RaftLib

RaftLib

Original author(s)	Jonathan Beard
Initial release	late 2014
Stable release	0.9 / January 2020 (2020-01)
Preview release	1.0a / May 18, 2020 (2020-05-18)
Written in	C++
Operating system	Linux, macOS, Windows
Type	Data analytics, HPC, Signal Processing, Machine Learning, Algorithms, Big Data
License	Apache License 2.0
Website	www.raftlib.io

RaftLib is a portable parallel processing system that aims to provide extreme performance while increasing programmer productivity. It enables a programmer to assemble a massively parallel program (both local and distributed) using simple iostream-like operators. RaftLib handles threading, memory allocation, memory placement, and auto-parallelization of compute kernels. It enables applications to be constructed from chains of compute kernels forming a task and pipeline parallel compute graph. Programs are authored in C++ (although other language bindings are planned).

Example

Here is a Hello World example for demonstration purposes:

#include <raft>
#include <raftio>
#include <cstdlib>
#include <string>
class hi : public raft::kernel
{
public:
hi() : raft::kernel()
{
output.addPort< std::string >( "0" );
}
virtual raft::kstatus run()
{
output[ "0" ].push( std::string( "Hello World\n" ) );
return( raft::stop );
}
};
int
main( int argc, char **argv )
{
/** instantiate print kernel **/
raft::print< std::string > p;
/** instantiate hello world kernel **/
hi hello;
/** make a map object **/
raft::map m;
/** add kernels to map, both hello and p are executed concurrently **/
m += hello >> p;
/** execute the map **/
m.exe();
return( EXIT_SUCCESS );
}

External links

• The RaftLib Project Page
• RaftLib User Wiki
• Project GitHub Repository
• CPPNow RaftLib Tutorial Session
• A Short Introduction to Stream Processing
• Parallel BZip2 Implementation Using RaftLib