G. Graefe and S. S. Thakkar, Tuning a Parallel Database Algorithm on a Shared-Memory Multiprocessor, Software - Practice and Experience 22, 7 (July 1992), 495. 9 Home/Search Document Not in Database Summary Related Articles Check |
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Recycling Secondary Index Structures - Aoki (Correct)
....extracted from the USGS GNIS [USGS95] and Land Use 5 Our bulk load routine uses the standard technique of extracting key,TID pairs from the base table, sorting the pairs into index leaf pages and then building the rest of the tree bottom up. Our external sorting routine follows the recent trend [DEWI91, GRAE92,NYBE94] toward quicksort based run generation. 11 Index Base Table (Heap) Type Cardinality Size Distributions (tuples) bytes) clustered unclustered B tree 10 4 10 6 sorted random 10 5 10 7 sorted random 10 6 10 8 sorted random R tree 1. 4 10 5 1. 3 10 7 Hilbert (H 22 ) ....
G. Graefe and S. S. Thakkar, "Tuning a Parallel Database Algorithm on a Shared-Memory Multiprocessor," Software---Practice & Experience 22, 7 (July 1992), 495-517.
Domain-Partitioned Parallel Sort-Merge Join - Larson (1995) (Correct)
....environments from sort merge joins to hash based joins. DeWitt et al. 7] addressed the issue of joins where almost the entire relations fit into memory. They were surprised to find that hash based joins performed better than sort merge joins under these conditions. Finally, Graefe and Thakkar [9] discussed the tuning of a parallel database algorithm external sort, in particular for performance. Most of their gains were realized by increasing the efficiency of buffer use. 1.1.2 The Companion Paper Introduction The Martin, Larson, and Deshpande paper [13] analyzed three hash based ....
G. Graefe and S.S. Thakkar, "Tuning a Parallel Database Algorithm on a SharedMemory Multiprocessor", Software: Practice and Experience, 22(7), pp. 495-517, July 1992.
Parallel Implementation And Performance Analysis For The Relational .. - Roy (1994) (Correct)
....decreases [Su88] An obvious solution to the above problem is parallel processing. With the advent of multiprocessor machines, there have been several parallel algorithms designed to efficiently utilize this environment in order to achieve enhanced performance. Database machines such as VOLCANO [GT92, Gra94], GAMMA [SD89] DIRECT [DeW79] etc. and general purpose machines such as the hypercube [TOY94] are all very popular choices for database processing. The mesh architecture presents a viable alternative due to its low cost. 1.1 RELATIONAL ALGEBRA DEFINITIONS There are various different data models ....
.... which may be executed in separate processes and processors connected via pipelines (inter operator parallelism) and each of these operators consumes and produces sets which may then be partitioned or fragmented into disjoint subsets to be processed in parallel (intra operator parallelism) [GT92]. Both these forms of parallelism require data exchange between processes. Query processing in parallel is characterized by various parameters such as processing allocated to the various processors, data transfer between disks and various processors, inter processor communication, and memory ....
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G. Graefe, S. Thakkar. Tuning a Parallel Database Algorithm on a Sharedmemory Multiprocessor. Software Practice and Experience, 22(7):495-517, July 1992.
Data Compression and Database Performance - Graefe, Shapiro (1991) (13 citations) Self-citation (Graefe) (Correct)
....is saved when moving data up or down in the hierarchy, when moving data laterally (e.g. between memories or caches) and by achieving a higher hit rate at each level. Reducing the amount of bus traffic in shared memory systems might also allow higher degrees of parallelism without bus saturation [14]. For transaction processing, there will probably be two main effects of compression. First, the buffer hit rate should increase since more records fit into the buffer space. Second, I O to log devices should decrease since the log records can become shorter. Most systems already employ log ....
G. Graefe and S. S. Thakkar, Tuning a Parallel Database Algorithm on a Shared-Memory Multiprocessor, Software - Practice and Experience 22, 7 (July 1992), 495. 9
Adaptive Parallel Query Execution in DBS3 - Bouganim, Dageville, Valduriez (1995) (Correct)
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G. Graefe, S. Thakkar, "Tuning a Parallel Database Algorithm on a Shared-Memory Multiprocessor", Software - Practice and Experience, Vol. 22, No.7, July 1992.
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