The TidalScale Blog

Test Drive TidalScale for Yourself

TidalScale, Test Drive

TidalScale has built and operates very large (1.5TB - 3.0TB RAM) TidalPods for customers to test and evaluate running their applications on a single, virtualized very large system. We call these systems PoC systems (i.e. Proof Of Concept). We are making these systems available to you for a limited time, at no charge, to test your application(s) in a large scale environment. The basic testing program:

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Tips for Scaling Up Open Source R

R, TidalScale


INTRODUCTION - R IN-MEMORY

When we started searching for large scale Open R benchmarks we were surprised to find few good workloads for multi-terabyte sized TidalScale systems.  We ended up writing our own R Benchmark that allowed us to scale R workloads to arbitrarily large in-memory sizes. In the process we learned a few tips and tricks that we thought we'd share for how to run large workloads using Open Source R. 

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Scale Up Open Source R on TidalScale

R, TidalScale

Like many statistical analytic tools, R can be incredibly memory intensive. A simple GAM (generalized additive model) or K-nearest neighbor routine can devour many multiples of memory size compared to the starting dataset. And, R doesn't always behave nicely when it runs out of memory.

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