Evolving a CUDA kernel from an nVidia template
Evolving a CUDA kernel from an nVidia template
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从 nVidia 模板演化 CUDA 内核
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
M. Harman
中科院分区：
文献类型：
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作者：
W. Langdon;M. Harman
而不是试图从零开始发展一个完整的程序，我们展示了遗传接口编程（GIP）自动生成一个并行CUDA内核与现有的高度优化的古代顺序C代码（gzip）相同的功能。通用GPGPU nVidia内核C++代码转换为BNF语法。强类型遗传编程使用BNF来生成可编译和可执行的图形卡内核。它们的适应度是通过在GPU上运行种群来给出的，这些种群具有来自gzip的SIR测试套件的随机训练数据子集。背靠背验证使用原始代码作为测试预言机。
Rather than attempting to evolve a complete program from scratch we demonstrate genetic interface programming (GIP) by automatically generating a parallel CUDA kernel with identical functionality to existing highly optimised ancient sequential C code (gzip). Generic GPGPU nVidia kernel C++ code is converted into a BNF grammar. Strongly typed genetic programming uses the BNF to generate compilable and executable graphics card kernels. Their fitness is given by running the population on a GPU with randomised subsets of training data itself derived from gzip's SIR test suite. Back-to-back validation uses the original code as a test oracle.