SAS: Managing Memory and Optimizing System Performance

1. SAS: Managing Memory and Optimizing System Performance Jacek Czajkowski 09/29/2008

2. Optimizing System Performance • Optimizing System Performance consists of managing the interplay of the following three critical computer resources: • I/O • Memory • CPU time

3. Definitions • Performance Statistics are measurements of the total input and output operations (I/O), memory, and CPU time used to process individual DATA or PROC steps. You can obtain these statistics by using SAS system options that can help you measure your job's initial performance and to determine how to improve performance. • System Performance is measured by the overall amount of I/O, memory, and CPU time that your system uses to process SAS programs. By using certain techniques and SAS system options you can reduce or reallocate your usage of these three critical resources to improve system performance. While you may not be able to take advantage of every technique for every situation, you can choose the ones that are best suited for a particular situation.

4. System Options • Options STIMER; NOTE: DATA statement used: real time 1.16 seconds cpu time 0.09 seconds • Options FULLSTIMER; NOTE: The SAS System used: real time 0.16 seconds user cpu time 0.01 seconds system cpu time 0.02 seconds Memory 1162k Page Faults 0 Page Reclaims 2619 Page Swaps 0 Voluntary Context Switches 81 Involuntary Context Switches 6 Block Input Operations 0 Block Output Operations 0

5. Interpreting the Performance Statistics • Real time represents the clock time it took to execute a job or step; it is heavily dependent on the capacity of the system and the current load. As more users share a particular resource, less of that resource is available to you. • CPU time represents the actual processing time required by the CPU to execute the job, exclusive of capacity and load factors. If you must wait longer for a resource, your CPU time will not increase, but your real time will increase. It is not advisable to use real time as the only criterion for the efficiency of your program because you cannot always control the capacity and load demands on your system. A more accurate assessment of system performance is CPU time, which decreases more predictably as you modify your program to become more efficient.

6. Interpreting the Performance Statistics Description of FULLSTIMER Statistics

7. Optimizing I/O • I/O is one of the most important factors for optimizing performance. Most SAS jobs consist of repeated cycles of reading a particular set of data to perform various data analysis and data manipulation tasks. To improve the performance of a SAS job, you must reduce the number of times SAS accesses disk or tape devices. • Process only the necessary variables and observations by: • using WHERE processing • using DROP and KEEP statements • using LENGTH statements • using the OBS= and FIRSTOBS= data set options. • Reduce the number of times it processes the data internally by: • creating SAS data sets • using indexes • accessing data through views

8. Optimizing I/O Process more data each time a device is accessed by: • BUFNO= SAS uses the BUFNO= option to adjust the number of open page buffers when it processes a SAS data set. Increasing this option's value can improve your application's performance by allowing SAS to read more data with fewer passes; however, your memory usage increases. Experiment with different values for this option to determine the optimal value for your needs. • BUFSIZE= When the Base SAS engine creates a data set, it uses the BUFSIZE= option to set the permanent page size for the data set. The page size is the amount of data that can be transferred for an I/O operation to one buffer. The default value for BUFSIZE= is determined by your operating environment. Note that the default is set to optimize the sequential access method. To improve performance for direct (random) access, you should change the value for BUFSIZE=. Whether you use your operating environment's default value or specify a value, the engine always writes complete pages regardless of how full or empty those pages are. If you know that the total amount of data is going to be small, you can set a small page size with the BUFSIZE= option, so that the total data set size remains small and you minimize the amount of wasted space on a page. In contrast, if you know that you are going to have many observations in a data set, you should optimize BUFSIZE= so that as little overhead as possible is needed. Note that each page requires some additional overhead. Large data sets that are accessed sequentially benefit from larger page sizes because sequential access reduces the number of system calls that are required to read the data set. Note that because observations cannot span pages, typically there is unused space on a page.

9. Optimizing I/O • SASFILE global statement The SASFILE global statement opens a SAS data set and allocates enough buffers to hold the entire data set in memory. Once it is read, data is held in memory, available to subsequent DATA and PROC steps, until either a second SASFILE statement closes the file and frees the buffers or the program ends, which automatically closes the file and frees the buffers. Using the SASFILE statement can improve performance by reducing multiple open/close operations (including allocation and freeing of memory for buffers) to process a SAS data set to one open/close operation reducing I/O processing by holding the data in memory.

10. Optimizing I/O • COMPRESS= One further technique that can reduce I/O processing is to store your data as compressed data sets by using the COMPRESS= data set option. However, storing your data this way means that more CPU time is needed to decompress the observations as they are made available to SAS. But if your concern is I/O, and not CPU usage, compressing your data may improve the I/O performance of your application.

11. Optimizing Memory Usage • If memory is a critical resource, several techniques can reduce your dependence on increased memory. However, most of them also increase I/O processing or CPU usage. • However, by increasing memory available to SAS, you can decrease processing time because the amount of time that is spent on paging, or reading pages of data into memory, is reduced. • MEMSIZE= Specifies the limit on the total amount of memory to be used by the SAS System SAS does not automatically reserve or allocate the amount of memory that you specify in the MEMSIZE option. SAS will only use as much memory as it needs to complete a process. For example, a DATA step might only require 20M of memory, so even though MEMSIZE is set to 500M, SAS will use only 20M of memory. Setting MEMSIZE to 0 is not recommended except for debugging and testing purposes. To determine this optimal value, run the SAS procedure or DATA step with MEMSIZE set to 0 and the FULLSTIMER option. Note the amount of memory used by the process and then set MEMSIZE to a larger amount. 

12. Optimizing Memory Usage • SORTSIZE= Specifies the amount of memory that is available to the SORT procedure • SUMSIZE= Specifies a limit on the amount of memory that is available for data summarization procedures such as the MEANS, OLAP, REPORT, SUMMARY, SURVEYFREQ, SURVEYLOGISTIC, SURVEYMEANS, and TABULATE procedures. • MVARSIZE= Specifies the maximum size for in-memory macro variable values

13. Optimizing Memory Usage • MEXECSIZE= Specifies the maximum macro size that can be executed in memory. Use the MEXECSIZE option to control the maximum size macro that will be executed in memory as opposed to being executed from a file. The MEXECSIZE option value is the compiled size of the macro. Memory is allocated only when the macro is executed. After the macro completes, the memory is released. If memory is not available to execute the macro, an out-of-memory message is written to the SAS log. Use the MCOMPILENOTE option to write to the SAS log the size of the compiled macro. The MEMSIZE option does not affect the MEXECSIZE option. • MSYMTABMAX= Specifies the maximum amount of memory available to the macro variable symbol table. Once the maximum value is reached, additional macro variables are written out to disk. A value of 0 causes all macro symbol tables to be written to disk. If this option is set too low and the application frequently reaches the specified memory limit, then disk I/O increases. If this option is set too high (on some operating environments) and the application frequently reaches the specified memory limit, then less memory is available for the application, and CPU usage increases. • REALMEMSIZE= Indicates the amount of real memory SAS can expect to allocate. Use the REALMEMSIZE system option to optimize the performance of SAS procedures that alter their algorithms and memory usage. Setting the value of REALMEMSIZE too low might result in less than optimal performance. For better performance, set REALMEMSIZE to the amount of memory (excluding swap space) that is available to the SAS session at invocation.

14. SAS System Options Lisiting • PROC OPTIONS; Lists the current values of all SAS system options. For example on our system: MEMSIZE=67108864 (64Mb) Specifies the limit on the total amount of memory to be used by SAS (maximum was found to be 4GB) SUMSIZE=0 Upper limit for data-dependent memory usage during summarization SORTSIZE=50331648 (48Mb) Specifies the amount of memory that is available to the SORT procedure MEXECSIZE=65536 (64KB) Maximum size for a macro to execute in memory MSYMTABMAX=4194304 (4MB) Maximum amount of memory allocated for the macro table MVARSIZE=32768 (32KB) Maximum length of value of macro variable REALMEMSIZE=0 Limit on the total amount of real memory to be used by the SAS System BUFNO=1 Number of buffers for each SAS data set BUFSIZE=0 Size of buffer for page of SAS data set • Setting value of 0 for some of the options causes the maximum allowable memory to be set

15. SAS Typical Memory/CPU Usage • Creating a copy of a dataset data basic; set indat.ganon3_1;run; NOTE: There were 982 observations read from the data set GANON3_1 NOTE: The data set WORK.BASIC has 982 observations and 42068 variables. NOTE: DATA statement used (Total process time): real time 1.86 seconds user cpu time 0.56 seconds system cpu time 0.34 seconds Memory 15820k Page Faults 0 Page Reclaims 7215 Page Swaps 0 Voluntary Context Switches 1275 Involuntary Context Switches 4 Block Input Operations 0 Block Output Operations 0

16. Optimizing CPU Performance • One can Reduce CPU Time by: • Using More Memory 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. • Reducing I/O 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. • 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.

17. Optimizing CPU Performance • Other Techniques to improve CPU performance: • Storing a Compiled Program for Computation-Intensive DATA Steps 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. A stored compiled DATA step program is a SAS file that contains a DATA step program that has been compiled and then stored in a SAS data library. You can execute stored compiled programs as needed, without having to recompile them. Stored compiled DATA step programs are of member type PROGRAM. To store a compiled DATA step: DATA data-set-name(s) / PGM=stored-program-name; To load a compiled DATA step: DATA PGM=stored-program-name ;