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SAS Companion for the OS/390 Environment

Optimizing I/O

To optimize SAS input and output in the OS/390 environment, consider the following suggestions.

Put catalogs and data sets into separate libraries, using the optimal block size for each

The physical block size (BLKSIZE= ) of a SAS bound data library determines both the minimum page size and the minimum unit of space allocation for the library. The 6KB default is relatively efficient across a range of device types, and it leads to lower memory requirements for catalog buffers. However, when you use the 6KB default, more DASD space is needed to hold a given amount of data because smaller blocks lead to capacity losses. In one test case on a 3380, an MXG daily PDB required 8% more tracks when it was stored in 6KB physical blocks instead of in half-track blocks.

Because the optimal block sizes for SAS catalogs and SAS data sets are not necessarily the same, consider putting catalogs and data sets into separate libraries. For catalog libraries, 6KB is a good general physical block size on any device. For data sets, choose either a full-track or half-track block size, depending on whether the data library is stored on a device that supports full-track blocks.

Use the optimal buffer size and buffer number for your data

When a member of a direct access bound library is processed sequentially, the values of the SAS system options BUFSIZE= and BUFNO= are the primary factors that affect I/O performance. When a SAS data library is processed sequentially, the unit of I/O transfer, in bytes, is equal to BUFSIZE*BUFNO.

BUFSIZE is the page size for the data set. You specify BUFSIZE only when you are creating an output data set; it then becomes a permanent attribute of the data set. BUFNO is the number of page buffers to allocate for the data set. For random access, BUFNO page buffers form a least-recently-used buffer pool that can significantly reduce physical I/O depending on the data-access pattern. Of course, the greater the number of page buffers, the more memory is required. Page buffers are stored above the 16MB line.

Note that the product of BUFNO and BUFSIZE is the important factor in sequential I/O performance rather than the specific value of either option. As BUFNO is increased, there is a marked reduction in I/O time and I/O count, although the cost of buffer storage increases. As a result, elapsed times can be significantly reduced. For example, when BUFNO=16 and BUFSIZE=6144, the results are very similar to BUFNO=4 and BUFSIZE=23040. Moreover, when BLKSIZE=6144, specifying BUFSIZE=24K yields performance results that are very close to those of BLKSIZE=23040 and BUFSIZE=23040.

Here are some guidelines for determining the optimal BUFSIZE and BUFNO values for your data:

Determine whether you should compress your data

Compressing data reduces I/O and disk space but increases CPU time. Therefore, whether or not data compression is worthwhile to you depends on the resource cost-allocation policy in your data center. Often your decision must be based on which resource is more valuable or more limited, DASD space or CPU time.

You can use the portable SAS system option COMPRESS= to compress all data sets that are created during a SAS session. Or, use the SAS data set option COMPRESS= to compress an individual data set. Data sets that contain many long character variables generally are excellent candidates for compression.

The following tables illustrate the results of compressing SAS data sets under OS/390. In both cases, PROC COPY was used to copy data from an uncompressed source data set into uncompressed and compressed result data sets, using the system option values COMPRESS=NO and COMPRESS=YES, respectively.(footnote 1) In the following tables, the CPU row shows how much time was used by an IBM 3090-400S to copy the data, and the SPACE values show how much storage (in megabytes) was used.

For the first table, the source data set was a problem-tracking data set. This data set contained mostly long, character data values, which often contained many trailing blanks.

Compressed Data Comparison 1
Resource Uncompressed Compressed Change
CPU 4.27 sec 27.46 sec +23.19 sec
Space 235 MB 54 MB -181 MB

For the preceding table, the CPU cost per megabyte is 0.1 seconds.

For the next table, the source data set contained mostly numeric data from an MICS performance database. The results were again good, although not as good as when mostly character data were compressed.

Compressed Data Comparison 2
Resource Uncompressed Compressed Change
CPU 1.17 sec 14.68 sec +13.51 sec
Space 52 MB 39 MB -13 MB

For the preceding table, the CPU cost per megabyte is 1 second.

For more information about the pros and cons of compressing SAS data, see SAS Programming Tips: A Guide to Efficient SAS Processing.

Consider using SAS software compression in addition to hardware compression

Some storage devices perform hardware data compression dynamically. Because this hardware compression is always performed, you may decide not to enable the SAS COMPRESS option when you are using these devices. However, if DASD space charges are a significant portion of your total bill for information services, you might benefit by using SAS software compression in addition to hardware compression. The hardware compression is transparent to the operating system; this means that if you use hardware compression only, space charges are assessed for uncompressed storage.

Consider placing SAS data libraries in hiperspaces

One effective method of avoiding I/O operations is to use the SAS System's HIPERSPACE engine option. This option that is specific to OS/390 enables you to place a SAS data library in a hiperspace instead of on disk.

A hiperspace overrides the specified physical data library. This means that the physical data library on disk is neither opened nor closed, and data are neither written to nor read from the data library. All data access is done in the hiperspace.

Because the specified data library is not written to, it should be a temporary data set. The only time the specified data library is used is when it is a DIV (Data-In-Virtual) data set, as explained in Hiperspace Libraries and DIV Data Sets.

The HIPERSPACE option is processed after the normal allocation processing is complete. The requested data set is allocated first, as it is with any LIBNAME statement or LIBNAME function. It is deallocated when you issue a LIBNAME CLEAR statement or when you terminate the SAS session. The hiperspace, in effect, overrides the data set.

Examples of Using the HIPERSPACE Engine Option

Here is an example of using the HIPERSPACE engine option to place a data library in a hiperspace: 

libname mylib '&templib' hip;

(HIP is an alias for the HIPERSPACE option.)

For a data library that was allocated externally with a DD statement or a TSO ALLOCATE command, specify a null data set name in quotes. For example, the following LIBNAME statement places a library that was allocated with the DDname "X" in a hiperspace: 

libname x '' hip;

To place the WORK data library in a hiperspace, specify the HSWORK SAS system option when you invoke SAS. See HSWORK for a description of the HSWORK option.

Controlling the Size of a Hiperspace Library

Just as you use the SPACE=, DISP=, and BLKSIZE= engine options to allocate a physical data set, you use the HSLXTNTS=, HSMAXPGS=, and HSMAXSPC= SAS system options to control the size of hiperspace libraries. These options are described in HSMAXPGS=.

The CONTENTS procedure reports all hiperspace libraries as residing on a 3380 device with a block size of 4096. These attributes may differ from the attributes of the physical data set.

Hiperspace Libraries and DIV Data Sets

The only time the allocated physical data set is actually used with the HIPERSPACE option is if the data set is a Data-In-Virtual (DIV) data set. (footnote 2) An empty DIV data set can be initialized by allocating it to a hiperspace library. An existing DIV data set that contains data can be read or updated, or both.

You can use the HSSAVE SAS system option to control whether the DIV pages are updated each time your application writes to the hiperspace or only when the data library is closed. See HSSAVE for more information about this option.

Performance Considerations for Hiperspace SAS Data Sets

The major factor that affects hiperspace performance is the amount of expanded storage on your system. The best candidates for using hiperspace are jobs that execute on a system that has plenty of expanded storage. If expanded storage on your system is constrained, the hiperspaces are moved to auxiliary storage. This eliminates much of the potential benefit of using the hiperspaces.

For more information about using hiperspaces under OS/390, see the documentation for your operating environment. Also see Tuning SAS Applications in the MVS Environment.

Consider designating temporary SAS libraries as virtual I/O data sets

Treating data libraries as "virtual I/O" data sets is another effective method of avoiding I/O operations. This method works well with any temporary SAS data library--especially WORK. To use this method, specify UNIT=VIO as an engine option in the LIBNAME statement or LIBNAME function.

The VIO method is always effective for small libraries (<10 cylinders). If your installation has set up your system to allow VIO to go to expanded storage, then VIO can also be effective for large temporary libraries (up to several hundred cylinders). Using VIO is most practical during evening and night shifts when the demands on expanded storage and on the paging subsystem are typically light.

The VIO method can also save disk space because it is an effective way of putting large paging data sets to double use. During the day, these data sets can be used for their normal function of paging and swapping back storage; during the night, they become a form of temporary scratch space.

FOOTNOTE 1:  When you use PROC COPY to compress a data set, you must include the NOCLONE option in your PROC statement. Otherwise, PROC COPY propagates all the attributes of the source data set, including its compression status. [arrow]

FOOTNOTE 2:  DIV data sets are also referred to as VSAM linear data sets. [arrow]


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