Large-scale algorithms are designed to solve gigantic complex problems. The characterizing feature of large-scale algorithms is their need to have more than one execution engine due to the scale of their data and processing requirements. This chapter starts by discussing what types of algorithms are best suited to be run in parallel. Then, it discusses the issues related to parallelizing algorithms. Next, it presents the Compute Unified Device Architecture (CUDA) architecture and discusses how a single graphics processing unit (GPU) or an array of GPUs can be used to accelerate the algorithms. It also discusses what changes need to be made to the algorithm to effectively utilize the power of the GPU. Finally, this chapter discusses cluster computing and discusses how Apache Spark creates Resilient Distributed Datasets...
United States
Great Britain
India
Germany
France
Canada
Russia
Spain
Brazil
Australia
Singapore
Hungary
Ukraine
Luxembourg
Estonia
Lithuania
South Korea
Turkey
Switzerland
Colombia
Taiwan
Chile
Norway
Ecuador
Indonesia
New Zealand
Cyprus
Denmark
Finland
Poland
Malta
Czechia
Austria
Sweden
Italy
Egypt
Belgium
Portugal
Slovenia
Ireland
Romania
Greece
Argentina
Netherlands
Bulgaria
Latvia
South Africa
Malaysia
Japan
Slovakia
Philippines
Mexico
Thailand