4/4/2023 0 Comments Oracle inform![]() ![]() ![]() There is a newer version of this script available from MOS (Doc ID 401749.1) which includes these kernel versions also. I've subsequently added support for 3.10 and 4.1. Thanks to Bjoern Rost for pointing out the issue when using the script against UEK3 and the suggested fix. *) echo "Unrecognized kernel version $KERN. '2.4') HUGETLB_POOL=`echo "$NUM_PG*$HPG_SZ/1024" | bc -q` Įcho "Recommended setting: vm.hugetlb_pool = $HUGETLB_POOL" # is an Oracle RDBMS shared memory segment or not. # segments available when the script is run, no matter it # Note: This script does calculation for all shared memory # recommended HugePages/HugeTLB configuration # Linux bash script to compute values for the The default HugePage size is 2MB on Oracle Linux 5.x and as you can see from the output below, by default no HugePages are defined.ĭepending on the size of your SGA, you may wish to increase the value of Hugepagesize to 1G.Ĭreate a file called "hugepages_setting.sh" with the following contents. Run the following command to determine the current HugePage usage. ![]() Instead, Automatic Shared Memory Management and Automatic PGA Management should be used as they are compatible with HugePages. To determine how much memory you are currently using to support the page table, run the following command at a time when the server is under normal/heavy load.Īutomatic Memory Management (AMM) is not compatible with Linux HugePages, so apart from ASM instances and small unimportant databases, you will probably have no need for AMM on a real database running on Linux. It is typically the combination of a large SGA and lots database connections that leads to problems. Just because you have a large SGA, it doesn't automatically mean you will have a problem if you don't use HugePages. The savings in memory and the effort of page management make HugePages pretty much mandatory for Oracle 11g systems running on x86-64 architectures. In addition to these changes, the memory associated with HugePages can not be swapped out, which forces the SGA to stay memory resident. Using HugePages, the page size is increased to 2MB (configurable to 1G if supported by the hardware), thereby reducing the total number of pages to be managed by the kernel and therefore reducing the amount of memory required to hold the page table in memory. Without HugePages, the memory of the SGA is divided into 4K pages, which have to be managed by the Linux kernel. Disabling Transparent HugePages (RHEL6/OL6 and RHEL7/OL7)įor large SGA sizes, HugePages can give substantial benefits in virtual memory management.Force Oracle to use HugePages (USE_LARGE_PAGES).Furthermore, we publish the Mahjong environment and an offline RL dataset as a benchmark to facilitate future research on oracle guiding ().Home » Articles » Linux » Here Configuring HugePages for Oracle on Linux (x86-64) We empirically demonstrate the effectiveness of VLOG in online and offline RL domains with tasks ranging from video games to a challenging tile-based game Mahjong. VLOG is featured with preferable properties such as its robust and promising performance and its versatility to incorporate with any value-based DRL algorithm. Our key contribution is to propose a general learning framework referred to as variational latent oracle guiding (VLOG) for DRL. In this work, we study such problems based on Bayesian theory and derive an objective to leverage oracle observation in RL using variational methods. For example, human experts will look at the replay after a Poker game, in which they can check the opponents' hands to improve their estimation of the opponents' hands from the visible information during playing. Despite recent successes of deep reinforcement learning (RL) in various decision making problems, an important but under-explored aspect is how to leverage oracle observation (the information that is invisible during online decision making, but is available during offline training) to facilitate learning. Abstract: How to make intelligent decisions is a central problem in machine learning and artificial intelligence. ![]()
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