Skip to Main Content
Large Scale and Big Data
book

Large Scale and Big Data

by Sherif Sakr, Mohamed Gaber
June 2014
Intermediate to advanced content levelIntermediate to advanced
636 pages
23h 13m
English
Auerbach Publications
Content preview from Large Scale and Big Data
16 Large Scale and Big Data
parallelism and graph parallelism. Graph parallelism is widely used in many domains
such as machine learning, data mining, physics, and electronic circuit designs, among
others. Many problems in these domains can be modeled as graphs in which verti-
ces represent computations and edges encode data dependencies or communications.
Recall that a graph G is a pair (V, E), where V is a nite set of vertices and E is a nite
set of pairwise relationships, E V × V, called edges. Weights can be associated with
vertices and edges to indicate the amount of work per each vertex and the communica-
tion data per each edge. To e ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Reinventing the Organization for GenAI and LLMs

Reinventing the Organization for GenAI and LLMs

Ethan Mollick
Big Data Analytics for Internet of Things

Big Data Analytics for Internet of Things

Tausifa Jan Saleem, Mohammad Ahsan Chishti
Scala:Applied Machine Learning

Scala:Applied Machine Learning

Pascal Bugnion, Patrick R. Nicolas, Alex Kozlov
Topics in Parallel and Distributed Computing

Topics in Parallel and Distributed Computing

Sushil K Prasad, Anshul Gupta, Arnold L Rosenberg, Alan Sussman, Charles C Weems

Publisher Resources

ISBN: 9781466581500