As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon Performance, and Efficient scale-out leveraging Spark architecture.
The Intel Pattern Matching Technology Library provides a range of API functions. The API functions and variables are described in this section:
Build your own DPDK-based traffic generator with a MinnowBoard Turbot or any Intel platform. OS is Ubuntu* 16.04 client with DPDK. Uses the TRex* realistic traffic generator.
This tutorial supports two hands-on labs delivered during the IEEE NFV/SDN conference in 2016. It includes comprehensive code samples and instructions to configure a single root I/O virtualization (SR-IOV) cluster and an NFV use case for Open vSwitch* with Data Plane Development Kit.
DEFLATE compression algorithms traditionally use either a dynamic or static compression table, with tradeoffs in processing time. The Intel® Intelligent Storage Acceleration Library (Intel® ISA-L) semi-dynamic compression comes close to getting the best of both worlds. Learn how.
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Storage efficiency technologies like compression and deduplication with rapid generation of cryptographic hashes are available now via the Intel® Intelligent Storage Acceleration Library (Intel® ISA-L). Code sample illustrates how to use this powerful feature.