Beyond Simple Monte-Carlo: Parallel Computing with QuantLib

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Multi-Threading: Observer-Pattern◮ Main purpose in QuantLib: Distributed event handling.◮ Current implementation is highly optimized for singlethreading performance.◮ In a thread local environment this would be sufficient, but ...◮ ... the parallel garbage collector in C#/F#, Java or Scala isby definition not thread local!◮ Shuo Chen article ”Where Destructors meet Threads”provides a good solution ...◮ ... but is not applicable to QuantLib without a major redesignof the observer pattern.Klaus SpanderenBeyond Simple Monte-Carlo: Parallel Computing with QuantLib
Multi-Threading: Observer-PatternScala example fails immediately with spurious error messages◮ pure virtual function call◮ segmentation faultimport org.quantlib.{Array => QArray, _}object ObserverTest {def main(args: Array[String]) : Unit = {System.loadLibrary("QuantLibJNI");val aSimpleQuote = new SimpleQuote(0)}}while (true) {(0 until 10).foreach(_ => {new QuoteHandle(aSimpleQuote)aSimpleQuote.setValue(aSimpleQuote.value + 1)})System.gc}Klaus SpanderenBeyond Simple Monte-Carlo: Parallel Computing with QuantLib
Page 1 and 2: Beyond Simple Monte-Carlo:Parallel
Page 3 and 4: Symmetric Multi-Processing: Overvie
Page 5 and 6: Multi-Threading: Overview◮ QuantL
Page 7: Multi-Threading: Singleton◮ Ricca
Page 11 and 12: Finite Differences Methods on GPUs:
Page 13 and 14: Spare Matrix Libraries for GPUsPerf
Page 15 and 16: Example I: Heston-Hull-White Model
Page 17 and 18: Example II: Heston Model on GPUsHes
Page 19 and 20: Example III: Virtual Power PlantGTX
Page 21 and 22: Quasi Monte-Carlo on GPUs: Arithmet
Page 23 and 24: Quasi Monte-Carlo on GPUs: QuantLib
Page 25 and 26: Quasi Monte-Carlo on GPUs: Scramble
Page 27 and 28: Message Passing Interface (MPI): Mo
Page 29 and 30: Message Passing Interface (MPI): Mo

Beyond Simple Monte-Carlo: Parallel Computing with QuantLib

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