Integral is a top chess engine developed in C++. It is a personal project developed with the goal of meshing my admiration for chess with programming.
Play against Integral on Lichess
Integral implements the widely adopted negamax search approach with alpha-beta pruning, and alongside it the various search heuristics that it enables. It utilizes the Lazy SMP approach for multi-threaded search, and has been proven to scale very well at higher thread counts compared to other alpha-beta chess engines.
Integral utilizes an efficiently updatable neural network (NNUE) for its evaluation function.
Integral's neural network is a horizontally mirrored perspective network, containing 12 factorized king input buckets, an L1 of 1536 neurons, an L2 of 16 neurons, an L3 of 32 neurons, and 8 output buckets.
(768x12 (Factorized) -> 1536)x2 -> (16 -> 32 -> 1)1x8
This neural network is trained on tens of millions of self-play games. Each self-play game starts with 3-4 randomly selected moves off a randomly selected opening from the UHO_Lichess_4852_v1 book. Additionally, 5-man Syzygy endgame tablebases are used to guide the data generation search.
The first iteration of Integral's neural network was trained on data from version 4, which had a powerful hand-crafted evaluation (HCE). Each iteration of Integral's neural network since then has been trained on a fresh dataset using the prior network. All networks are trained using the Bullet trainer, which has made my life way easier.
Note
Integral should be compiled with GCC >= v13 or Clang >= v10
Integral provides a Makefile for compilation, but you can use CMake as well.
To compile Integral, enter the following commands in a terminal:
git clone https://github.com/aronpetko/integral
cd integral
make [native | vnni512 | avx512 | avx2_bmi2 | avx2 | sse41_popcnt]
