IBM z13s, Техническое руководство

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Chapter 12. Performance
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processor hardware. These past techniques approximated the hardware characteristics that
were not available through software performance reporting tools.
Beginning with the z10 processor, the hardware characteristics can now be measured by
using CPU MF (SMF 113) counters data. A production workload can now be matched to an
LSPR workload category through these hardware characteristics. For more information about
RNI, see 12.5, “LSPR workload categories based on relative nest intensity” on page 443.
The AVERAGE RNI LSPR workload is intended to match most client workloads. When no
other data is available, use it for capacity analysis.
Direct access storage device (DASD) I/O rate was used for many years to separate workloads
into two categories:
򐂰 Those whose DASD I/O per MSU (adjusted) is <30 (or DASD I/O per Peripheral
Component Interconnect (PCI)
is <5)
򐂰
Those higher than these values
Most production workloads fell into the “low I/O” category, and a LoIO-mix workload was used
to represent them. Using the same I/O test, these workloads now use the AVERAGE RNI
LSPR workload. Workloads with higher I/O rates can use the HIGH RNI workload or the
AVG-HIGH RNI workload that is included with IBM zPCR. Low-Average and Average-High
categories allow better granularity for workload characterization.
For z10 and newer processors, the CPU MF data can be used to provide an extra hint as to
workload selection. When available, this data allows the RNI for a production workload to be
calculated. By using the RNI and another factor from CPU MF, the L1MP (percentage of data
and instruction references that miss the L1 cache), a workload can be classified as LOW,
AVERAGE, or HIGH RNI. This classification and resulting hit are automated in the zPCR tool.
It is preferable to use zPCR for capacity sizing.
12.7 Workload performance variation
Performance variability from application to application is expected. This variation is similar to
that seen on the zBC12 and z114. This variability can be observed in certain ways. The range
of performance ratings across the individual workloads is likely to have a spread, but not as
large as with the z10 BC.
The memory and cache designs affect various workloads in many ways. All workloads are
improved, with cache-intensive loads benefiting the most. When comparing moving from
z9 BC to z10 BC with moving from z10 BC to z114 or from z114 to zBC12, it is likely that the
relative benefits per workload will vary. Those workloads that benefited more than the
average when moving from z9 BC to z10 BC will benefit less than the average when moving
from z10 BC to z114. Nevertheless, the workload variability for moving from zBC12 to z13s is
expected to be less than the last few upgrades.
The effect of this variability is increased deviations of workloads from single-number
metric-based factors, such as millions of instructions per second (MIPS), MSUs, and CPU
time charge-back algorithms.
Experience demonstrates that z Systems servers can be run at up to 100% utilization levels,
sustained. However, most clients prefer to leave a bit of room and run at 90% or slightly
under. For any capacity comparison exercise, using a single metric, such as MIPS or MSU, is
not a valid method. When deciding the number of processors and the uniprocessor capacity,