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Original author(s) | Jonathan Beard |
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Initial release | late 2014 (late 2014) |
Stable release | 0.9 / January 2020 (2020-01) |
Preview release | 1.0a / May 18, 2020; 4 years ago (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[1] 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.[2] 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).
Here is a Hello World example for demonstration purposes:[3]
#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 );
}
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General |
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Multithreading |
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Coordination |
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Programming |
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