Parallel database systems: the future of high performance database systems
Communications of the ACM
ACM SIGMOD Record
LH*—a scalable, distributed data structure
ACM Transactions on Database Systems (TODS)
Pthreads programming
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Model 204 Architecture and Performance
Proceedings of the 2nd International Workshop on High Performance Transaction Systems
Parallel algorithms for database operations and a database operation for parallel algorithms
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Optimizing Queries on Compressed Bitmaps
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Query processing and optimization in Oracle Rdb
The VLDB Journal — The International Journal on Very Large Data Bases
Hardware acceleration for spatial selections and joins
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Byte-aligned bitmap compression
DCC '95 Proceedings of the Conference on Data Compression
Making the Pyramid Technique Robust to Query Types and Workloads
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Scout: A Hardware-Accelerated System for Quantitatively Driven Visualization and Analysis
VIS '04 Proceedings of the conference on Visualization '04
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Scientific data management in the coming decade
ACM SIGMOD Record
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
GPUTeraSort: high performance graphics co-processor sorting for large database management
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Scalable Data Servers for Large Multivariate Volume Visualization
IEEE Transactions on Visualization and Computer Graphics
GPUQP: query co-processing using graphics processors
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Multi-resolution bitmap indexes for scientific data
ACM Transactions on Database Systems (TODS)
Variable Interactions in Query-Driven Visualization
IEEE Transactions on Visualization and Computer Graphics
On the performance of bitmap indices for high cardinality attributes
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Efficient gather and scatter operations on graphics processors
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Breaking the Curse of Cardinality on Bitmap Indexes
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
A Fast Similarity Join Algorithm Using Graphics Processing Units
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Column imprints: a secondary index structure
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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The multi-core trend in CPUs and general purpose graphics processing units (GPUs) offers new opportunities for the database community. The increase of cores at exponential rates is likely to affect virtually every server and client in the coming decade, and presents database management systems with a huge, compelling disruption that will radically change how processing is done. This paper presents a new parallel indexing data structure for answering queries that takes full advantage of the increasing thread-level parallelism emerging in multi-core architectures. In our approach, our Data Parallel Bin-based Index Strategy (DP-BIS) first bins the base data, and then partitions and stores the values in each bin as a separate, bin-based data cluster. In answering a query, the procedures for examining the bin numbers and the bin-based data clusters offer the maximum possible level of concurrency; each record is evaluated by a single thread and all threads are processed simultaneously in parallel. We implement and demonstrate the effectiveness of DP-BIS on two multi-core architectures: a multi-core CPU and a GPU. The concurrency afforded by DP-BIS allows us to fully utilize the thread-level parallelism provided by each architecture---for example, our GPU-based DP-BIS implementation simultaneously evaluates over 12,000 records with an equivalent number of concurrently executing threads. In comparing DP-BIS's performance across these architectures, we show that the GPU-based DP-BIS implementation requires significantly less computation time to answer a query than the CPU-based implementation. We also demonstrate in our analysis that DP-BIS provides better overall performance than the commonly utilized CPU and GPU-based projection index. Finally, due to data encoding, we show that DP-BIS accesses significantly smaller amounts of data than index strategies that operate solely on a column's base data; this smaller data footprint is critical for parallel processors that possess limited memory resources (e.g. GPUs).