Techniques for Optimizing CPU Performance

Executing a single stream of code takes approximately the same amount of CPU time each time that code is executed. Optimizing CPU performance in these instances is usually a tradeoff. For example, you might be able to reduce CPU time by using more memory, because more information can be read and stored in one operation, but less memory is available to other processes.

Also, because the CPU performs all the processing that is needed to perform an I/O operation, an option or technique that reduces the number of I/O operations can also have a positive effect on CPU usage.

Another technique that can improve CPU performance is to store a DATA step that is executed repeatedly as a compiled program rather than as SAS statements. This is especially true for large DATA step jobs that are not I/O-intensive. For more information about storing compiled DATA steps, see Stored Compiled DATA Step Programs.

The PATH= system option specifies the list of directories (or libraries, in some operating environments) that contain SAS executable files. Your default configuration file specifies a certain order for these directories. You can rearrange the directory specifications in the PATH= option so that the most commonly accessed directories are listed first. Place the least commonly accessed directories last.

When SAS processes the program data vector, it typically moves the data in one large operation rather than by individual variables. When data is properly aligned (in 8-byte boundaries), data movement can occur in as little as two clock cycles (a single load followed by a single store). SAS moves unaligned data by more complex means, at worst, a single byte at a time. This would be at least eight times slower for an 8-byte variable.

Many high performance RISC (Reduced Instruction Set Computer) processors pay a very large performance penalty for movement of unaligned data. When possible, leave numeric data at full width (eight bytes). Note that SAS must widen short numeric data for any arithmetic operation. On the other hand, short numeric data can save both memory and I/O. You must determine which method is most advantageous for your operating environment and situation.

SAS System 9 supports a new wave of SAS functionality related to parallel processing. Parallel processing means that processing is handled by multiple CPUs simultaneously. This technology takes advantage of SMP computers and provides performance gains for two types of SAS processes: threaded I/O and threaded application processing.

For information, see Support for Parallel Processing.

When SAS compiles a program, the code is optimized to remove redundant instructions, missing value checks, and repetitive computations for array subscripts. The code detects patterns of instruction and replaces them with more efficient sequences, and also performs optimizations that pertain to the SAS register. In most cases, performing the code-generation optimization is preferable. If you have a large DATA step program, performing code generation optimization can result in a significant increase in compilation time and overall execution time.

You can reduce or turn off the code generation optimization by using the CGOPTIMIZE= system option. Set the code generation optimization that you want SAS to perform using these CGOPTIMIZE= system option values:

For more information, see CGOPTIMIZE= System Option in SAS System Options: Reference.