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Dive into the research topics where David Kalmuk is active.

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Featured researches published by David Kalmuk.


very large data bases | 2013

DB2 with BLU acceleration: so much more than just a column store

Vijayshankar Raman; Gopi K. Attaluri; Ronald J. Barber; Naresh K. Chainani; David Kalmuk; Vincent Kulandaisamy; Jens Leenstra; Sam Lightstone; Shaorong Liu; Guy M. Lohman; Tim R Malkemus; Rene Mueller; Ippokratis Pandis; Berni Schiefer; David C. Sharpe; Richard S. Sidle; Adam J. Storm; Liping Zhang

DB2 with BLU Acceleration deeply integrates innovative new techniques for defining and processing column-organized tables that speed read-mostly Business Intelligence queries by 10 to 50 times and improve compression by 3 to 10 times, compared to traditional row-organized tables, without the complexity of defining indexes or materialized views on those tables. But DB2 BLU is much more than just a column store. Exploiting frequency-based dictionary compression and main-memory query processing technology from the Blink project at IBM Research - Almaden, DB2 BLU performs most SQL operations - predicate application (even range predicates and IN-lists), joins, and grouping - on the compressed values, which can be packed bit-aligned so densely that multiple values fit in a register and can be processed simultaneously via SIMD (single-instruction, multipledata) instructions. Designed and built from the ground up to exploit modern multi-core processors, DB2 BLUs hardware-conscious algorithms are carefully engineered to maximize parallelism by using novel data structures that need little latching, and to minimize data-cache and instruction-cache misses. Though DB2 BLU is optimized for in-memory processing, database size is not limited by the size of main memory. Fine-grained synopses, late materialization, and a new probabilistic buffer pool protocol for scans minimize disk I/Os, while aggressive prefetching reduces I/O stalls. Full integration with DB2 ensures that DB2 with BLU Acceleration benefits from the full functionality and robust utilities of a mature product, while still enjoying order-of-magnitude performance gains from revolutionary technology without even having to change the SQL, and can mix column-organized and row-organized tables in the same tablespace and even within the same query.


Archive | 2012

PROCESSOR PROVISIONING BY A MIDDLEWARE SYSTEM FOR A PLURALITY OF LOGICAL PROCESSOR PARTITIONS

Paul M. Bird; David Kalmuk


Archive | 2008

Method and system for optimizing file table usage

David Kalmuk; Jon A. Lind; Hebert W. Pereyra; Xun Xue


Archive | 2003

Reduced synchronization reservation system and method for a shared memory buffer

Matthew A. Huras; David Kalmuk; John Kennedy; Herbert W. Pereyra; Mark Francis Wilding


Archive | 2003

Communication multiplexor for use with a database system implemented on a data processing system

David Kalmuk; Jon A. Lind; Hebert W. Pereyra; Xun Xue


Archive | 2014

Adaptive resource usage limits for workload management

Paul M. Bird; David Kalmuk; Stephen Rees; Scott Douglas Walkty


Archive | 2003

Communication multiplexor using listener process to detect newly active client connections and passes to dispatcher processes for handling the connections

David Kalmuk; Jon A. Lind; Hebert W. Pereyra; Xun Xue


Archive | 2006

Asynchronous interconnect protocol for a clustered dbms

David Kalmuk; Herbert W. Pereyra; Jack Hon Wai Ng; Cheuk Lun Lam


Archive | 2014

PROCESSOR PROVISIONING BY A MIDDLEWARE PROCESSING SYSTEM FOR A PLURALITY OF LOGICAL PROCESSOR PARTITIONS

Paul M. Bird; David Kalmuk


Archive | 2014

Processor provisioning by a middleware processing system

Paul M. Bird; David Kalmuk

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