  The substantial increase in data volume and complexity expected from future
experiments will require significant investment to prepare experimental
algorithms. These algorithms include physics object reconstruction,
calibrations, and processing of observational data. In addition, the changing
computing architecture landscape, which will be primarily composed of
heterogeneous resources, will continue to pose major challenges with regard to
algorithmic migration. Portable tools need to be developed that can be shared
among the frontiers (e.g., for code execution on different platforms) and
opportunities, such as forums or cross-experimental working groups, need to be
provided where experiences and lessons learned can be shared between
experiments and frontiers. At the same time, individual experiments also need
to invest considerable resources to develop algorithms unique to their needs
(e.g., for facilities dedicated to the experiment), and ensure that their
specific algorithms will be able to efficiently exploit external heterogeneous
computing facilities. Common software tools represent a cost-effective
solution, providing ready-to-use software solutions as well as a platform for
R\&D work. These are particularly important for small experiments which
typically do not have dedicated resources needed to face the challenges imposed
by the evolving computing technologies. Workforce development is a key concern
across frontiers and experiments, and additional support is needed to provide
career opportunities for researchers working in the field of experimental
algorithm development. Finally, cross-discipline collaborations going beyond
high-energy physics are a key ingredient to address the challenges ahead and
more support for such collaborations needs to be created. This report targets
future experiments, observations and experimental algorithm development for the
next 10-15 years.
