Design investigation of the Lake Calumet Complex: improving water quality to regenerate local industrial, community, and ecosystem resources

This design thesis is an inquiry of the highly industrialized urban landscape of the Lake Calumet Complex on the South Side of the City of Chicago. It examines geologic and anthropogenic strata within this region as waste used for staging various social, industrial, and ecological systems. Today, these social, industrial, and ecological systems are not responsive to each other and certainly do not possess resilient attributes that would allow them to interact within the landscape in perpetuity. The resulting design strategy seeks to re-think the treatment of waste in the landscape into a new framework for future park design. This park will serve as grounds to interweave these complex systems in order to rehabilitate ecosystem functions and improve water quality. Additionally the park hybridizes many social and ecological functions to improve community recreational opportunities and gain public acceptance and appeal.

A parallel numerical solver using hierarchically tiled arrays

Solving linear systems is an important problem for scientific computing. Exploiting parallelism is essential for solving complex systems, and this traditionally involves writing parallel algorithms on top of a library such as MPI. The SPIKE family of algorithms is one well-known example of a parallel solver for linear systems. The Hierarchically Tiled Array data type extends traditional data-parallel array operations with explicit tiling and allows programmers to directly manipulate tiles. The tiles of the HTA data type map naturally to the block nature of many numeric computations, including the SPIKE family of algorithms. The higher level of abstraction of the HTA enables the same program to be portable across different platforms. Current implementations target both shared-memory and distributed-memory models. In this thesis we present a proof-of-concept for portable linear solvers. We implement two algorithms from the SPIKE family using the HTA library. We show that our implementations of SPIKE exploit the abstractions provided by the HTA to produce a compact, clean code that can run on both shared-memory and distributed-memory models without modification. We discuss how we map the algorithms to HTA programs as well as examine their performance. We compare the performance of our HTA codes to comparable codes written in MPI as well as current state-of-the-art linear algebra routines.