The resurgence of current and upcoming multithreaded architectures and programming models led us to conduct a detailed study to understand the potential of these platforms to increase the performance of data-intensive, irregular scientific applications. Our study is based on a power system state estimation application and a novel anomaly detection application applied to network traffic data. We also conducted a detailed evaluation of the platforms using microbenchmarks in order to gain insight into their architectural capabilities and their interaction with programming models and application software. The evaluation was performed on the Cray MTA-2 and the Sun Niagara.

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Two of the most commonly used hashing strategies-linear probing and hashing with chaining-are adapted for efficient execution on a Cray XMT. These strategies are designed to minimize memory contention. Datasets that follow a power law distribution cause significant performance challenges to shared memory parallel hashing implementations. Experimental results show good scalability up to 128 processors on two power law datasets with different data types: integer and string. These implementations can be used in a wide range of applications.

Quantitative information flow (QIF) is concerned with measuring how much of a secret is leaked to an adversary who observes the result of a computation that uses it. Prior work has shown that QIF techniques based on abstract interpretation with probabilistic polyhedra can be used to analyze the worst-case leakage of a query, on-line, to determine whether that query can be safely answered. While this approach can provide precise estimates, it does not scale well. This paper shows how to solve the scalability problem by augmenting the baseline technique with sampling and symbolic execution. We prove that our approach never underestimates a query's leakage (it is sound), and detailed experimental results show that we can match the precision of the baseline technique but with orders of magnitude better performance.

The massive data sets produced by the highthroughput, multidimensional mass spectrometry instruments used in proteomics create challenges in data acquisition, storage and analysis. Data compression can help mitigate some of these problems but at the cost of less efficient data access, which directly impacts the computational time of data analysis. We have developed a compression methodology that 1) is optimized for a targeted mass spectrometry proteomics data set and 2) provides the benefits of size and speed from compression while increasing analysis efficiency by allowing extraction of segments of uncompressed data from a file without having to uncompress the entire file. This paper describes our compression algorithm, presents comparative metrics of compression size and speed, and explores approaches for applying the algorithm to a generalized data set.