We develop BenchPress, the first ML benchmark generator for compilers that is
steerable within feature space representations of source code. BenchPress
synthesizes compiling functions by adding new code in any part of an empty or
existing sequence by jointly observing its left and right context, achieving
excellent compilation rate. BenchPress steers benchmark generation towards
desired target features that has been impossible for state of the art
synthesizers (or indeed humans) to reach. It performs better in targeting the
features of Rodinia benchmarks in 3 different feature spaces compared with (a)
CLgen - a state of the art ML synthesizer, (b) CLSmith fuzzer, (c) SRCIROR
mutator or even (d) human-written code from GitHub. BenchPress is the first
generator to search the feature space with active learning in order to generate
benchmarks that will improve a downstream task. We show how using BenchPress,
Grewe's et al. CPU vs GPU heuristic model can obtain a higher speedup when
trained on BenchPress's benchmarks compared to other techniques. BenchPress is
a powerful code generator: Its generated samples compile at a rate of 86%,
compared to CLgen's 2.33%. Starting from an empty fixed input, BenchPress
produces 10x more unique, compiling OpenCL benchmarks than CLgen, which are
significantly larger and more feature diverse.

BenchPress 是第一个可以在源代码的特征空间表征内进行控制的编译器 ML 基准生成器，通过在任何空白或现有序列的任何部分添加新代码来综合编译函数，从而与（a）CLgen，（b） CLSmith fuzzer，（c）SRCIROR mutator 甚至（d）GitHub 上的人工编写代码相比，在 3 种不同的特征空间中更好地定位 Rodinia 基准测试的功能。（由于）使用主动学习在特征空间搜索，BenchPress 是第一个生成用于改进下游任务的基准测试的生成器。