With the fast growth of parameter size, it becomes increasingly challenging to deploy large
generative models as they typically require large GPU memory consumption and massive …

We present egglog, a fixpoint reasoning system that unifies Datalog and equality saturation
(EqSat). Like Datalog, egglog supports efficient incremental execution, cooperating …

The simplicity of Python and its rich set of libraries has made it the most popular language
for data science. Moreover, the interpreted nature of Python offers an easy debugging …

As modern data pipelines continue to collect, produce, and store various data formats,
extracting and combining value from traditional and context-rich sources becomes …

Tensor algebra is essential for data-intensive workloads in various computational domains.
Computational scientists face a trade-off between the specialization degree provided by …

From FORTRAN to NumPy, arrays have revolutionized how we express computation.
However, arrays in these, and almost all prominent systems, can only handle dense …

Quantum computing has emerged as a promising tool for transforming the landscape of
computing technology. Recent efforts have applied quantum techniques to classical …

We introduce a framework for automatically choosing data structures for efficient query
processing. Our contributions are twofold. First, we introduce a novel low-level intermediate …

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

Sparse tensors are prevalent in many data-intensive applications. However, existing
automatic differentiation (AD) frameworks are tailored towards dense tensors, which makes …