Vectorize definition4/5/2023 ![]() While some may tell you that “most cycles” needs to be over 90 percent, we have found this number to vary widely based on the application and whether the Intel Xeon Phi coprocessor needs to be the top performance source in a node or just needs to contribute performance. Unless your application is bandwidth limited, the most effective use of Intel Xeon Phi coprocessors will be when most cycles executing are vector instructions 2. Therefore, time spent in the math routines may be considered as vector time. If you are using libraries, like the Intel Math Kernel Library (Intel MKL), you should consider that math routines would remain vectorized no matter how you compile the application itself. Look again at the dramatic benefits vectorization may offer as illustrated in Figure 1.13. If the performance difference is insufficient you should examine opportunities to increase vectorization. For instance, if you are using Intel compilers auto-vectorization: disable vectorization via compiler switches: -no-vec -no-simd, use at least –O2 -xhost for vectorization. To check vectorization, compile your application with and without vectorization. If the performance graph indicates any significant trailing off of performance, you have tuning work you can do to improve your application scaling before trying an Intel Xeon Phi coprocessor. ![]() This can be done with settings for OpenMP, Intel® Threading Building Blocks (Intel TBB) or Intel Cilk Plus (for example, OMP_NUM_THREADS for OpenMP). ![]() To check scaling, create a simple graph of performance as you run with various numbers of threads (from one up to the number of cores, with attention to thread affinity) on an Intel Xeon processor-based system. Assuming you have a working application, you can get some impression of where you are with regards to scaling and vectorization by doing a few simple tests. To know if your application is maximized on an Intel Xeon processor-based system, you should examine how your application scales, uses vectors, and uses memory. Jim Jeffers, James Reinders, in Intel Xeon Phi Coprocessor High Performance Programming, 2013 Measuring readiness for highly parallel execution 4.Īrray alignment can play an important role in the decisions about vectorization and care must be taken to appropriately specify the array alignment directives. These are typically algorithmic in nature and may require major data structure reorganization to eliminate. True indirection (indexing an array with a subscript that is also an array element). Loop interchange and data structure transformations can eliminate some of these. These can be due to indexing in multidimensional arrays, or due to accessing fields in arrays of structures. #pragma simd and #pragma ivdep can be used to tell the compiler to ignore unknown dependences or to tell it that dependences are of a certain type, such as a reduction. Problems are typically one or more of: 1. Examination of the vectorization report may provide insight into the problems. The compiler vectorization report should be examined and see if the code can be restructured to enable better vectorization. For example, lots of scalar operations or lots of gathers and scatters lower vectorization intensity. If vectorization intensity is < 0.5, the vector units of Knights Landing are not effectively being utilized in the application - typically indicated poor vectorization by the compiler due to some issues in the code.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |