Effects of synchronization barriers on multiprocessor performance
Parallel Computing
High-performance computer architecture
High-performance computer architecture
Communications of the ACM
Approximate Analysis of Fork/Join Synchronization in Parallel Queues
IEEE Transactions on Computers
Performance Analysis of Parallel Processing Systems
IEEE Transactions on Software Engineering
Speedup Versus Efficiency in Parallel Systems
IEEE Transactions on Computers
On the execution of parallel programs on multiprocessor systems—a queuing theory approach
Journal of the ACM (JACM)
A performance evaluation of a general parallel processing model
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Computer Performance Modeling Handbook
Computer Performance Modeling Handbook
Numerical Methods
Communication Issues in the Design and Analysis of Parallel Algorithms
IEEE Transactions on Software Engineering
A performance analysis of local synchronization
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
Analysis of Delays Caused by Local Synchronization
SIAM Journal on Computing
Scheduling parallel processors: Structural properties and optimal policies
Mathematical and Computer Modelling: An International Journal
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In this paper, we derive bounds on the speedup and efficiency of applications that schedule tasks on a set of parallel processors. We assume that the application runs an algorithm that consists of N iterations and before starting its i+1st iteration, a processor must wait for data (i.e., synchronize) calculated in the ith iteration by a subset of the other processors of the system. Processing times and interconnections between iterations are modeled by random variables with possibly deterministic distributions. Scientific applications consisting of iterations of recursive equations are examples of such applications that can be modeled within this formulation. We consider the efficiency of applications and show that, although efficiency decreases with an increase in the number of processors, it has a nonzero limit when the number of processors increases to infinity. We obtain a lower bound for the efficiency by solving an equation that depends on the distribution of task service times and the expected number of tasks needed to be synchronized. We also show that the lower bound is approached if the topology of the processor graph is ldquo;spread-out,” a notion we define in the paper.