public class PiDistributed extends PiParallelCalculates pi using a cluster of servers. The servers should be running
OperationServer. The names and ports of the cluster nodes are read from the file
cluster.properties, or a
ResourceBundleby the name "cluster". The format of the property file is as follows:
server1=hostname.company.com:1234 server2=hostname2.company.com:2345 server3=hostname3.company.com:3456 weight1=100 weight2=110 weight3=50The server addresses are specified as hostname:port. Weights can (but don't have to) be assigned to nodes to indicate the relative performance of each node, to allow distributing a suitable amount of work for each node. For example,
weight2is the relative performance of
server2etc. The weights must be integers in the range 1...1000.
Guidelines for configuring the servers:
- If the machines are not identical, give proper weights to every machine. This can improve performance greatly.
- If the machines are somewhat similar (e.g. same processor but
different clock frequency), you can calculate the weight roughly
clockFrequency * numberOfProcessors. For example, a machine with two 1600MHz processors is four times as fast as a machine with one 800MHz processor.
- If the machines are very heterogenous, you can benchmark their
performance by running e.g.
PiParallelwith one million digits. Remember to specify the correct number of CPUs on each machine.
- Different JVMs can have different performance. For example, Sun's Java client VM achieves roughly two thirds of the performance of the server VM when running this application.
- When running
OperationServeron the cluster nodes, specify the number of worker threads for each server to be the same as the number of CPUs of the machine.
- Additionally, you should specify the number of processors
correctly in the
apfloat.propertiesfile for each cluster server.
Similarly as with
PiParallel, if some nodes have multiple CPUs, to get any performance gain from running many threads in parallel, the JVM must be executing native threads. If the JVM is running in green threads mode, there is no advantage of having multiple threads, as the JVM will in fact execute just one thread and divide its time to multiple simulated threads.
- Mikko Tommila
Nested Class Summary
Nested Classes Modifier and Type Class Description
protected static class
PiDistributed.DistributedBinarySplittingPiCalculatorDistributed version of the binary splitting algorithm.
PiDistributed.DistributedChudnovskyPiCalculatorClass for calculating pi using the distributed Chudnovskys' binary splitting algorithm.
PiDistributed.DistributedRamanujanPiCalculatorClass for calculating pi using the distributed Ramanujan's binary splitting algorithm.
protected static class
PiDistributed.NodeRemoteOperationExecutor that implements the weight property.
Nested classes/interfaces inherited from class org.apfloat.samples.PiParallel
PiParallel.ParallelBinarySplittingPiCalculator, PiParallel.ParallelChudnovskyPiCalculator, PiParallel.ParallelRamanujanPiCalculator, PiParallel.ThreadLimitedOperation<T>
Nested classes/interfaces inherited from class org.apfloat.samples.Pi
Pi.AbstractBinarySplittingSeries, Pi.BinarySplittingPiCalculator, Pi.BinarySplittingProgressIndicator, Pi.BinarySplittingSeries, Pi.BorweinPiCalculator, Pi.ChudnovskyBinarySplittingSeries, Pi.ChudnovskyPiCalculator, Pi.GaussLegendrePiCalculator, Pi.RamanujanBinarySplittingSeries, Pi.RamanujanPiCalculator
Methods inherited from class org.apfloat.samples.Pi
checkAlive, getErr, getInt, getLong, getOut, getPrecision, getRadix, run, setAlive, setErr, setOut