Oracle International Corporation

Oracle America, Inc.

Salesforce, Inc.

Kevin Moore

Brian Gold

Aarti Basant

Nipun Agarwal

David Dice

Sam Idicula

Eric Sedlar

Mark S Moir

Vitaly Gordon

Shubha Umesh Nabar

Sarah Aerni

Leah McGuire

Eric Wayman

Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.

Identification and application of hyperparameters for machine learning

A method for providing optimized data representation of relations for in-memory database query processing is disclosed. The method seeks to optimize the use of the available memory by encoding relations on which the in-memory database query processing is performed and by employing auxiliary structures to maintain performance. Relations are encoded based on data patterns in one or more attribute-columns of the relation and the encoding that is selected is suited to a particular type of data in the column. Members of a set of auxiliary structures are selected based on the benefit the structure can provide and the cost of the structure in terms of the amount of memory used. Encoding of the relations is performed in real-time while query processing occurs, using locks to eliminate conflicts between the query processing and encoding.

Optimal data representation and auxiliary structures for in-memory database query processing

Techniques for Aligned Run-Length Encoding (ARLE) are described. ARLE is an encoding scheme that transforms sets of same-valued consecutive rows into one or more runs, while enforcing boundaries between the runs at set intervals (e.g. every predetermined number of rows). Consecutive rows that contain the same value, but which cross one or more interval boundaries, are encoded as multiple runs that are divided along those interval boundaries. According to one technique, a database server accelerates query processing by setting the interval size to the word size of the processor performing the predicate comparisons. According to another technique, a database server accelerates row lookup by maintaining an offset array that stores the run offsets into the ARLE data of the run that begins each interval.

Techniques for aligned run-length encoding

Multi-threaded, transactional memory systems may allow concurrent execution of critical sections as speculative transactions. These transactions may abort due to contention among threads. Hardware feedback mechanisms may detect information about aborts and provide that information to software, hardware, or hybrid software/hardware contention management mechanisms. For example, they may detect occurrences of transactional aborts or conditions that may result in transactional aborts, and may update local readable registers or other storage entities (e.g., performance counters) with relevant contention information. This information may include identifying data (e.g., information outlining abort relationships between the processor and other specific physical or logical processors) and/or tallied data (e.g., values of event counters reflecting the number of aborted attempts by the current thread or the resources consumed by those attempts). This contention information may be accessible by contention management mechanisms to inform contention management decisions (e.g. whether to revert transactions to mutual exclusion, delay retries, etc.).

Method and system for hardware feedback in transactional memory

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San Francisco, CA, United States of America

Kevin Moore