You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
While database management systems (DBMSs) are highly optimized, interactions across the boundary between the programming language (PL) and the DBMS are costly, even for in-process embedded DBMSs. In this paper, we show that programs that interact with the popular embedded DBMS SQLite can be significantly optimized - by a factor of 3.4 in our benchmarks - by inlining across the PL DBMS boundary. We achieved this speed-up by replacing parts of SQLite's C interpreter with RPython code and composing the resulting meta-tracing virtual machine (VM) - called SQPyte - with the PyPy VM. SQPyte does not compromise stand-alone SQL performance and is 2.2 faster than SQLite on the widely used TPC-H benchmark suite.

Cited Abstract(s):
[1]: We present the application of customized code generation to database query evaluation. The idea is to use a collection of highly efficient code templates and dynamically instantiate them to create query- and hardware-specific source code. The source code is compiled and dynamically linked to the database server for processing. Code generation diminishes the bloat of higher-level programming abstractions necessary for implementing generic, interpreted, SQL query engines. At the same time, the generated code is customized for the hardware it will run on. We term this approach holistic query evaluation. We present the design and development of a prototype system called HIQUE, the Holistic Integrated Query Engine, which incorporates our proposals. We undertake a detailed experimental study of the system's performance. The results show that HIQUE satisfies its design objectives, while its efficiency surpasses that of both well-established and currently-emerging query processing techniques.
[1] generate C code from queries and load the compiled shared libraries to execute them. Their compilation process dynamically instantiates carefully handwritten templates to create source code specific for a given query and hardware. The performance of their dynamically generated evaluation plans are comparable to hard-coded equivalents.