.. _chap_performance: Computational Performance ========================= In deep learning, datasets and models are usually large, which involves heavy computation. Therefore, computational performance matters a lot. This chapter will focus on the major factors that affect computational performance: imperative programming, symbolic programming, asynchronous computing, automatic parallelism, and multi-GPU computation. By studying this chapter, you may further improve computational performance of those models implemented in the previous chapters, for example, by reducing training time without affecting accuracy. .. toctree:: :maxdepth: 2 hybridize async-computation auto-parallelism hardware multiple-gpus multiple-gpus-concise parameterserver