Pension Projection Models
While the input specification and output analysis ease-of-use features make the PSG models fast to use, they are also fast when executing a run (that is, reading the input databases, performing the requested simulation, and writing the output files). The execution time of a run can vary substantially depending on the nature of the run and on the kind of computer running the models.
The PSG models are designed to take advantage of multi-core computers when operating in OLC mode and when executing stochastic (multi-scenario) runs in ESP mode or RCS mode, so run times will fall substantially in coming years as standard computers have increasingly many cores.
Execution times for several types of runs are described below, followed by notes on how distributed-processing techniques are used to achieve massive speedups by running the Linux version of the PSG models on many cloud-computing servers that can be rented inexpensively by the hour.
The following execution times are for PSG model runs that use 2014 Trustees Report intermediate-cost and other baseline assumptions and that execute under Windows 7 running under the Parallels Desktop hypervisor running under Mac OS X on an iMac computer equipped with one quad-core, hyper-threaded Intel Core i5 chip running at 2.7 gigahertz. This computer is equipped with one SATA disk drive rotating at 5400 RPM and with eight gigabytes of RAM, which is far more memory than needed by the PSG models even when eight threads of execution are being used in an OLC mode run.
A single-scenario, non-stochastic CBA mode run executes in less than one second.
A stochastic CBA mode run, which simulates 500 macro scenarios, each of which is 135 years long, executes in about 0.4 minutes.
A single-scenario, non-stochastic OLC mode run, which generates a one-in-a-thousand sample for each of 156 cohorts born since 1935 and uses the standard 75 year social security actuarial test period, executes in about 2.9 minutes, with the execution time split about evenly between pension and social security benefit calculations. This execution time is for an OLC run that uses eight threads of execution to simulate eight birth cohorts at a time.
A single-scenario ESP mode run, which generates a two percent sample of the 2000 birth cohort, simulates employer-sponsored pensions but not social security, and writes limited pension output, executes in about 0.9 minutes. Note that the two percent sample excludes the spouses of sample individuals, so the total number of life histories simulated is more than twice as many as the 127,388 sample individuals included in the output files.
A single-scenario RCS mode run, which generates a two percent sample of the 2000 birth cohort, simulates current-law-policy social security as well as pensions, and writes detailed benefit output, executes in about 1.5 minutes.
Stochastic runs take proportionately longer, so stochastic OLC mode runs will require many hours to execute even on a multiple-core computer. Experience has shown that analytically useful stochastic ESP and RCS mode runs can be specified using a modest per-scenario sample size that implies run times of only about an hour.
These run execution times are very fast relative to other models that simulate U.S. pensions and social security. Most users will find run execution speed to be no problem when using the PSG models.
The problem of long stochastic OLC mode run times, plus the unusual need of one model user to execute from several hundred to a few thousand runs at a time, led to the development in early 2008 of a Linux version of the PSG models and in 2009 of the AWS Toolkit, which together allow stochastic runs to be executed in pieces simultaneously on many Amazon Web Services (AWS) computers. Using this distributed-processing approach with 25 eight-core AWS computers — each of which rents for no more than $0.66 per hour, and usually for about $0.07 per hour unless demand for AWS computers is high — a stochastic OLC mode run with 500 scenarios can be executed in one hour at a modest cost.
Other key features.