
5
Table 4. DL980/P2000 supported Expansion Slot Configurations
Standard Main I/O with 5 Gen2 slots: (3) x4 PCI-Express; (2) x8 PCI-Express
PCIe Option with 6 slots: (1) x4 Gen1 PCI-Express; (1) x4 Gen2 PCI-Express (4) x8 Gen2 PCI-Express
Low Profile Expansion Option with 5 Gen2 slots; (1) x4 PCI-Express; (4) x8 PCI-Express
Recommended configurations
The detailed information for these recommended configurations includes the server, number and type of processors,
memory, internal and external storage. The configurations were evaluated for a given workload: concurrent users,
I/O throughput, and database size. The configurations are based on testing done in HP’s Oracle integration lab
using HP DL980 G7 ProLiant servers running Red Hat Enterprise Linux (RHEL) and Oracle 11gR2 Enterprise Edition.
The configurations were determined based on the following utilization criteria:
CPU utilization up to 90% at the target workload
Process Global Area (PGA) cache hit ratio 99% or higher with very low counts of 1 or multipass PGA executions
Disk I/O activity reflects a read/write ratio of approximately 95/5
The focus of this reference configuration document is the data mart or data warehouse type configuration, based on
the ProLiant DL980 G7.
Table 5. Recommended Configuration metrics and storage type options
(288) 146GB 6G
15K SAS SFF
Table 6 outlines the server configuration details for this platform.
Table 6. ProLiant DL980 G7 configuration details
(8) Ten-Core Intel Xeon Processors Model E7-4870 (30MB Cache, 2.4GHz, 130W, 6.4 GT/s QPI)
(10) x8, (2) x4, all FL/FH
(4) HP 146GB 6G SAS 15K rpm SFF (2.5-inch) DP HDD
HP Smart Array P410i/1GB FBWC
(12) HP 82Q PCI-e 8Gb/s FC Dual Port HBA (QLogic)
One HP NC375i Quad port Gigabit Server Adapters (four ports total)
1024GB PC3-10600R-9 expandable to 2TB
Note that the recommended 1024GB is the minimum RAM for this reference configuration. Generally, for Oracle
Data Warehousing environments, as workload demands grow, increasing RAM provides performance benefits
(requires RHEL 6.x). If the workload consists of a large number of small-to-medium sized queries hitting the same data
(for example, last week’s sales query), performance and throughput can be increased by caching results in memory.
Additional network capacity can be added to support larger numbers of client connections however most DW
workloads have very few client connections.
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