Introduction to Parallel Computing

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era (2019, background): “(Grama et al. 2003) than ParGenes (DNA: 76.7% and AA: 94.4%), but ParGenes needs less computational time.”
Scalable parallel numerical constraint solver using global load balancing (2015, background): “A substantial amount of work exists regarding the parallelization of the branch and bound algorithm with both search-space splitting and work stealing [11, 15, 18].”
Managing computational complexity using surrogate models: a critical review (2020, methodology): “Even methods, such as parallel computing, which is a computation in which many calculations or executions of processes are carried out simultaneously, are not very helpful (Grama 2013).”
Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019, methodology): “We note that for the data parallel MLPs, the parameter updates in the backward pass are accumulated with an allreduce 3 and applied to the replicated parameters on each device [12] in a synchronous fashion, ensuring the updated parameters on each device consistent before every iteration.”
Rateless codes for near-perfect load balancing in distributed matrix-vector multiplication (2018, background): “Distributed Coded Computation The idea that error-correcting codes can be used for faulttolerance in computing tasks has its roots in works on algorithmic fault tolerance [4], [5].”