Package org.apfloat.samples
Class PiDistributed
java.lang.Object
org.apfloat.samples.Pi
org.apfloat.samples.PiParallel
org.apfloat.samples.PiDistributed
Calculates 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 ResourceBundle
by 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,
weight2
is the
relative performance of server2
etc. 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
as
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.
PiParallel
with 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
OperationServer
on 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.properties
file 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.
- Version:
- 1.9.0
- Author:
- Mikko Tommila
-
Nested Class Summary
Modifier and TypeClassDescriptionprotected static class
Distributed version of the binary splitting algorithm.static class
Class for calculating pi using the distributed Chudnovskys' binary splitting algorithm.static class
Class for calculating pi using the distributed Ramanujan's binary splitting algorithm.protected static class
RemoteOperationExecutor 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
-
Field Summary
-
Method Summary
-
Method Details
-
main
Command-line entry point.- Parameters:
args
- Command-line parameters.- Throws:
IOException
- In case writing the output fails.ApfloatRuntimeException
-