1. The Yin and Yang of Processing Data Warehousing Queries on GPUs
Two opposite forces that are interconnected and interdependent in natural world
The Yin and Yang of Processing Data Warehousing Queries on GPUs
Yuan Yuan, Rubao Lee, Xiaodong Zhang
The Ohio State University
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2. GPUs: Powerful and Programmable
Performance (GFLOPS)
After 10 years’ R&D, GPUs have evolved from dedicated graphics processors into high performance, general purpose computing devices
2002
2012
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3. GPUs In High Performance Computing
No. 1 Titan:
261,632 NVIDIA Cores
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4. GPUs: massive parallel computing units
DW workloads: rich data parallelism
A decade of research efforts in database community
[SIGMOD03] [VLDB04] [SIGMOD04] [SIGMOD06] [SIGMOD08]
[SIGMOD10] [VLDB10] [VLDB11] [VLDB12]
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5. GPUs in DW Production Systems?
None!
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6. Why such general purpose GPUs have not been adopted for critical query processing?
Query Characteristics
Software
Techniques
Hardware
Advancement
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7. Query Processing on GPUs
CPU
Yang
Core
Core
Core
Core
GPU
Core
Core
Core
Core
Device
Kernel Execution
Host Memory
Device Memory D A T A
PCIe
Yin
PCIe Data Transfer
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8. Experimental Environment
Hardware
CPU Intel Core i7 3770k
NVIDIA GTX 480, 580 and 680
GPU Query Engine Prototype
Automatic translator from SQL to highly optimized CUDA programs (based on YSmart)
Column store
Workload
Star Schema Benchmark
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9. Unbalanced Yin and Yang of SSBM
Kernel execution time varies greatly
Most queries are dominated by PCIe data transfer
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10. Understand “Where does time go?”
How do different query characteristics affect query performance?
How does GPU hardware advancement affect the performance?
How do software optimizations affect the performance?
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11. GPU Hardware Parameters
GTX 480
GTX 580
GTX 680
Year
2010
2011
2012
Architecture
Fermi
Kepler
# of Cores
480
512
1536
GFLOPS
1345
1581.1
3090.4
Memory BW(GB/s)
177.4
192.4
192.3
PCIe
2.0
3.0
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12. Limited Performance improvement by GPU Arch
480
580
680
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13. More Performance Improvement by PCIe Bandwidth
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14. Performance Prediction
10%-15% improvement
30%-35% improvement
Limited benefits from near future GPU advancement
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15. Software Optimization
Techniques
Data Compression
Invisible Join
Transfer Overlapping
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16. The Impact of Compression on SSBM
Effective: high selectivity, and less projected columns
baseTransfer
compressTransfer
baseKernel
compressKernel
Ineffective: high selectivity, and with projected dim columns
Ineffective: extreme low selectivity
Reduced PCIe transfer time
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17. The Impact of Invisible Join on SSBM
Effective: more projected columns from dimension table, with high selectivity
baseTransfer
inviTransfer
baseKernel
inviKernel
Effective: high Selectivity
projected dim columns
Ineffective: extreme low selectivity
No effect on PCIe transfer
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18. The Impact of Transfer Overlapping on SSBM
Effective: low selectivity, more projected column from fact table
Ineffective: high selectivity
Effective: extreme low selectivity
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19. So, why GPUs not adopted in DWs?
Complicated and subtle choices of query optimization techniques.
Limited usage of GPU hardware resources and unlikely benefit from GPUs advancement due to unbalanced Yin and Yang.
Lack of efficient system software support for memory management and task concurrency.
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20. Take Home Messages Data Software Hardware
Schema design should take into account GPU features and avoid data alignment issues.
Software
Compression, invisible join and transfer overlapping are the most effective techniques for GPU query processing, but they favor different kind of queries
Hardware
Query performance are bounded by PCIe bandwidth and GPU device memory bandwidth, but limited benefits from the advancement of GPU hardware.
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21. Thank you! Questions?
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