Friday, June 16, 2017

The Machine and BigData

HP’s „The Machine“ (1) project is in my eyes the most advanced in the IT world with the simple goal to rethink the entire computer design. And the plan is ambitious – the first edge devices shall be ready in 2018, industrialized series in 2020.

Will “The Machine” really revolutionize an entire industry mostly influenced by IBM? Let’s say it could and probably will with a high percentage of success.
Based on the idea of Memristor (2) the project uses memory based technology to store data. Nothing new here. New is the non-volatile usage. Data, stored in an Memristor, persists unless the storing bit gets cleaned and new aligned. Now, NVRRAM (non-volatile resistive RAM) it’s faster as volatile DDR4 modules (which they use at the moment until Western Digital can deliver NVRRAM modules) and factor 100x faster than current state-of-the-art SSD based technologies. The newest prototype has 40 nodes with approx. 160 TB DDR4-RAM and 1,280 Cores connected with X1 PM’s (Photonic Modules). Means: pretty fast. Anyhow, just follow the appendix (1) to get more interesting engineering facts.
The most important consideration is the pure permanent all-integrated storage itself. The part of attached storage (like HDFS, GFS, Ceph) would simply disappear and directly merge with the computation layer. The principle “local data first” will surely be a part of any fine-tuning approach but with the high density of storage that will not really matter. All pieces of computation will be at the same place (cache, volatile and permanent storage combined with fast caching) and work as one homogenous entity which can hold every state of every piece of data during the whole computation lifecycle.
I just want to consider the changing fundamentals of that idea and what that would mean to data processing. The first big difference – a trinity memristor can store 10 bits instead of 8 today. That means simply a 3 times higher data storage density than today. Additionally, the highly volatile cache a CPU uses during the calculation process will be stored permanently which allows following processes to reuse the pre-calculated subsets and that would speed up any calculation dramatically. As for example in pattern detection algorithms like MCMC (3) could highly benefit simply by picking up the already calculated subset and use it in a new chain which would revolutionize data intelligence in terms of speed and tree generation. I think thats an huge step into the AI world - ultrafast learning algorithms helping the mankind to operate high sensitive environments like deep- space flights, connected cars, CEP networks or decentral power grids.