By James Reinders
ISBN-10: 0128021187
ISBN-13: 9780128021187
High functionality Parallelism Pearls indicates find out how to leverage parallelism on processors and coprocessors with an identical programming – illustrating the simplest how one can greater faucet the computational capability of structures with Intel Xeon Phi coprocessors and Intel Xeon processors or different multicore processors. The ebook comprises examples of profitable programming efforts, drawn from throughout industries and domain names reminiscent of chemistry, engineering, and environmental technological know-how. every one bankruptcy during this edited paintings contains exact factors of the programming ideas used, whereas exhibiting excessive functionality effects on either Intel Xeon Phi coprocessors and multicore processors. examine from dozens of recent examples and case experiences illustrating "success tales" demonstrating not only the positive aspects of those strong structures, but additionally tips to leverage parallelism throughout those heterogeneous platforms.
- Promotes constant standards-based programming, displaying intimately how you can code for top functionality on multicore processors and Intel® Xeon Phi™
- Examples from a number of vertical domain names illustrating parallel optimizations to modernize real-world codes
- Source code to be had for obtain to facilitate additional exploration
Read Online or Download High Performance Parallelism Pearls Volume One: Multicore and Many-core Programming Approaches PDF
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Additional resources for High Performance Parallelism Pearls Volume One: Multicore and Many-core Programming Approaches
Example text
Chapter 26 juggles data, computation, and storage to increase performance. Chapter 12 increases performance by ensuring parallelism in a heterogeneous node. Enhancing parallelism across a heterogeneous cluster is illustrated in Chapter 13 and Chapter 25. MODERNIZE WITH VECTORIZATION AND DATA LOCALITY Chapter 8 provides a solid examination of data layout issues in the quest to process data as vectors. Chapters 27 and 28 provide additional education and motivation for doing data layout and vectorization work.
Fortunately, this is fairly simple: Hydro2D’s performance is largely independent of the specific initial and boundary conditions specified, so we are free to choose any test problem. The NewtonRaphson iterations performed in the Riemann solver have control flow that may increase runtime for a flux computation depending on the input, but this is only significant for pathological cases. To capture the sensitivity of the code to problem sizes, we will explore a variety of problem sizes and generally normalize our results to the time taken to process a single cell in a single timestep.
When the integration is complete, the results are written back to the solution grid and another subregion is copied to the slab, an so on, until the whole solution grid has been updated. 6 for a diagram of this procedure. It is worth noting that the slab used in the reference code is used for both the x- and y-dimensional updates and that updates are always done in complete x “rows” and y “columns” to handle boundaries properly without further copies. Data copied to/from the slab from/to the global grid is transposed for the y-pass, and therefore the slab always must be wide enough to accommodate the larger of the x- and y-dimensions of the grid (the other dimension of the slab is a user-selectable parameter).
High Performance Parallelism Pearls Volume One: Multicore and Many-core Programming Approaches by James Reinders
by Richard
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