  As diverse high-performance computing (HPC) systems are built, many
opportunities arise for applications to solve larger problems than ever before.
Given the significantly increased complexity of these HPC systems and
application tuning, empirical performance tuning, such as autotuning, has
emerged as a promising approach in recent years. Despite its effectiveness,
autotuning is often a computationally expensive approach. Transfer learning
(TL)-based autotuning seeks to address this issue by leveraging the data from
prior tuning. Current TL methods for autotuning spend significant time modeling
the relationship between parameter configurations and performance, which is
ineffective for few-shot (that is, few empirical evaluations) tuning on new
tasks. We introduce the first generative TL-based autotuning approach based on
the Gaussian copula (GC) to model the high-performing regions of the search
space from prior data and then generate high-performing configurations for new
tasks. This allows a sampling-based approach that maximizes few-shot
performance and provides the first probabilistic estimation of the few-shot
budget for effective TL-based autotuning. We compare our generative TL approach
with state-of-the-art autotuning techniques on several benchmarks. We find that
the GC is capable of achieving 64.37% of peak few-shot performance in its first
evaluation. Furthermore, the GC model can determine a few-shot transfer budget
that yields up to 33.39$\times$ speedup, a dramatic improvement over the
20.58$\times$ speedup using prior techniques.
