Population protocol

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A population protocol is a distributed computing model formed by resource-limited mobile agents which meet in a random way according to an interaction graph. Functions are computed by updating the state of agents whenever they meet based on the previous value of the states, and the result of the computation can be read in the states of the agents once the computation has converged.

Population protocols were introduced by Dana Angluin et al.^[1] as one of the first models of computation to be fully decentralized and to involve agents with highly limited resources, e.g., those found in sensor networks. Since then, this abstract computation model found applications in robotics^[2] and chemistry^[3].

References

^ Dana Angluin, James Aspnes, Zoë Diamadi, Michael J. Fischer, René Peralta. Computation in networks of passively mobile finite-state sensors. Distributed Computing, 2006. [1]
^ Gregory Dudek, Michael Jenkin. Computational Principles of Mobile Robotics, Chapter 10.
^ Ho-Lin Chen, David Doty, David Soloveichik. Deterministic function computation with chemical reaction networks. Natural Computing, 2014. [2]

v t e Parallel computing
General	Distributed computing Parallel computing Parallel algorithm Massively parallel Cloud computing High-performance computing Multiprocessing Manycore processor GPGPU Computer network Systolic array
Levels	Bit Instruction Thread Task Data Memory Loop Pipeline
Multithreading	Temporal Simultaneous (SMT) Simultaneous and heterogenous Speculative (SpMT) Preemptive Cooperative Clustered multi-thread (CMT) Hardware scout
Theory	PRAM model PEM model Analysis of parallel algorithms Amdahl's law Gustafson's law Cost efficiency Karp–Flatt metric Slowdown Speedup
Elements	Process Thread Fiber Instruction window Array
Coordination	Multiprocessing Memory coherence Cache coherence Cache invalidation Barrier Synchronization Application checkpointing
Programming	Stream processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm
Hardware	Flynn's taxonomy SISD SIMD Array processing (SIMT) Pipelined processing Associative processing MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Beowulf cluster Grid computer Hardware acceleration
APIs	Ateji PX Boost Chapel HPX Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays GPUOpen MPI OpenMP OpenCL OpenHMPP OpenACC Parallel Extensions PVM pthreads RaftLib ROCm UPC TBB ZPL
Problems	Automatic parallelization Deadlock Deterministic algorithm Embarrassingly parallel Parallel slowdown Race condition Software lockout Scalability Starvation
Category: Parallel computing

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