup relative to a sequential execution on one of the machines? If we would like to double that speedup, how many CPU would be required? 6. An application with a 5% non-parallelizable part, is to be modified for parallel execution. Currently on the market there are two parallel machines available: machine X with 4 CPUs, each CPU capable of executing the application in 1 hour on its own, and, machine Y with 16 CPUs, with each CPU capable of executing the application in 2 hours on its own.
Each element (or group of elements) is then assigned to a separate compute node. The process may involve a modification of the original algorithm so that concurrent operations can take place. The way of operation mimicks dynamic programming, where bigger problems are solved based on the stored solutions of smaller problems. For example, let’s consider the problem of calculating the partial sums of an array: Given an input array A with N elements, calculate the partial sums Si = ij=0 Aj . 3. 3 Sequential pseudocode for the calculation of partial sums of an array.