In this paper, we address the question of how to automatically map computational kernels to
highly efficient code for a wide range of computing platforms and establish the correctness of …

Although compiling queries to efficient machine code has become a common approach for
query execution, a number of newly created database system projects still refrain from using …

The query engines of most modern database systems are either based on vectorization or
data-centric code generation. These two state-of-the-art query processing paradigms are …

Since its invention, data-centric code generation has been adopted for query compilation by
various database systems in academia and industry. These database systems are fast but …

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 …

Today's users of data processing systems come from different domains, have different levels
of expertise, and prefer different programming languages. As a result, analytical workload …

Modern analytics applications use a diverse mix of libraries and functions. Unfortunately,
there is no optimization across these libraries, resulting in performance penalties as high as …

To leverage modern hardware platforms to their fullest, more and more database systems
embrace compilation of query plans to native code. In the research community, there is an …

Compiling queries to machine code is a very efficient way for executing queries. One often
overlooked problem with compilation is the time it takes to generate machine code. Even …

Cardinality estimation (CardEst) is an essential component in query optimizers and a
fundamental problem in DBMS. A desired CardEst method should attain good algorithm …