High-Performance Analytics from SAS consists of SAS Grid Computing, SAS In-Database Analytics and SAS In-Memory Analytics. The latter component has a new addition in the form of SAS High-Performance Analytics (SAS HPA), which was announced in Dec. 2011. SAS HPA is appliance-ready software that uses hardware from database partners (Teradata or EMC Greenplum) for high performance data exploration, model development and model deployment and scoring. The whole lifecycle from descriptive statistics to variable selection and modeling to model comparison and scoring is supported within these appliances.
Faster analytic model processing performance can make a real difference. For instance a large customer was spending 5 hours to develop a single algorithm (Neural Network) model each day for new customer acquisition business case. With SAS HPA they reduced the model processing time to 3 minutes, allowing a model to be iterated every 30 minutes or so while also using multiple modeling techniques. This improved their model lift from 1.6% to 2.5% – a potentially huge value for the enterprise with a large, diversified customer base.
One of the key elements of the SAS strategy is maximizing the value of existing investments. The infrastructure is designed to support users who want to write SAS analytic programs using SAS/STAT or use the graphical user interface of SAS Enterprise Miner to develop models interactively. This means existing single-threaded SAS code that uses SAS/Access for data read and runs on the client can be updated with a single proc name change (proc logistic to proc hplogistic). This makes the code multi-threaded, aware of the distributed computing environment, uses SAS/Access for parsing the data and then runs on the DBMS appliance in an in-memory fashion. In SAS Enterprise Miner there are simply new high-performance nodes for the analytic process flow that evoke in-memory processing using the database appliance and produce identical results for the rest of the process (scoring, deployment, model management, etc).
The EMC Greenplum and Teradata appliances for SAS HPA support data storage, distributed execution of SAS code through the SAS embedded process engine as well as in-memory processing for analytics routines. As a result SAS HPA supports analytic data preparation, data exploration and model development (covering the most common data mining and predictive analytic routines). Obviously the in-memory routines also work on a SAS server too. In general SAS HPA requires data staging using data management processes. The high-performance analytics procedures leverage in-memory computations for tremendous performance gains and run alongside the database to solve complex business problems on massive amounts of data.
