jnturton commented on issue #2421:
URL: https://github.com/apache/drill/issues/2421#issuecomment-1005676173


   A few, informational notes from me.  First, I wondered how it could be that 
benchmark-busting Impala manages to load SIMD registers from a row oriented 
memory layout then found the following [in 
here](http://cidrdb.org/cidr2015/Papers/CIDR15_Paper28.pdf).
   
   > We are also considering switching to a columnar canonical in-memory format 
for data that needs to be materialized during query processing, in order to 
take advantage of SIMD instructions 
   
   I wonder whether they switched?  I suppose I need only go over to 
apache/impala to find out.  I can't speak for anyone else but some of my 
important Drill workloads actually have been bulk floating point arithmetic 
(statistics, probabilities) where you do get a major boost from SIMD.  That 
said, I probably did even more work doing filtering, sorting and aggregating 
the same data in Drill before it was ready for arithmetic.
   
   Second, if you did want to do SIMD properly in Java (i.e. not praying for 
auto-vectorisation) then are you looking at JEP 338 and do [you have some 
vector types from JDK dictated to you](https://openjdk.java.net/jeps/338)?  
Would these vectors work with our direct memory and Netty-based code?
   
   Third, would our direct memory code remain unchanged if we replace 
ValueVectors or would we want to start accessing direct memory through [JEP 
393](https://openjdk.java.net/jeps/393).  Or... are [the high tech garbage 
collectors in recent 
JVMs](https://blogs.oracle.com/javamagazine/post/understanding-the-jdks-new-superfast-garbage-collectors)
 so good that we would laugh off the burden direct memory and its management 
entirely?
   
   Re. SIMD, I know there are positions like "Forget SIMD" that have been 
stated, but I wanted to note this stuff down for us anyway.
   


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