Matrix analysis
Randomized parallel algorithms for backtrack search and branch-and-bound computation
Journal of the ACM (JACM)
A polynomial algorithm for deciding bisimilarity of normed context-free processes
Theoretical Computer Science
Complexity and real computation
Complexity and real computation
Space-efficient scheduling of nested parallelism
ACM Transactions on Programming Languages and Systems (TOPLAS)
Scheduling multithreaded computations by work stealing
Journal of the ACM (JACM)
PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Model Checking Probabilistic Pushdown Automata
LICS '04 Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science
Quantitative Analysis of Probabilistic Pushdown Automata: Expectations and Variances
LICS '05 Proceedings of the 20th Annual IEEE Symposium on Logic in Computer Science
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
On the convergence of Newton's method for monotone systems of polynomial equations
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Adaptive work-stealing with parallelism feedback
ACM Transactions on Computer Systems (TOCS)
Recursive Markov chains, stochastic grammars, and monotone systems of nonlinear equations
Journal of the ACM (JACM)
Undecidable equivalences for basic parallel processes
Information and Computation
Non-interleaving bisimulation equivalences on Basic Parallel Processes
Information and Computation
On fixed point equations over commutative semirings
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
An extension of Newton's method to ω-continuous semirings
DLT'07 Proceedings of the 11th international conference on Developments in language theory
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We study the problem of scheduling tasks for execution by a processor when the tasks can stochastically generate new tasks. Tasks can be of different types, and each type has a fixed, known probability of generating other tasks. We present results on the random variable S^@s modeling the maximal space needed by the processor to store the currently active tasks when acting under the scheduler @s. We obtain tail bounds for the distribution of S^@s for both offline and online schedulers, and investigate the expected value E[S^@s].