By Jim Jeffers, James Reinders
High functionality Parallelism Pearls quantity 2 deals one other set of examples that exhibit how one can leverage parallelism. just like quantity 1, the innovations incorporated right here clarify how one can use processors and coprocessors with an identical programming – illustrating the best how one can mix Xeon Phi coprocessors with Xeon and different multicore processors. The publication contains examples of winning programming efforts, drawn from throughout industries and domain names akin to biomed, genetics, finance, production, imaging, and extra. every one bankruptcy during this edited paintings contains particular factors of the programming options used, whereas exhibiting excessive functionality effects on either Intel Xeon Phi coprocessors and multicore processors. research from dozens of recent examples and case reviews illustrating "success tales" demonstrating not only the gains of Xeon-powered platforms, but additionally the best way to leverage parallelism throughout those heterogeneous systems.
- Promotes write-once, run-anywhere coding, displaying the way to code for top functionality on multicore processors and Xeon Phi
- Examples from a number of vertical domain names illustrating real-world use of Xeon Phi coprocessors
- Source code on hand for obtain to facilitate extra exploration
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Extra resources for High Performance Parallelism Pearls Volume Two: Multicore and Many-core Programming Approaches
In contrast to the NIM dynamics, WSM6 computations are performed in double-precision and, for the majority of computations, arrays are stored with the “i” dimension innermost. Due to the differences in storage order and precision, transposition between dynamics arrays and physics arrays is required. 10 CHAPTER 2 NUMERICAL WEATHER PREDICTION OPTIMIZATION Fortunately, dynamics and physics share a relatively small set of arrays so transposition costs are small (total costs for all physics packages are less than 3% of total run time).
Compile-time constants allow the compiler to generate aligned vector instructions that reduce instruction latency. This speeds up compute-intensive code more than memory-intensive code. In addition, the use of “unaligned” instructions on “aligned” memory incurs no performance penalty on Intel Xeon processors, but significantly slows the Intel Xeon Phi coprocessor. Thus, the use of compile-time constants benefits the coprocessor more than the processor. In general, NWP has low computational intensity (the number of arithmetic operations per access to memory from last-level cache): often less than 1 and almost never greater than 2.
NIM is a recently developed research dynamical core designed for high-resolution global NWP simulations. 2 The Nonhydrostatic Icosahedral Model (NIM) uses an icosahedral grid as shown above. Except for 12 pentagons, all grid cells are hexagons. Compared to other commonly used discretizations of the sphere, this grid minimizes variation in grid cell spacing across the globe allowing model time step to be maximized. smaller than 10 km. 2. Since its inception, NIM has been a close collaboration involving NWP domain experts and HPC software engineers.