Oracle accelerates analytics applications with new API and developer kit


Oracle is after developers with a new tool, unveiled today. The company just released a free and open API and developer kit, made for its Data Analytics Accelerator (DAX) in SPARC processors, allowing for fast and high performance data analytics and new processing capabilities.

This new 32-core, 256 thread SPARC M7 processor, used along with DAX, adds new processing capabilities, designed for efficiency and swiftness. It can run selective functionality, such as scan, extract, and translate, at new speeds by using a dedicated physical unit that’s kept separate from the standard compute cores; basically, it delegates the workloads to new units, allowing everything to run at maximum efficiency by eliminating the need to multi-task.

The SPARC M7 and DAX design provides several other advantages, such as 160GB/s memory bandwidth. It includes decompressing with the in-memory processing, which works faster than software implementations and skips constant back-and-forth memory transfers; the results are then used to provide better CPU efficiency.

Speaking of the CPU cache, the DAX is designed to do most of its computations without storing intermediate data in the cache. That means that the CPU is free for other processes, so it can keep things moving quickly.

The new API and developer kit is released through Oracle’s Software in Silicon Developer Program. The program provides new step function improvements for areas that were previously lacking, such as data analytics and security, by integrating the functionality directly into the processors.

To demonstrate the DAX’s efficiency, Oracle is including sample use cases and program code through Software in Silicon, so users can test it for themselves. Resources can be found through the Oracle Software in Silicon Cloud service, which gives developers and researchers access to the technology.

Open APIs for Oracle’s Data Analytics Accelerator are available now from the Software in Silicon Cloud. For more information, visit

Photo by Peter Kaminski