Accelerating R with multi-node parallelism – Rmpi, BatchJobs and OpenLava

Gord Sissons, Feng Li In a previous blog we showed how we could use the R BatchJobs package with OpenLava to accelerate a single-threaded k-means calculation by breaking the workload into chunks and running  them as serial jobs. R users frequently need to find solutions to parallelize workloads, and while solutions like multicore and socket level parallelism are good for some problems, when it comes to large problems there is nothing like a distributed cluster. The message passing inter...
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Public cloud provider

Teraproc Cloud Manager provides self-service portal that are required in order for us to serve enterprise and individual users. Their engineers worked hard and delivered the solution on schedule in our cloud project. We are glad to have Teraproc as our partner.
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R studio user

While just entering beta trials, Teraproc's R cluster-as-a-service offering makes it easier than ever to deploy a ready to use multi-user R studio environment to solve large scale parallel problems in R. It looks like a winner.
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Teraproc Cloud Manager

Teraproc Cloud Manager is a complete solution of managing a heterogeneous cloud computing environment. Whether you have traditional or cloud workloads, Teraproc Cloud Manager helps you manage all your applications in one cloud environment. While other cloud management solutions only support applications running in a virtualized environment, Teraproc Cloud Manager is flexible providing your choice of deployment models. Teraproc Cloud Manager is easy to use designed for users with little experienc...
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Why Teraproc for your HPC or Analytic cluster

Guided by a belief that compute and data intensive distributed clusters should be simple, scalable, affordable and open, Teraproc delivers unique, turnkey cluster-as-a-service offerings tailored to design, simulation and scaled-out data science applications. It improves analyst productivity, speeds time to deployment, provides user self-service on cloud, and manages infrastructure costs with the enhanced flexibility.
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Early access for R CaaS

Teraproc announces early registration for our R Cluster-as-a-Service offering. It's the eleventh hour so hurry up and secure your space! Learn more about the service here. As data scientists and statisticians know, R is an excellent language for analytic problems. For large scale problems, configuring distributed Hadoop or compute clusters can be a challenge. Talented technical people can spend days or weeks building out distributed clusters, assembling all the needed software components a...
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Teraproc HPC Cluster-as-a-Service

Teraproc HPC Cluster-as-a-Service is a simple, scalable and affordable way to quickly deliver an HPC cluster in cloud with a built-in workload scheduler. Teraproc customers can focus on their own applications and data, and we do the rest delivering turnkey, elastic, pre-integrated clusters production proven on the industry’s leading public cloud platform. Deploys in minutes with one step cluster creation No infrastructure required by leveraging public cloud Deploys in minutes Sc...
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Teraproc R Analytics Cluster-as-a-Service

The Teraproc R Analytics Cluster-as-a-Service is a simple and affordable way for R users to quickly get ready-to-use R environment in the cloud. Whether you need just a single R Studio instance, or a scaled-out R supercomputer to tackle those big gnarly problems, Teraproc has a solution for you.  Pre-configured with a wealth of R tools and parallel frameworks, clusters can be deployed in minutes and can take advantage of Amazon spot instances to keep costs low. It is comprised of 100% open-sourc...
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