Package org.apfloat.samples

Sample applications demonstrating apfloat use.

See: Description

Interface Summary

Interface	Description
Operation<T>	Interface for implementing arbitrary operations to be executed.
OperationExecutor	Interface for implementing objects that can execute Operations.
Pi.BinarySplittingSeries	Terms for the binary splitting series.
PiAWT.StatusIndicator	Interface to indicate an error status in the application.

Class Summary

Class	Description
ApfloatHolder	Simple JavaBean to hold one Apfloat.
BackgroundOperation<T>	Class for running an Operation in the background in a separate thread.
LocalOperationExecutor	Class to execute Operations locally.
OperationServer	Server for executing Operations from remote calls.
Pi	Calculates pi using four different algorithms.
Pi.AbstractBinarySplittingSeries	Abstract base class for the binary splitting series.
Pi.BinarySplittingPiCalculator	Class for implementing the binary splitting algorithm.
Pi.BinarySplittingProgressIndicator	Indicates progress of the pi calculation using the binary splitting algorithm.
Pi.BorweinPiCalculator	Calculates pi using the Borweins' quartic algorithm.
Pi.ChudnovskyBinarySplittingSeries	Chudnovskys' algorithm terms for the binary splitting series.
Pi.ChudnovskyPiCalculator	Basic class for calculating pi using the Chudnovskys' binary splitting algorithm.
Pi.GaussLegendrePiCalculator	Calculates pi using the Gauss-Legendre algorithm.
Pi.RamanujanBinarySplittingSeries	Ramanujan's algorithm terms for the binary splitting series.
Pi.RamanujanPiCalculator	Basic class for calculating pi using the Ramanujan binary splitting algorithm.
PiApplet	Applet for calculating pi using four different algorithms.
PiAWT	Graphical AWT elements for calculating pi using four different algorithms.
PiDistributed	Calculates pi using a cluster of servers.
PiDistributed.DistributedBinarySplittingPiCalculator	Distributed version of the binary splitting algorithm.
PiDistributed.DistributedChudnovskyPiCalculator	Class for calculating pi using the distributed Chudnovskys' binary splitting algorithm.
PiDistributed.DistributedRamanujanPiCalculator	Class for calculating pi using the distributed Ramanujan's binary splitting algorithm.
PiDistributed.Node	RemoteOperationExecutor that implements the weight property.
PiGUI	AWT client application for calculating pi using four different algorithms.
PiParallel	Calculates pi using multiple threads in parallel.
PiParallel.ParallelBinarySplittingPiCalculator	Parallel version of the binary splitting algorithm.
PiParallel.ParallelChudnovskyPiCalculator	Class for calculating pi using the parallel Chudnovskys' binary splitting algorithm.
PiParallel.ParallelRamanujanPiCalculator	Class for calculating pi using the parallel Ramanujan's binary splitting algorithm.
PiParallel.ThreadLimitedOperation<T>	Class to execute operations while setting ApfloatContext.setNumberOfProcessors(int) to some value.
PiParallelApplet	Applet for calculating pi using multiple threads in parallel.
PiParallelAWT	Graphical AWT elements for calculating pi using multiple threads in parallel.
PiParallelGUI	AWT client application for calculating pi using multiple threads in parallel.
RemoteOperationExecutor	Class to call an OperationServer to execute Operations remotely.

Package org.apfloat.samples Description

Sample applications demonstrating apfloat use.

Three different versions of an application for calculating π are included. The simplest, Pi runs on one computer using one processor (and one thread) only. PiParallel executes multiple threads in parallel and has vastly better performance on multi-core computers. Finally, PiDistributed can use multiple separate computers for calculating pi with even greater processing power.

As a curiosity, two applets are provided for running Pi and PiParallel through a graphical user interface: PiApplet and PiParallelApplet, correspondingly. These programs can also be run as stand-alone Java applications: PiGUI and PiParallelGUI.

Compared to the C++ version of apfloat, the Java version pi calculation program is usually just as fast. Even in worst cases the Java version achieves roughly 50% of the performance of the assembler-optimized C++ versions of apfloat. Modern JVMs are nearly as efficient as optimizing C++ compilers in code generation. The advantage that JVMs have over native C++ compilers is obviously that the JVM generates optimal code for every target architecture and runtime profile automatically, from an intermediate portable binary executable format. With C++, the source code must be compiled and profiled manually for every target architecture, which can be difficult and tedious.

On multi-core computers the Java parallel pi calculator is often significantly faster than the C++ parallel version. The same applies to the distributed pi calculator. Multi-threaded and distributed applications are more efficient to implement in Java due to C++'s historical lack of standard libraries for threading and networking.