The huge demand for computation in artificial intelligence (AI) is driving unparalleled
investments in hardware and software systems for AI. This leads to an explosion in the …

GPUs offer massive parallelism and high-bandwidth memory access, making them an
attractive option for accelerating data analytics in database systems. However, while modern …

The relational data model was designed to facilitate large-scale data management and
analytics. We consider the problem of how to differentiate computations expressed …

Although dominant for tabular data, ML libraries that train tree models over normalized
databases (eg, LightGBM, XGBoost) require the data to be denormalized as a single table …

Database engines have historically absorbed many of the innovations in data processing,
adding features to process graph data, XML, object oriented, and text among many others. In …

Machine learning tasks over image databases often generate masks that annotate image
content (eg, saliency maps, segmentation maps, depth maps) and enable a variety of …

The past two decades have witnessed columnar storage revolutionizing data warehousing
and analytics. However, the rapid growth of machine learning poses new challenges to this …

Although database systems perform well in data access and manipulation, their relational
model hinders data scientists from formulating machine learning algorithms in SQL …

This short paper studies query execution based on message passing on CPU-GPU systems,
using random forests training as the workload. We investigate different data placement and …

Code-generation suits well for reverse mode automatic differentiation as it stores each
partial derivative as a virtual register. Since the introduction of just-in-time compilation in …