performance optimization

Optimize Financial Applications using Intel® Math Kernel Library

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:

  • Desenvolvedores
  • Apple OS X*
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8
  • C/C++
  • Fortran
  • Principiante
  • Intermediário
  • Compilador C++ Intel®
  • Composer XE C++ Intel®
  • Compilador Fortran Intel®+
  • Composer XE Fortran Intel®
  • Biblioteca kernel de matemática Intel®
  • Learning Lab
  • FSI
  • Financial Services
  • Intel MKL Training
  • performance optimization
  • Ferramentas de desenvolvimento
  • Serviços financeiros
  • Otimização
  • Computação paralela
  • MEM_TRANS_RETIRED.LOAD_LATENCY events

    There are 8 MEM_TRANS_RETIRED.LOAD_LATENCY_GT_* precise events available on Intel® Microarchitecture Codename Sandy Bridge.  The events allow you to pinpoint loads that exceeded a given latency, measured in CPU clock cycles.  For example, the MEM_TRANS_RETIRED.LOAD_LATENCY_GT_4 event is for loads exceeding 4 clocks in latency, and the MEM_TRANS_RETIRED.LOAD_LATENCY_GT_512 event is for loads longer than 512 clocks. 

  • Intel® VTune™ Amplifier XE
  • performance tuning
  • performance optimization
  • performance profiler
  • event-based sampling
  • Optimize Code for the Most-Often Used Code Path


    Challenge

    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.

  • Execution
  • performance optimization
  • Computação paralela
  • Create Cache-Data Blocks


    Challenge

    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.

  • Memory cache
  • performance optimization
  • Computação paralela
  • Assine o performance optimization