MPP on HANA, Exadata, Teradata, and Netezza

6 May… There is a good summary of this post and on the comments here.  – Rob

17 April… A single unit of parallelism is a core plus a thread/process to feed it instructions plus a feed of data. The only exception is when the core uses hyper-threading… in which case 2 instructions can execute more-or-less at the same time… then a core provides 2 units of parallelism. All of the other stuff: many threads per core and many data shards/slices per thread are just techniques to keep the core fed. – Rob

16 April… I edited this to correct my loose use of the word “shard”. A shard is a physical slice of data and I was using it to represent a unit of parallelism. – Rob

I made the observation in this post that there is some inefficiency in an architecture that builds parallel streams that communicate on a single node across operating system boundaries… and these inefficiencies can limit the number of parallel streams that can be deployed. Greenplum, for example, no longer recommends deploying a segment instance per core on a single node and as a result not all of the available CPU can be applied to each query.

This blog will outline some other interesting limits on the level of parallelism in several products and on the definition of Massively Parallel Processing (MPP). Note that the level of parallelism is directly associated with performance.

On HANA a thread is built for each core… including a thread for each hyper-thread. As a result HANA will split and process data with 80 units of parallelism on a high-end 40-core Intel server.

Exadata deploys 12 cores per cell/node in the storage subsystem. They deploy 12 disk drives per node. I cannot see it clearly documented how many threads they deploy per disk… but it could not be more than 24 units of parallelism if they use hyper-threading of some sort. It may well be that there are only 12 units of parallelism per node (see here).

Updated April 16: Netezza deploys 8 “slices” per S-Blade… 8 units of parallelism… one for each FPGA core in the Twin times four (2X4) Twinfin architecture (see here). The next generation Netezza Striper will have 16-way parallelism per node with 16 Intel cores and 16 FPGA cores…

Updated April 17: Teradata uses hyper-threading (see here)… so that they will deploy 24 units of parallelism per node on an EDW 6700C (2X6X2) and  32 units of parallelism per node on an EDW 6700H (2X8X2).

You can see the different definitions of the word “massive” in these various parallel processing systems.

Note that the next generation of Xeon processors coming out later this year will have 8X15 processors or 120 cores on a fat node:

  • This will provide HANA with the ability to deploy 240 units of parallelism per node.
  • Netezza will have to find a way to scale up the FPGA cores per S-Blade to keep up. TwinFin will have to become QuadFin or DozenFin. It became HexadecaFin… see above. – Rob
  • Exadata will have to put 120 SSD/disk drive combos in each node instead of 12 if they want to maintain the same parallelism-to-disk ratio with 120 units of parallelism.
  • Teradata will have to find a way to get more I/O bandwidth on the problem if they want to deploy nodes with 120+ units of parallelism per node.

Most likely all but HANA will deploy more nodes with a smaller number of cores and pay the price of more servers, more power, more floor space, and inefficient inter-node network communications.

So stay tuned…

Co-processing and Exadata

In my first blog (here) I discussed the implications of using co-processors to offload CPU. The point was that with multi-core processors it made more sense to add generalized processing hardware that could be applied to all parts of the query process than to add specialized processors that dealt with only part of the problem.

Kevin Closson has produced two videos that critically evaluate the architecture of Exadata and I strongly suggest that you view them here before you go on with this post… They are enlightening, irreverent, and make the long post I’ve been drafting on Exadata lightweight and unnecessary.

If you have seen Kevin’s post you understand that Exadata is asymmetric and unbalanced. But his post extends and generalizes my discussion of co-processing in a nice way. Co-processing is asymmetric by definition. The co-processor is not busy after it has executed on its part of the problem.

In fact, Oracle has approximately mirrored the Netezza architecture with Exadata but used commercial processors instead of FPGAs to offload I/O and predicate processing. The result is the same in both cases… underutilized processing capability. The difference is that Netezza wastes some power on relatively inexpensive FPGA processors while Exadata wastes general and expensive CPU resources that might actually be applied usefully elsewhere. And Netezza splits the processing within a shared-nothing architecture while Exadata mixes architectures adding to the inefficiency.