Big data analytics enjoy increasingly wide applications in the real world enabled by the development of model, data, and hardware. However, the development of these three components usually shows a significant imbalance. While there are many new models and data, commercialized hardware usually provides only limited support. My research mitigates this gap by building systems for efficient big data analytics that stitch model, data, and hardware together. In particular, we find that given specialized hardware with limited compute primitives, system optimizations are the keys to generalizing such specialized hardware and efficiently supporting diverse model and data workload.

This thesis discusses systems for efficient big data analytics under three aspects. The first aspect introduces hardware-aware kernel tuning. Based on limited hardware compute primitives, my research builds more middle-level libraries to efficiently support diverse big data analytic workloads. The second aspect proposes runtime systems for efficient neural network inference. While larger neural networks are used for general workload, a specialized workload is usually observed in a specific scenario. My research builds runtime systems to automatically detect the scenario information during runtime and exploit such information for efficient big data analytics. The third aspect is to build secure deep learning frameworks to efficiently support diverse workloads such as zero-knowledge neural networks and neural network verification. My research abstracts the key computing patterns in secure deep learning and automatically optimizes diverse NN operators with framework supports.