frame or pixel may be rendered independently (see parallel rendering). Some brute-force searches in cryptography. Notable real-world examples include distributed.net and proof-of-work systems used in cryptocurrency. BLAST searches in bioinformatics with split databases. Large scale facial recognition systems that compare thousands of arbitrary acquired faces (e.g., a security or surveillance video via closed-circuit television) with similarly large number of previously stored faces (e.g., a rogues gallery or similar watch list). Computer simulations comparing many independent scenarios. Genetic algorithms. Ensemble calculations of numerical weather prediction. Event simulation and reconstruction in particle physics. The marching squares algorithm. Sieving step of the quadratic sieve and the number field sieve. Tree growth step of the random forest machine learning technique. Discrete Fourier transform where each harmonic is independently calculated. Convolutional neural networks running on GPUs. Hyperparameter grid search in machine learning. Parallel search in constraint programming Implementations In R (programming language) – The Simple Network of Workstations (SNOW) package implements a simple mechanism for using a set of workstations or a Beowulf cluster for embarrassingly parallel computations. Similar R packages include "future", "parallel" and others. See also Amdahl's law defines value P, which would be almost or exactly equal to 1 for embarrassingly parallel problems. Map (parallel pattern) Multiprocessing Massively parallel Parallel computing Process-oriented programming Shared-nothing architecture (SN) Symmetric multiprocessing (SMP) Connection Machine Cellular automaton CUDA framework Manycore processor Vector processor References External links Embarrassingly Parallel Computations, Engineering a Beowulf-style Compute Cluster "Star-P: High Productivity Parallel Computing" Parallel computing Distributed computing problems Applications of distributed computing