13570: Doc tweaks

* Capitalize "Keep"
* Clarify usage of cgroups
* Clarify "Available compute node types"

Arvados-DCO-1.1-Signed-off-by: Peter Amstutz <pamstutz@veritasgenetics.com>

diff --git a/doc/api/execution.html.textile.liquid b/doc/api/execution.html.textile.liquid
index f7772cb2f73f91b898c4f418a13138025abf9caa..cada9ab1b88ac226231633a8a3b43f56cf735a5b 100644 (file)
--- a/doc/api/execution.html.textile.liquid
+++ b/doc/api/execution.html.textile.liquid
@@ -24,17 +24,17 @@ h2. Container API
 
 h2(#RAM). Understanding RAM requests for containers
 
-The @runtime_constraints@ section of a container specifies working RAM (@ram@) and keep cache (@keep_cache_ram@).  If not specified, containers get a default keep cache (@container_default_keep_cache_ram@, default 256 MiB).  The total RAM requested for a container is the sum of working RAM, keep cache, and an additional RAM reservation configured by the admin (@ReserveExtraRAM@ in the dispatcher configuration, default zero).
+The @runtime_constraints@ section of a container specifies working RAM (@ram@) and Keep cache (@keep_cache_ram@).  If not specified, containers get a default Keep cache (@container_default_keep_cache_ram@, default 256 MiB).  The total RAM requested for a container is the sum of working RAM, Keep cache, and an additional RAM reservation configured by the admin (@ReserveExtraRAM@ in the dispatcher configuration, default zero).
 
-The total RAM request is used to schedule containers onto compute nodes.  On HPC systems, multiple containers may run on a multi-core node.  RAM allocation limits may be enforced using kernel controls such as cgroups.
+The total RAM request is used to schedule containers onto compute nodes.  RAM allocation limits are enforced using kernel controls such as cgroups.  A container which requests 1 GiB RAM will only be permitted to allocate up to 1 GiB of RAM, even if scheduled on a 4 GiB node.  On HPC systems, a multi-core node may run multiple containers at a time.
 
 When running on the cloud, the memory request (along with CPU and disk) is used to select (and possibly boot) an instance type with adequate resources to run the container.  Instance type RAM is derated 5% from the published specification to accomodate virtual machine, kernel and system services overhead.
 
 h3. Calculate minimum instance type RAM for a container
 
-    (RAM request + keep cache + ReserveExtraRAM) * (100/95)
+    (RAM request + Keep cache + ReserveExtraRAM) * (100/95)
 
-For example, for a 3 GiB request, default keep cache, and no extra RAM reserved:
+For example, for a 3 GiB request, default Keep cache, and no extra RAM reserved:
 
     (3072 + 256) * 1.0526 = 3494 MiB
 
@@ -42,9 +42,9 @@ To run this container, the instance type must have a published RAM size of at le
 
 h3. Calculate the maximum requestable RAM for an instance type
 
-    (Instance type RAM * (95/100)) - keep cache - ReserveExtraRAM
+    (Instance type RAM * (95/100)) - Keep cache - ReserveExtraRAM
 
-For example, for a 3.75 GiB node, default keep cache, and no extra RAM reserved:
+For example, for a 3.75 GiB node, default Keep cache, and no extra RAM reserved:
 
     (3840 * 0.95) - 256 = 3392 MiB
 

diff --git a/doc/user/cwl/cwl-style.html.textile.liquid b/doc/user/cwl/cwl-style.html.textile.liquid
index 5d84f1d512c30386465e1c690fdb79aecf8a574b..6b5147ddcbda8260f9431fa8ec201bc759a8aa41 100644 (file)
--- a/doc/user/cwl/cwl-style.html.textile.liquid
+++ b/doc/user/cwl/cwl-style.html.textile.liquid
@@ -113,7 +113,7 @@ steps:
         tmpdirMin: 90000
 </pre>
 
-* Available compute nodes vary over time and across different cloud providers, so try to limit the RAM requirement to what the program actually needs.  However, if you need to target a specific compute node type, see this discussion on "calculating RAM request and choosing instance type for containers.":{{site.baseurl}}/api/execution.html#RAM
+* Available compute nodes types vary over time and across different cloud providers, so try to limit the RAM requirement to what the program actually needs.  However, if you need to target a specific compute node type, see this discussion on "calculating RAM request and choosing instance type for containers.":{{site.baseurl}}/api/execution.html#RAM
 
 * Instead of scattering separate steps, prefer to scatter over a subworkflow.