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![Supported Workload – Scan, Join, Aggregation
▌SELECT cat, AVG(x) FROM t0 NATURAL JOIN t1 [, ...] GROUP BY cat;
t0: 100M rows, t1~t10: 100K rows for each, all the data was preloaded.
▌Environment:
PostgreSQL v9.5beta1 + PG-Strom (22-Oct), CUDA 7.0 + RHEL6.6 (x86_64)
CPU: Xeon E5-2670v3, RAM: 384GB, GPU: NVIDIA TESLA K20c (2496cores)
PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞4
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# of tables involved
Time consumption per component (PostgreSQL v9.5β vs PG-Strom)
Scan Join Aggregate Others](https://image.slidesharecdn.com/20151127ltkaigaisqlgpussden-151127113507-lva1-app6891/75/SQL-GPU-SSD-English-4-2048.jpg)
![Supported Workload – Scan, Join, Aggregation
▌SELECT cat, AVG(x) FROM t0 NATURAL JOIN t1 [, ...] GROUP BY cat;
t0: 100M rows, t1~t10: 100K rows for each, all the data was preloaded.
▌Environment:
PostgreSQL v9.5beta1 + PG-Strom (22-Oct), CUDA 7.0 + RHEL6.6 (x86_64)
CPU: Xeon E5-2670v3, RAM: 384GB, GPU: NVIDIA TESLA K20c (2496cores)
PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞4
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PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom
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QueryResponseTime[sec]
# of tables involved
Time consumption per component (PostgreSQL v9.5β vs PG-Strom)
Scan Join Aggregate Others](https://crownmelresort.com/image.slidesharecdn.com/20151127ltkaigaisqlgpussden-151127113507-lva1-app6891/75/SQL-GPU-SSD-English-4-2048.jpg)
Uploaded byKohei KaiGai
SQL+GPU+SSD=∞ (English)
This document discusses using GPUs and SSDs to accelerate PostgreSQL queries. It introduces PG-Strom, a project that generates CUDA code from SQL to execute queries massively in parallel on GPUs. The document proposes enhancing PG-Strom to directly transfer data from SSDs to GPUs without going through CPU/RAM, in order to filter and join tuples during loading for further acceleration. Challenges include improving the NVIDIA driver for NVMe devices and tracking shared buffer usage to avoid unnecessary transfers. The goal is to maximize query performance by leveraging the high bandwidth and parallelism of GPUs and SSDs.
More Related Content
SQL+GPU+SSD=∞ (English)
- 1.
- 2. Self Introduction ▌Name: Wasserschwein@Shinagawa ▌PostgreSQL:9Years (2006~) ▌Works: Security, FDW, etc... ▌Hobby: Mixture of heterogeneous technology with PostgreSQL PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞2 Very powerful computing capability Very functional & well-used database PG-Strom: What I’m making GPGPU
- 3. What’s PG-Strom –Brief overview PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞3 ▌Core ideas ① GPU native code generation on the fly ② Asynchronous massive parallel execution ▌Advantages Transparent acceleration with 100% query compatibility Commodity H/W and less system integration cost Parser Planner Executor Custom- Scan/Join Interface Query: SELECT * FROM l_tbl JOIN r_tbl on l_tbl.lid = r_tbl.rid; PG-Strom CUDA driver nvrtc DMA Data Transfer CUDA Source code Massive Parallel Execution
- 4. Supported Workload –Scan, Join, Aggregation ▌SELECT cat, AVG(x) FROM t0 NATURAL JOIN t1 [, ...] GROUP BY cat; t0: 100M rows, t1~t10: 100K rows for each, all the data was preloaded. ▌Environment: PostgreSQL v9.5beta1 + PG-Strom (22-Oct), CUDA 7.0 + RHEL6.6 (x86_64) CPU: Xeon E5-2670v3, RAM: 384GB, GPU: NVIDIA TESLA K20c (2496cores) PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞4 0 50 100 150 200 250 300 PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom PgSQL Strom 2 3 4 5 6 7 8 QueryResponseTime[sec] # of tables involved Time consumption per component (PostgreSQL v9.5β vs PG-Strom) Scan Join Aggregate Others
- 5. Next target isI/O acceleration – from TPC/DS results PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞5 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Time consumption per workloads (PostgreSQL v9.5beta+PG-Strom) Scan Join Aggregate Others So, How to accelerate I/O stuff by GPU?
- 6. NOTICE The story Ilike to introduce next is... Just my Ideaat this moment ......So, I’ll pay my efforts to implement PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞6
- 7. A rough x86_64hardware architecture PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞7 GPU SSD CPU + RAM CPU + RAM PCI-E SAS Usual I/O bottleneck
- 8. Simplified diagram forintroduction PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞8 GPU SSD CPU + RAM PCI-E OK, it’s storage
- 9. NVM EXPRESS SSD PostgreSQLConference Japan - LT: SQL+GPU+SSD=∞9 PCI-E direct SSD device – low latency and higher bandwidth Samsung SSD 950 PRO Intel SSD 750 HGST Ultrastar SN100 Intel SSD DC P3700
- 10. Data Flow inanalytic queries ① Data load from storage to CPU/RAM PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞10 GPU SSD CPU + RAM PCI-E Table
- 11. Data Flow inanalytic queries ① Data load from storage to CPU/RAM ② Remove invisible rows (Select) PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞11 GPU SSD CPU + RAM PCI-E Table
- 12. Data Flow inanalytic queries ① Data load from storage to CPU/RAM ② Remove invisible rows (Select) ③ Remove unreferenced columns (Projection) PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞12 GPU SSD CPU + RAM PCI-E Table
- 13. The jobof CPU Data Flow in analytic queries ① Data load from storage to CPU/RAM ② Remove invisible rows (Select) ③ Remove unreferenced columns (Projection) ④ Join with other tables (Join) PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞13 GPU SSD CPU + RAM PCI-E Table +
- 14. SSD-to-GPU Direct PostgreSQL ConferenceJapan - LT: SQL+GPU+SSD=∞14 Data transfer between SSD and GPU, bypass CPU/RAM Also available on NVMe, not only Fusion-IO
- 15. Data Flow inanalytic queries (1/3) – Basic PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞15 GPU SSD CPU + RAM PCI-E Table SSD-to-GPU Direct
- 16. Data Flow inanalytic queries (1/3) – Basic PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞16 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct
- 17. Data Flow inanalytic queries (1/3) – Basic PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞17 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Remove invisible rows according to the scan qualifiers
- 18. Data Flow inanalytic queries (1/3) – Basic PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞18 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Only visible rows are moved to CPU+RAM Remove invisible rows according to the scan qualifiers
- 19. Data Flow inanalytic queries (2/3) – Advanced PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞19 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct
- 20. Data Flow inanalytic queries (2/3) – Advanced PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞20 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Remove invisible rows according to the scan qualifiers
- 21. Data Flow inanalytic queries (2/3) – Advanced PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞21 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Only visible rows and referenced columns are moved to CPU+RAM Remove invisible rows according to the scan qualifiers Remove invisible rows and unreferenced columns according to the scan qualifiers and projection
- 22. Data Flow inanalytic queries (3/3) – Ultimate PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞22 GPU SSD CPU + RAM PCI-E GPUcodegenerated fromSQLonthefly Innerrelations (JOINtarget) Table
- 23. Data Flow inanalytic queries (3/3) – Ultimate PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞23 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Innerrelations (JOINtarget)
- 24. Data Flow inanalytic queries (3/3) – Ultimate PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞24 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Innerrelations (JOINtarget)
- 25. Data Flow inanalytic queries (3/3) – Ultimate PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞25 GPU SSD CPU + RAM PCI-E Table GPUcodegenerated fromSQLonthefly SSD-to-GPU Direct Tuples are already joined when it read data from the storage Innerrelations (JOINtarget) + Generate joined tuples on GPU side
- 26. Primitive Technologies ▌NVIDIA GPUDirectenhancement on NVMe device driver Interaction between NVMe and NVIDIA drivers are needed ▌Usage statistics of shared_buffers per relations To avoid SSDGPU direct on relations that is already preloaded ▌Add new access mode to shared_buffers Nobody can make the buffer dirty under the SSDGPU Direct transfer We are welcome all the developer who join to PG-Strom project PostgreSQL Conference Japan - LT: SQL+GPU+SSD=∞26
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