Solving Performance Issues in BI through Purpose-built Analytical ...

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Vector Processing One operation performed on one element at a time Large overheads Traditional Scalar Processing 1 x 1 = 1 2 x 2 = 4 3 x 3 = 9 4 x 4 = 16 5 x 5 = 25 6 x 6 = 36 7 x 7 = 49 8 x 8 = 64 . . . n x n = n 2 Many V’s 1 Vector Processing 1 x 1 2 x 2 3 x 3 4 x 4 5 x 5 6 x 6 7 x 7 8 x 8 . . . n x n = 1 4 9 16 25 36 49 64 . . . n 2 One operation performed on a set of data at a time No overheads Process even 1.5GB per second
Processing in Chip Cache Time / Cycles to Process 2-20 150-250 Millions Using CPU cache is far more faster & efficient DISK 40-100MB RAM 2-3GB Data Processed GB/s Measure of Throughput Cycles Amount of CPU time required to process data CHIP 10GB
Page 1 and 2: Solving Performance Issues in BI th
Page 3 and 4: Today’s Challenges in BI Make inf
Page 5 and 6: Biggest BI Challenge - SPEED Source
Page 7 and 8: Relational Databases and Speed Limi
Page 9 and 10: Approaches Used for Achieving Datab
Page 11 and 12: VectorWise: On Chip Computing/Colum
Page 13: Modern CPU Instruction Capabilities
Page 17 and 18: Optimized Compression & Fast De-Com
Page 19 and 20: VectorWise Features Performance •
Page 21 and 22: TPC Benchmark Results - 1TB 2.5x Pe
Page 23 and 24: VectorWise BI Tuning & Complexity T
Page 25 and 26: Analytical Databases - Illustrative
Page 27 and 28: More Information VectorWise LinkedI

Solving Performance Issues in BI through Purpose-built Analytical ...

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