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Practical Abstractions for Dynamic and Parallel Software
{ Thu, 12 Jul 2012, 14h00 }

By: Umut A. Acar  [ show info ]

Developing efficient and reliable software is a difficult task. Rapidly growing and dynamically changing data sets further increase complexity by making it more challenging to achieve efficiency and performance. I present practical and powerful abstractions for taming software complexity in two large domains: 1) dynamic software that interacts with dynamically changing data, and 2) parallel software that utilizes multiple processors or cores. Together with the algorithmic models and programming-languages that embody them, these abstractions enable designing and developing efficient and reliable software by using high-level reasoning principles and programming techniques. As evidence of their effectiveness, I consider a broad range benchmarks involving lists, arrays, matrices, and trees, as well as sophisticated applications in geometry, machine-learning, and large-scale cloud computing. On the theoretical side, I show asymptotically significant improvements in efficiency and present solutions to several open problems. On the practical side, I present programming languages, compilers, and related software systems that deliver massive speedups with little or no programmer effort.

Hosted by: Computer Systems

Location: DI seminars room

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