Intel® Math Kernel Library (Intel® MKL) contains a wealth of highly optimized math functions that are fundamental to a wide variety of Financial Applications. Intel MKL uses Industry Standard interfaces and can be easily integrated into your current application framework. The Webinar provides an overview of Intel MKL to accelerate financial applications. Topics include:
Overcome the limitation of optimizing compilers in terms of not knowing which code-execution path is most likely to be used. For example, an optimizer can refine a long series of if statements and have it run at great speed; but if it does not know that in the majority of runs, the very last test is the one that is run, the optimizer cannot rearrange the sequence for best possible performance. It has to work on the assumption that all if tests in the sequence are equally probable.
Take advantage of data-cache locality with cache-data blocking. Loops with frequent iterations over large data arrays should be restructured such that the large array is subdivided into smaller blocks, or tiles. Each data element in the array is therefore reused within the data block, so that the block of data fits within the data cache, before operating on the next block or tile.
Minimize performance losses due to unaligned data. Unaligned data can be a potentially serious performance problem. It is important to remember to focus on data elements in the most CPU-intensive parts of your program.
Align data on natural operand size address boundaries. If the data will be accessed with vector instruction loads and stores, align the data on 16-byte boundaries. For best performance, align data as follows: