Difference between revisions of "Pigeon"

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'''Pigeon''',<br/>
 
'''Pigeon''',<br/>
 
an original, experimental 64-bit [[UCI]] compliant chess engine by [[Stuart Riffle]], written in [[Cpp|C++]].  
 
an original, experimental 64-bit [[UCI]] compliant chess engine by [[Stuart Riffle]], written in [[Cpp|C++]].  
Pigeon is [[:Category:Open Source|open source]], released under the [https://en.wikipedia.org/wiki/MIT_License MIT license].  
+
Pigeon is [[:Category:Open Source|open source]], released under the [[Massachusetts Institute of Technology#License|MIT license]].  
 
To support [[SIMD and SWAR Techniques|SIMD]] by [[SSE2]] or [[AVX2]] instructions as well as [https://en.wikipedia.org/wiki/CUDA CUDA] for [[Nvidia]] [[GPU|GPUs]], much of the critical code is branch-free.  
 
To support [[SIMD and SWAR Techniques|SIMD]] by [[SSE2]] or [[AVX2]] instructions as well as [https://en.wikipedia.org/wiki/CUDA CUDA] for [[Nvidia]] [[GPU|GPUs]], much of the critical code is branch-free.  
 
Pigeon generates [[Sliding Piece Attacks|sliding piece attacks]] by [[Kogge-Stone Algorithm|Kogge-Stone algorithm]] <ref>[https://github.com/StuartRiffle/pigeon/blob/master/src/bits.h pigeon/bits.h at master · StuartRiffle/pigeon · GitHub]</ref>.  
 
Pigeon generates [[Sliding Piece Attacks|sliding piece attacks]] by [[Kogge-Stone Algorithm|Kogge-Stone algorithm]] <ref>[https://github.com/StuartRiffle/pigeon/blob/master/src/bits.h pigeon/bits.h at master · StuartRiffle/pigeon · GitHub]</ref>.  

Revision as of 16:54, 25 August 2020

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Picasso - La Colombe [1]

Pigeon,
an original, experimental 64-bit UCI compliant chess engine by Stuart Riffle, written in C++. Pigeon is open source, released under the MIT license. To support SIMD by SSE2 or AVX2 instructions as well as CUDA for Nvidia GPUs, much of the critical code is branch-free. Pigeon generates sliding piece attacks by Kogge-Stone algorithm [2]. It performs a monochrome move generation approach, color flipping the board after make move [3], has an iterative search, and used automated tuning of evaluation weights by logistic regression.

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