smart timely decisions at scale



On July 28, 2016, we finalized the hardware and software configurations we tested. Updates for current and

recently released hardware and software appear often, so unavoidably these configurations may not represent

the latest versions available when this report appears. For older systems, we chose configurations representative

of typical purchases of those systems. We concluded hands-on testing on August 2, 2016.

Appendix A – How we calculated results

Scaling throughput

To show how throughput scaled, we applied a weighted average to the throughput the operating

system reported.

We weighted the average by the number of SAS processes reported at each 15-second interval.

We then normalized the throughput weighted average to demonstrate near linear scaling. We normalized the

amplitude of scaling to the single-server configuration.

CPU/real-time ratio

The CPU/real-time ratio divides the total CPU time (user and system) by the total elapsed time to complete every

job in the workload. We report the average CPU/real-time ratio of the servers for each level of scaling.

Some SAS procedures are threaded, so jobs can actually use more CPU cycles than real time. A CPU/real-time

ratio less than 1.0 indicates that the CPU is waiting on resources to finish processes. In most of those cases where

the ratio is less than 1.0, the CPU is waiting on I/O resources from storage. Our solution delivered average CPU/

real-time ratios greater than 1.0 at each level of scaling.

Modernize your SAS analytics infrastructure to get smart, timely decisions at scale September 2016 | 8

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