3 ms·
When we think about TB of data we do not want to store that in RAM. So if you are looking at a 10TB dataset. You may be willing to have 10TB of ram to fit it i
by felipe_aramburu 10y ago
When we think about TB of data we do not want to store that in RAM. So if you are looking at a 10TB dataset. You may be willing to have 10TB of ram to fit it in but we aren't trying to solve that problem. We try to solve the problem of reducing the amount of information that has to be pulled from disk to be analyzed by having fine grain partitioning and using data skipping. We are optimizing the compression and decompression of the data to reduce file i/o bottleknecks. So your 10TB dataset can be analyzed with a 100GB of RAM. I agree that video is not very compelling. We are giving a talk at GTC that will show you something 3 orders of magnitude more interesting than that. https://gputechconf2017.smarteventscloud.com/connect/search.ww#loadSearch-searchPhrase=&searchType=session&tc=0&sortBy=dayTime&p=&i(40701)=109194 https://gputechconf2017.smarteventscloud.com/connect/search....
- ruw1090 10y agoSo blazing db mainly uses gpus to speed up compression/decompression? How do gpus help with partitioning and data skipping?
- felipe_aramburu 10y agoBlazingDB uses gpus to speed up transformations, predicates, running predicates on skip data metadata, gpu accelerated join algorithms. In terms of partitioning we use both to generate the data skipping meta data on the partitions and to reorder data around so that we can minimize the number of comparisons joins will likely require or reduce the number of partitions that need to be operated on. At large scale reorganizing information becomes very expensive the gpus allow us to crunch those numbers very quickly.