org.apfloat.samples.package-info Maven / Gradle / Ivy
/**
Sample applications demonstrating apfloat use.
Three different versions of an application for calculating π are
included. The simplest, {@link org.apfloat.samples.Pi} runs on one
computer using one processor (and one thread) only. {@link org.apfloat.samples.PiParallel}
executes multiple threads in parallel and has vastly better performance
on multi-core computers. Finally, {@link org.apfloat.samples.PiDistributed}
can use multiple separate computers for calculating pi with even
greater processing power.
As a curiosity, two applets are provided for running {@link org.apfloat.samples.Pi}
and {@link org.apfloat.samples.PiParallel} through a graphical user
interface: {@link org.apfloat.samples.PiApplet} and {@link org.apfloat.samples.PiParallelApplet},
correspondingly. These programs can also be run as stand-alone
Java applications: {@link org.apfloat.samples.PiGUI} and {@link org.apfloat.samples.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.
*/
package org.apfloat.samples;