Press Release
Analytics Added to Open Platform for Security to Enable Breakthroughs in Data Analytics Using Oracle’s Software in Silicon Technology
Redwood Shores, Calif.—Mar 8, 2016
Analytics Added to Open Platform for Security to Enable Breakthroughs in Data Analytics Using Oracle’s Software in Silicon Technology
Redwood Shores, Calif.—Mar 8, 2016
Empowering
developers to build the next generation of big data analytics platforms, Oracle
released a free and open API and developer kit for its Data Analytics
Accelerator (DAX) in SPARC processors through its Software in Silicon Developer Program.
The program also lets developers view sample use cases and program code, and to
test and validate how DAX can speed up analytics applications while test
driving Software in Silicon Technology.
“High performance data analytics are critical to a range of
key use cases like click stream data, social media sentiment, buying behavior,
and more,” said John Fowler, Executive Vice President of Systems, Oracle.
“Through our Software in Silicon Developer Program, developers can now apply
our DAX technology to a broad spectrum of previously unsolvable challenges in
the analytics space because we have integrated data analytics acceleration into
processors, enabling unprecedented data scan rates of up to 170 billion rows
per second.”
With the release of
the 32-core, 256 thread SPARC M7 processor, Oracle created a number of
Software in Silicon features by building in higher-level software functions
into processor design. One of the most exciting new capabilities introduced in
the SPARC M7 processor as part of the Software in Silicon innovations in SPARC
M7 is DAX, which delivers unprecedented analytics efficiency.
Data Analytics Accelerator on SPARC
M7
DAX adds processing capability that can run selective
functionality – Scan, Extract, Select and Translate – at incredibly fast
speeds. The SPARC M7 DAX accelerates these analytics primitives on a dedicated
physical unit separate from the standard compute cores.
Initial software development enabled DAX for Oracle Database
12c, and all the applications above it. This extends analytics acceleration to
all Oracle, ISV, and customer applications.
Large scale scan and filter operations are made trivial by
transparent use of 32 dedicated DAX co-processors in the SPARC microprocessor
which operate at memory bus speeds of up to 160 GB/s between cache and DRAM.
These accelerators, implemented for the first time on-chip for the highest
level of performance and efficiency, can now be used by developers through APIs
in Oracle Solaris 11, and applied to a variety of use cases.
As one notable example of Data Analytics Accelerator
integration into machine learning and Big Data use cases, Oracle engineers
have shown that the DAX can significantly accelerate Apache Spark, which
has become one of the most popular methods for processing large data sets.
Through this project, engineers used the DAX with Apache Spark to
take one billion rows of data in memory and filter it into a 3D cube so fast
that interactive data analytics are now possible.
SPARC M7 and DAX design advantages include:
- Industry-leading
delivered memory bandwidth: at an industry-leading 160GB/s memory
bandwidth, the SPARC M7 processor provides enough capacity to feed both
the DAX units as well as processor cores.
- DAX
offload: frees the processor cores for other processing.
- Efficient
decompressing combined with in-memory processing: putting decompression in
the DAX unit is much faster than software implementations. Designing
decompression with scanning means needless back and forth memory transfers
are avoided. Results from the DAX are entered into the CPU cache for
better CPU efficiency.
- DAX range
comparisons: many real-world database analytics queries are written to
find data transacted between certain dates, or product cost ranges, etc.
The DAX processes range comparisons at the same rate as individual
comparisons. Other processors require additional computational time for
each comparison.
- Avoiding
cache pollution: the DAX does much of its computation without needing to
store intermediate data in a cache, freeing the CPU’s cache for other
processing.
Partnerships with Developer
Community and Leading Higher Education Institutions
Oracle continues to deliver traditional processor
enhancements to improve performance of traditional workloads with more than 20
world record results over the competition. Software in Silicon can deliver
previously unattainable step function improvements required in areas such as
security and data analytics by embedding the functionalities to handle
particular algorithms on the processor with greater performance and efficiency.
Oracle has also published several use cases with code
samples to maximize developer productivity and expedite projects as well as a
detailed example of DAX integration with Apache Spark. Resources can be
accessed now via the Oracle Software
in Silicon Cloud, a freely available cloud service for developers and
researchers that provides direct access to this technology. Additionally,
Oracle is partnering with leading higher education institutions such as Brown
University, on innovative research projects with Software in Silicon.
“We are currently working on characterizing the performance
of DAX across a suite of modern in-memory data layout schemes. After completing
this study, we will work on the optimal use of DAX in accelerating interactive
data exploration and visualization with the Tupleware main-memory database
system and S-Store real-time stream processing system,” stated Ugur Centimenel,
Chairman of the Computer Science Department, Brown University. “Through these
studies, we will quantify the performance and scalability of M7 and DAX on real
workloads involving sophisticated search and machine learning over large data
sets.”
Open APIs for Oracle’s Data Analytics Accelerator are now
available for free via the Software in Silicon Cloud. Developers can join the
community now to get started on developing the next generation of big data and
analytics applications.
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