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--- cluster:174 [2018/08/27 18:23] – hmeij07
+++ cluster:174 [2018/08/28 12:38] (current) – hmeij07
@@ Line 4: / Line 4: @@
 ==== SQL on GPU ====
-MapD built the first ever open source SQL engine to harness GPU computing for analytics. Designed for maximum performance, the MapD SQL engine dynamically compiles SQL to run across multiple GPUs and CPUs.
+MapD built the first ever open source SQL engine to harness GPU computing for analytics. Designed for maximum performance, the MapD SQL engine dynamically compiles SQL to run across multiple GPUs and CPUs. Massively parallel database servers.
   * http://www.mapd.com
@@ Line 20: / Line 20: @@
   - Choose the flights_2008_7M table as the data source.
   - Click X Axis +Add Measure.
-  - Choose depdelay.
+  - Choose depdelay. (departure delay)
   - Click Y Axis +Add Measure.
-  - Choose arrdelay.
+  - Choose arrdelay. (arrival delay)
-million records, amazingly fast I mean  like < 50 ms. There are pie, bar, stacked bar, bubble etc charts and point maps or chloropleth maps. A SQL editor.  And I do not even observe a blip on gpu utilization.
+million records, amazingly fast. I mean  like < 50 ms (milliseconds). There are pie, bar, stacked bar, bubble etc charts and point maps or chloropleth maps. A SQL editor.  And I do not even observe a blip on gpu utilization. Each of these K20 nodes still has 7 drive bays available which would allow for a large database storage platform if we had a mapd project.
 {{ :cluster:dep-arr-scatterplot.png?nolink&400 |}}
@@ Line 32: / Line 32: @@
   * https://www.mapd.com/docs/latest/3_mapdql.html
-  * can be scripted too
+  * can be scripted using mapdql
 <code>
@@ Line 99: / Line 99: @@
 </code>
+  * https://www.mapd.com/docs/latest/4_centos7-yum-gpu-ce-recipe.html
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