ScaleOut Grid Computing Edition™
ScaleOut StateServer® Grid Computing Edition combines a scalable, distributed in-memory data grid (IMDG) with powerful computational features for business intelligence (BI), financial modeling, and other high performance data analysis. Advanced capabilities for rapidly searching grid-based data and for quickly developing "map/reduce" applications make it perfect for fast, scalable analysis of both enterprise data as well as public data sets. Also included is the ScaleOut Management Pack™ which provides comprehensive tools for observing, managing, and preserving grid-based data.
Fast Data Access
Grid computing has rapidly grown in popularity to address the needs of complex computational problems which require scalable performance that only compute grids can provide. Financial services and other data-intensive industries routinely demand real time processing and overnight batch analysis of large data sets. By storing fast-changing data in-memory in ScaleOut StateServer®'s distributed data grid using industry-leading distributed caching, these applications can dramatically reduce access latencies, avoid bottlenecks, and achieve peak performance. The distributed in-memory data grid also lets applications immediately share data across compute nodes without the need for message passing; this simplifies program structure and shortens design cycles. These combined benefits make Grid Computing Edition a powerful data access platform for a wide range of high performance computing (HPC) applications.
Powerful Parallel Query
ScaleOut StateServer Grid Computing Edition is designed to maximize application performance and simplify design. Grid computing capabilities let applications easily access and operate in parallel on stored data across the compute grid. Applications can perform parallel queries to rapidly search grid-based data for selected objects based on metadata associated with stored objects. For example, cached stock price objects could be searched to determine which stocks were associated with a specified industry group. Employing patent-pending, parallel search and merging algorithms, ScaleOut StateServer Grid Computing Edition provides the fastest possible parallel query across all hosts within the distributed data grid.
Parallel Data Analysis
ScaleOut StateServer Grid Computing Edition takes data grid support to the next level by enabling applications to execute user-defined methods in parallel on a selected set of objects and then combine the results using user-defined merge algorithms. For example, a set of portfolio objects could be analyzed in parallel with the results merged into a single report. 
By simplifying application design and reducing data motion, this "map/reduce" capability works in concert with the grid's job scheduler to further extend the distributed in-memory data grid's power to accelerate the performance of HPC applications.
Unlike traditional map/reduce implementations which process large, file-based data sets, Grid Computing Edition stores data sets in a scalable, in-memory distributed cache within the distributed data grid. Grid Computing Edition automatically maps the user's "map" operation across both multiple cores within each server and multiple servers within the grid, and the map operation then executes in parallel on a queried set of cached objects. GCE's runtime system maximizes parallelism and minimizes data motion within the grid, In addition, GCE performs high-speed, parallel execution of the user's "reduce" operation across all grid nodes. The result is very high performance for the complete map-reduce operation.
Since the Grid Computing Edition includes the ScaleOut Management Pack with its parallel backup and restore capability, the contents of a distributed in-memory data grid can be captured as a snapshot at any point in time and later restored and analyzed using in-memory map/reduce algorithms.
Importantly, the user does not need to write special code to harness the power of the grid's infrastructure. Grid Computing Edition's APIs enable map/reduce calculations to be written as in-memory methods that avoid explicit cache accesses, and it provides automatic parallel speedup. In addition, the need for traditional HPC message-passing within the user's application is eliminated. Now, developers can obtain parallel performance without becoming a parallel processing expert.
Powerful Management of Cached Data
Included with Grid Computing Edition, the ScaleOut Management Pack adds important functions that extend your ability to manage, analyze, and protect data stored in the distributed in-memory data grid. The Management Pack contains two components: an object browser for visually browsing and managing objects stored in the IMDG and a parallel backup and restore feature for quickly archiving its contents in the file system. These tools are designed to automatically scale their performance so that they can efficiently handle very large data sets.
See the Product Matrix for a quick comparison of the features included in each version of our products.
