https://github.com/retronym/gridgain3-test

Test of GridGain 3.0 using its Scala API to implement a Monte Carlo simulation with a stopping criteria.
https://github.com/retronym/gridgain3-test

Last synced: 29 days ago
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Test of GridGain 3.0 using its Scala API to implement a Monte Carlo simulation with a stopping criteria.

• Host: GitHub
• URL: https://github.com/retronym/gridgain3-test
• Owner: retronym
• Created: 2010-09-05T12:23:10.000Z (over 14 years ago)
• Default Branch: master
• Last Pushed: 2010-09-12T15:19:26.000Z (over 14 years ago)
• Last Synced: 2024-10-14T15:35:25.439Z (2 months ago)
• Language: Scala
• Homepage:
• Size: 26.5 MB
• Stars: 4
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.textile

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README

        
h1. GridGain based Monte Carlo Sample

h2. Motivation

"GridGain 3.0":http://www.gridgain.com/product.html is just around the corner, promising a nice blend of calculation grid + data grid + cloud computing. I'm interested in writing Monte Carlo simulations for option pricing.

GridGain comes with a Monte Carlo example, see $GRIDGAIN_HOME/examples/java/org/gridgain/examples/montecarlo/CreditRiskManager.java

But what about simulation without a fixed number of iterations? This doesn't fit into a simple Map/Reduce. Instead, the workers must send a stream of local statistics to a central aggregator that checks for convergence (aka stopping criteria.)

h2. Map/Reduce vs Actors

The problem seems like a good one for a Distributed Actor System, such as Akka. But I don't want to sacrifice the other features of GridGain, such as topology discovery, distributed class loading, adaptive load balancing and failover. It "turns out":http://www.gridgainsystems.com/jiveforums/thread.jspa?threadID=1320 that the GridGain API offers access to enough lower-level functions to break out of the constraints of Map/Reduce.

See $GRIDGAIN_HOME/examples/java/org/gridgain/examples/messaging for examples.

h2. Tools

h3. Scala / Scalar

Clearly, I want to write this in Scala! GridGain 3.0 includes 'Scalar', a Scala friendly API on top of GridGain. I don't actually use too much of this in the final solution, as I needed to implement GridJob explicitly to inject resources.

h3. GridTaskSession

This allows me to communicate with the workers executing a GridTask. I use this to send the model parameters to all nodes to start the simulation, and to cancel all jobs once the stopping criteria are met.

h3. Grid#listenAsync / GridRichNode#send

We can register a listener on the master node, and send messages to this from the workers.

h2. Calculating π

To demonstrate this, PiDigits approximates pi by counting how many randomly drawn points in the square (0, 0) - (1, 1) lie in the circle centered at the origin with radius 1.

Note that ModelData is not actually needed for this problem.