  Various state-of-the-art automated reasoning (AR) tools are widely used as
backend tools in research of knowledge representation and reasoning as well as
in industrial applications. In testing and verification, those tools often run
continuously or nightly. In this work, we present an approach to reduce the
runtime of AR tools by 10% on average and up to 20% for long running tasks. Our
improvement addresses the high memory usage that comes with the data structures
used in AR tools, which are based on conflict driven no-good learning. We
establish a general way to enable faster memory access by using the memory
cache line of modern hardware more effectively. Therefore, we extend the
standard C library (glibc) by dynamically allowing to use a memory management
feature called huge pages. Huge pages allow to reduce the overhead that is
required to translate memory addresses between the virtual memory of the
operating system and the physical memory of the hardware. In that way, we can
reduce runtime, costs, and energy consumption of AR tools and applications with
similar memory access patterns simply by linking the tool against this new
glibc library when compiling it. In every day industrial applications this
easily allows to be more eco-friendly in computation. To back up the claimed
speed-up, we present experimental results for tools that are commonly used in
the AR community, including the domains ASP, BMC, MaxSAT, SAT, and SMT.
