Monte-Carlo simulation on Asian Options Pricing

By Michael A Pearce, Published: 09/07/2016, Last Updated: 09/07/2016

This is an exercise in performance optimization on heterogeneous Intel architecture systems based on multi-core processors and manycore (MIC) coprocessors.

NOTE: this lab follows the discussion in Section 4.7.1 and 4.7.2 in the book "Parallel Programming and Optimization with Intel Xeon Phi Coprocessors", second edition (2015). The book can be obtained at xeonphi.com/book

In this step, we will look at how to load-balance in an MPI application running on a heterogeneous cluster. The provided source code is a Monte-Carlo simulation on Asian Options Pricing. For the purposes of this exercise, the actual implementation of the simulation is not important, however those if you are interested in learning more about the simulation itself refer to the Colfax Research website.

Asian Options Code Sample link: https://github.com/ColfaxResearch/Asian-Options

  1. Study "workdistribution.cc" and compile it. Then run the MPI application across all the nodes available to you (including MICs), with one process on each node.

    You should see that there is load-imbalance, where one node finished faster than others.

  2. A simple solution to this load balance is to distribute work unevenly depending on the target system. Implement a tuning variable "alpha" (should be typefloat or double) where the workload MIC receives is alpha times the workload the CPU receives. Each node shpould calculate which options to work on. To do this use the function input "rankTypes", which stores the type (CPU or MIC) of all nodes in the world. "rankTypes[i]" is "1" if the rank "i" node is on a coprocessor, and "0" if it is on a CPU. Make sure every option is accounted for.

    Compile and run the application. Then try to find the "alpha" value that provides the best performance.

  3. The previous implementation, although simple to implement, has the drawback that the alpha value will be dependent on the cluster. To make the application independent of the cluster it runs on, implement boss-worker model in which the boss assigns work to the workers as the workers completes them.

    Compile and run the code to see the performance. Remember that node that has the boss proccess should have 2 processes.

    Hint: To implement th boss worker model, you will need an if statement with two while loops in it. The worker loop should send it's rank to the host, and receive the index that it needs to calculate. The host should use MPI_ANY_SOURCE in it's receive for the rank, and send the next index to the worker rank that it received. When there are no more options to be simulated, the boss should send a "terminate" index (say index of -1). When the worker receives this "terminate" index it should exit the while loop. The host should exit the while loop when "terminate" has been sent to every other process. Finally, don't forget to have MPI_Barrier in before the MPI_Reduce to make sure all processes are done before the reduction happens.

Asian-Options code GitHub link: https://github.com/ColfaxResearch/Asian-Options

Product and Performance Information

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