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Revolution R Enterprise DistributedR
DistributedR Enables Revolution R Enterprise To Run On Diverse Architectures from Windows Laptops and Compute Clusters to EDWs and Hadoop.
Revolution R Enterprise DistributedR is a parallel computing framework responsible for managing compute resources used by Revolution R Enterprise.
By providing consistent management of memory, cores, processors, threads and servers, DistributedR enables R scripts to take advantage of multiple architectures.
DistributedR Provides A Consistent, Portable Platform for R Analytics
In order to support systems as divergent as Teradata Database, various Hadoop distributions and Microsoft HPC, Revolution R Enterprise DistributedR provides consistent task execution and data communications. With Revolution R Enterprise DistributedR, new platforms do not require new versions of Revolution R Enterprise ScaleR, assuring:
- Completeness: Virtually all of Revolution R Enterprise ScaleR’s algorithms are available on each supported platform
- Consistency: Behavior of Revolution R Enterprise ScaleR algorithms is consistent from platform to platform despite big differences in capability and architecture.
- Performance: Revolution R Enterprise DistributedR assures that Revolution R Enterprise ScaleR algorithms run efficiently in all environments using optimum resource management, communications and I/O.
DistributedR Simplifies Portability of R Analytics
Revolution R Enterprise DistributedR brings portability to ScaleR algorithms. By changing just one simple line in an R script, a user can direct the execution of R analytics to run on any supported Revolution R Enterprise platform.
Support for EDWs and Hadoop Clusters
With versions for Teradata Database and multiple distributions of Hadoop, DistributedR maximizes the range of data scale and compute performance available to R developers through support of popular Big Data platforms.