Evolving CUDA PTX programs by quantum inspired linear genetic programming

Abstract

The tremendous computing power of Graphics Processing Units (GPUs) can be used to accelerate the evolution process in Genetic Programming (GP). The automatic generation of code using the GPU usually follows two different approaches: compiling each evolved or interpreting multiple programs. Both approaches, however, have performance drawbacks. In this work, we propose a novel approach where the GPU pseudo-assembly language, PTX (Parallel Thread Execution), is evolved. Evolving PTX programs is faster, since the compilation of a PTX program takes orders of magnitude less time than a CUDA program compilation on the CPU, and no interpreter is necessary. Another important aspect of our approach is that the evolution of PTX programs follows the Quantum Inspired Linear Genetic Programming (QILGP). Our approach, called QILGP3U (QILGP + GPGPU), enables the evolution on a single machine in a reasonable time, enhances the quality of the model with the use of PTX, and for big databases can be much faster than the CPU implementation.