Queues in series via interacting particle systems
Mathematics of Operations Research
Large Tandem Queueing Networks with Blocking
Queueing Systems: Theory and Applications
IEEE Transactions on Parallel and Distributed Systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Period optimization for hard real-time distributed automotive systems
Proceedings of the 44th annual Design Automation Conference
Adaptive work-stealing with parallelism feedback
ACM Transactions on Computer Systems (TOCS)
Amdahl's Law in the Multicore Era
Computer
Validity of the single processor approach to achieving large scale computing capabilities
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A view of the parallel computing landscape
Communications of the ACM - A View of Parallel Computing
Flexible filters: load balancing through backpressure for stream programs
EMSOFT '09 Proceedings of the seventh ACM international conference on Embedded software
Parallelizable stable explicit numerical integration for efficient circuit simulation
Proceedings of the 46th Annual Design Automation Conference
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With the end of clock-frequency scaling, parallelism has emerged as the key driver of chip-performance growth. Yet, several factors undermine efficient simultaneous use of on-chip resources, which continue scaling with Moore's law. These factors are often due to sequential dependencies, as illustrated by Amdahl's law. Quantifying achievable parallelism can help prevent futile programming efforts and guide innovation toward the most significant challenges. To complement Amdahl's law, we focus on stream processing and quantify performance losses due to stochastic runtimes. Using spectral theory of random matrices, we derive new analytical results and validate them by numerical simulations. These results allow us to explore unique benefits of stochasticity and show that they outweigh the costs for software streams.