Homeland security relies heavily on analytical chemistry to identify suspicious materials and persons. Traditionally this role has focused on attribution, determining the type and origin of an explosive, for example. But as technology advances, analytical chemistry can and will play an important role in the prevention and preemption of terrorist attacks. More sensitive and selective detection techniques can allow suspicious materials and persons to be identified even before a final destructive product is made. The work presented herein focuses on the use of commercial and novel detection techniques for application to the prevention of terrorist activities. Although drugs are not commonly thought of when discussing terrorism, narcoterrorism has become a significant threat in the 21st century. The role of the drug trade in the funding of terrorist groups is prevalent; thus, reducing the trafficking of illegal drugs can play a role in the prevention of terrorism by cutting off much needed funding. To do so, sensitive, specific, and robust analytical equipment is needed to quickly identify a suspected drug sample no matter what matrix it is in. Single Particle Aerosol Mass Spectrometry (SPAMS) is a novel technique that has previously been applied to biological and chemical detection. The current work …

Concurrency levels in large-scale, distributed-memory supercomputers are rising exponentially. Modern machines may contain 100,000 or more microprocessor cores, and the largest of these, IBM's Blue Gene/L, contains over 200,000 cores. Future systems are expected to support millions of concurrent tasks. In this dissertation, we focus on efficient techniques for measuring and analyzing the performance of applications running on very large parallel machines. Tuning the performance of large-scale applications can be a subtle and time-consuming task because application developers must measure and interpret data from many independent processes. While the volume of the raw data scales linearly with the number of tasks in the running system, the number of tasks is growing exponentially, and data for even small systems quickly becomes unmanageable. Transporting performance data from so many processes over a network can perturb application performance and make measurements inaccurate, and storing such data would require a prohibitive amount of space. Moreover, even if it were stored, analyzing the data would be extremely time-consuming. In this dissertation, we present novel methods for reducing performance data volume. The first draws on multi-scale wavelet techniques from signal processing to compress systemwide, time-varying load-balance data. The second uses statistical sampling to select a small …