Chapter 2. Profiling – Measuring Code's Performance
The first step to improve the performance of R programs is to identify where the performance bottlenecks are occurring. To do this, we profile or measure the performance of an R program as it runs with respect to various measures such as execution time, memory utilization, CPU utilization, and disk I/O. This gives us a good idea of how the program and its parts perform, so that we can tackle the biggest bottlenecks first. This chapter will show you how to use a few simple tools to measure the performance of R programs.
The 80/20 rule is applied here. 80 percent of the possible performance improvements can usually be achieved by tackling 20 percent of the largest performance problems. We will look at how to determine which problems to solve first in order to get maximum improvement in the least amount of time and effort.
This chapter covers the following topics:
- Measuring the total execution time
- Profiling the execution...
