We present an overview of our ongoing work on parallelizing self-adjusting-computation techniques. In self-adjusting computation, programs can respond to changes to their data (e....
Matthew Hammer, Umut A. Acar, Mohan Rajagopalan, A...
The predominant thread-based approach to concurrent programming is bug-prone, difficult to reason about, and does not scale well to large numbers of processors. Sieves provide a s...
We describe a framework for better understanding scheduling policies for fine-grained parallel computations and their effect on space usage. We define a profiling semantics that c...