Oracle International Corporation

Microsoft Technology Licensing, LLC

Victor Chen

Shasank Kisan Chavan

Amit Ganesh

Krishnaram Kenthapadi

Vineet Marwah

Jesse Kamp

Ganesh Venkataraman

Yi Li

Anindya Chandra Patthak

Phumpong Watanaprakornkul

Methods and apparatuses for determining set-membership using Single Instruction Multiple Data ('SIMD') architecture are presented herein. Specifically, methods and apparatuses are discussed for compressing or packing, in parallel, multiple fixed-length values into a stream of multiple variable-length values using SIMD architecture.

Efficient hardware instructions for single instruction multiple data processors: fast fixed-length value compression

A method, apparatus, and system for OZIP, a data compression and decompression codec, is provided. OZIP utilizes a fixed size static dictionary, which may be generated from a random sampling of input data to be compressed. Compression by direct token encoding to the static dictionary streamlines the encoding and avoids expensive conditional branching, facilitating hardware implementation and high parallelism. By bounding token definition sizes and static dictionary sizes to hardware architecture constraints such as word size or processor cache size, hardware implementation can be made fast and cost effective. For example, decompression may be accelerated by using SIMD instruction processor extensions. A highly granular block mapping in optional stored metadata allows compressed data to be accessed quickly at random, bypassing the processing overhead of dynamic dictionaries. Thus, OZIP can support low latency random data access for highly random workloads, such as for OLTP systems.

OZIP compression and decompression

The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size. Items are distributed based on similarities identified with matrix factorization. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.

Member clustering with equi-sized partitions

The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size, items are distributed based on similarities identified with matrix factorization. A matrix used in the matrix factorization is customized based on the recommendation type. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.

Efficient recommendation services

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Foster City, CA, United States of America

Victor Chen