52 research outputs found
Decoding billions of integers per second through vectorization
In many important applications -- such as search engines and relational
database systems -- data is stored in the form of arrays of integers. Encoding
and, most importantly, decoding of these arrays consumes considerable CPU time.
Therefore, substantial effort has been made to reduce costs associated with
compression and decompression. In particular, researchers have exploited the
superscalar nature of modern processors and SIMD instructions. Nevertheless, we
introduce a novel vectorized scheme called SIMD-BP128 that improves over
previously proposed vectorized approaches. It is nearly twice as fast as the
previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the
same time, SIMD-BP128 saves up to 2 bits per integer. For even better
compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has
a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while
being two times faster during decoding.Comment: For software, see https://github.com/lemire/FastPFor, For data, see
http://boytsov.info/datasets/clueweb09gap
One-Pass Ranking Models for Low-Latency Product Recommendations
Purchase logs collected in e-commerce platforms provide rich information about customer preferences. These logs can be leveraged to improve the quality of product recommenda-tions by feeding them to machine-learned ranking models. However, a variety of deployment constraints limit the näıve applicability of machine learning to this problem. First, the amount and the dimensionality of the data make in-memory learning simply not possible. Second, the drift of customers’ preference over time require to retrain the ranking model regularly with freshly collected data. This limits the time that is available for training to prohibitively short intervals. Third, ranking in real-time is necessary whenever the query complexity prevents us from caching the predictions. This constraint requires to minimize prediction time (or equiva
Data Mining in Data-Intensive and Cognitively-Complex Settings: Lessons Learned from the Dicode Project
The antecedent and consequence behaviour of sustainable tourism: integrating the concepts of marketing strategy and destination image
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