This paper proposes an autotuning framework for OpenMP, a popular parallel programming model. The framework uses machine learning to optimize OpenMP applications.
We accepted static configurations as a necessary evil for decades. We spun up servers, set our ulimit , tuned our garbage collection threads, and prayed to the gods of latency that the defaults held under pressure.
This survey paper provides an overview of autotuning techniques, including machine learning-based approaches, and their applications in high-performance computing.
This paper proposes an autotuning framework for OpenMP, a popular parallel programming model. The framework uses machine learning to optimize OpenMP applications.
We accepted static configurations as a necessary evil for decades. We spun up servers, set our ulimit , tuned our garbage collection threads, and prayed to the gods of latency that the defaults held under pressure. program autotune
This survey paper provides an overview of autotuning techniques, including machine learning-based approaches, and their applications in high-performance computing. This paper proposes an autotuning framework for OpenMP,