Download E-books Patterns for Parallel Programming PDF
By Timothy G. Mattson
The Parallel Programming advisor for Every software program Developer
From grids and clusters to next-generation online game consoles, parallel computing goes mainstream. options comparable to Hyper-Threading expertise, HyperTransport know-how, and multicore microprocessors from IBM, Intel, and solar are accelerating the movement's progress. just one factor is lacking: programmers with the talents to fulfill the hovering call for for parallel software program.
That's the place Patterns for Parallel Programming is available in. it is the first parallel programming consultant written in particular to serve operating software program builders, not only laptop scientists. The authors introduce a whole, hugely obtainable trend language that might aid any skilled developer "think parallel"-and begin writing potent parallel code shortly. rather than formal idea, they carry confirmed options to the demanding situations confronted by way of parallel programmers, and pragmatic suggestions for utilizing modern day parallel APIs within the genuine global. insurance contains:
- Understanding the parallel computing panorama and the demanding situations confronted by way of parallel developers
- Finding the concurrency in a software program layout challenge and decomposing it into concurrent tasks
- Managing using facts throughout tasks
- Creating an set of rules constitution that successfully exploits the concurrency you will have identified
- Connecting your algorithmic constructions to the APIs had to enforce them
- Specific software program constructs for imposing parallel programs
- Working with latest top parallel programming environments: OpenMP, MPI, and Java
Patterns have helped millions of programmers grasp object-oriented improvement and different advanced programming applied sciences. With this e-book, you are going to research that they are tips on how to grasp parallel programming too.
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Extra resources for Patterns for Parallel Programming
We must also ensure that the data required for the update of each chunk is present when needed. This problem is somewhat analogous to the problem of managing data dependencies in the Task Parallelism pattern, and again the design must keep in mind the sometimes conflicting goals of simplicity, portability, scalability, and efficiency. answer Designs for problems that fit this pattern involve the following key elements: partitioning the global data structure into substructures or "chunks" (the data decomposition), ensuring that each task has access to all the data it needs to perform the update operation for its chunk (the exchange operation), updating the chunks (the update operation), and mapping chunks to UEs in a way that gives good performance (the data distribution and task schedule). information decomposition The granularity of the data decomposition has a significant impact on the efficiency of the program. In a coarsegrained decomposition, there are a smaller number of large chunks. This results in a smaller number of large messages, which can greatly reduce communication overhead. A finegrained decomposition, on the other hand, results in a larger number of smaller chunks, in many cases leading to many more chunks than PEs. This results in a larger number of smaller messages (and hence increases communication overhead), but it greatly facilitates load balancing. Although it might be possible in some cases to mathematically derive an optimum granularity for the data decomposition, programmers usually experiment with a range of chunk sizes to empirically determine the best size for a given system. This depends, of course, on the computational performance of the PEs and on the performance characteristics of the communication network. Therefore, the program should be implemented so that the granularity is controlled by parameters that can be easily changed at compile or runtime. The shape of the chunks can also affect the amount of communication needed between tasks. Often, the data to share between tasks is limited to the boundaries of the chunks. In this case, the amount of shared information scales with the surface area of the chunks. Because the computation scales with the number of points within a chunk, it scales as the volume of the region. This surfacetovolume effect can be exploited to maximize the ratio of computation to communication. Therefore, higher dimensional decompositions are usually preferred. For example, consider two different decompositions of an N by N matrix into four chunks. In one case, we decompose the problem into four column chunks of size N by N/4. In the second case, we decompose the problem into four square chunks of size N/2 by N/2. For the column block decomposition, the surface area is 2N + 2(N/4) or 5N/2. For the square chunk case, the surface area is 4(N/2) or 2N. Hence, the total amount of data that must be exchanged is less for the square chunk decomposition. In some cases, the preferred shape of the decomposition can be dictated by other concerns.