20150318-SFPUG-Meetup-PGStrom
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The document presents PG-Strom, an extension for PostgreSQL that accelerates query processing by leveraging GPU resources for heavy SQL computations. Key features include automatic GPU code generation, an asynchronous query executor, and compatibility with existing PostgreSQL tools, eliminating the need for extensive database tuning. Future developments aim to enhance functionality and integration with newer technologies while promoting the project's open-source nature.
![World of current cpu/memory bottleneck
Join, Aggregation, Sort, Projection, ...
[strategy]
• burstable access pattern
• parallel algorithm
World of traditional disk-i/o bottleneck
SeqScan, IndexScan, ...
[strategy]
• reduction of i/o (size, count)
• distribution of disk (RAID)
RDBMS and bottleneck (2/2)
DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 6
Processor
RAM
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bandwidth：
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hundreds GB/s
bandwidth：
multiple GB/s](https://image.slidesharecdn.com/20150318-sfpug-meetup-kaigai-150319005255-conversion-gate01/85/20150318-SFPUG-Meetup-PGStrom-6-320.jpg)
![Background (3/4) – How GPU works
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Computing
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𝑖𝑡𝑒𝑚[𝑖]
𝑖=0…𝑁−1
with N-cores of GPU
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Total sum of items[]
with log2N steps
Inter core synchronization by HW support](https://image.slidesharecdn.com/20150318-sfpug-meetup-kaigai-150319005255-conversion-gate01/85/20150318-SFPUG-Meetup-PGStrom-9-320.jpg)
![Benchmark result (1/2) – simple tables join
▌Benchmark Query:
SELECT * FROM t0 NATURAL JOIN t1 [NATURAL JOIN ....];
▌Environment:
t0 has 100million rows (13GB), t1-t9 has 40,000 rows for each, all-data pre-loaded
CPU: Xeon E5-2670v3 (12C, 2.3GHz) x2, RAM: 384GB, GPU: Tesla K20c x1
PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.17
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number of tables joined
Simple Tables Join Benchmark
PG-Strom PostgreSQL](https://image.slidesharecdn.com/20150318-sfpug-meetup-kaigai-150319005255-conversion-gate01/85/20150318-SFPUG-Meetup-PGStrom-17-320.jpg)
![Benchmark result (2/2) – Star Schema Model
▌40 typical reporting queries
▌100GB of retail / start-schema data, all pre-loaded
▌Environment
CPU: Xeon E5-2670v3(12C, 2.3GHz) x2, RAM: 384GB, GPU: Tesla K20c x1
PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.19
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QueryResponseTime[sec]
Typical Reporting Queries on Retail / Star-Schema Data
PG-Strom PostgreSQL](https://image.slidesharecdn.com/20150318-sfpug-meetup-kaigai-150319005255-conversion-gate01/85/20150318-SFPUG-Meetup-PGStrom-19-320.jpg)
20150318-SFPUG-Meetup-PGStrom
- 1. PG-Strom Query Acceleration Engine of PostgreSQL Powered by GPGPU NEC OSS Promotion Center The PG-Strom Project KaiGai Kohei <[email protected]>
- 2. Self Introduction ▌Name: KaiGai Kohei ▌Company: NEC ▌Mission: Software architect & Intrepreneur ▌Background: Linux kernel development (2003~?) PostgreSQL development (2006~) SAP alliance (2011~2013) PG-Strom development & productization (2012~) ▌PG-Strom Project: In-company startup of NEC Also, an open source software project PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.2
- 3. What is PG-Strom ▌An Extension of PostgreSQL ▌Off-loads CPU intensive SQL workloads to GPU processors ▌Major Features ① Automatic and just-in-time GPU code generation from SQL ② Asynchronous and concurrent query executor PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.3 database Query Executor Query Planner Custom Executor Custom Planner GPU code on the flySQL command Async- Execution PG-Strom Query Frontend
- 4. Concept ▌No Pain Looks like a traditional PostgreSQL database from standpoint of applications, thus, we can utilize existing tools, drivers, applications. ▌No Tuning Massive computing capability by GPGPU kills necessity of database tuning by human. It allows engineering folks to focus on the task only human can do. ▌No Complexity No need to export large data to external tools from RDBMS, because its computing performance is sufficient to run the workloads nearby data. PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.4
- 5. RDBMS and bottleneck (1/2) DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 5 Storage Processor Data RAM Data Size > RAM Data Size < RAM Storage Processor Data RAM In the future? Processor Wide Band RAM Non- volatile RAM Data
- 6. World of current cpu/memory bottleneck Join, Aggregation, Sort, Projection, ... [strategy] • burstable access pattern • parallel algorithm World of traditional disk-i/o bottleneck SeqScan, IndexScan, ... [strategy] • reduction of i/o (size, count) • distribution of disk (RAID) RDBMS and bottleneck (2/2) DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 6 Processor RAM Storage bandwidth： multiple hundreds GB/s bandwidth： multiple GB/s
- 7. Background (1/4) – Semiconductor Trend ▌Movement to CPU/GPU integrated architecture rather than multicore CPU ▌Free lunch for SW by HW evolution will finish soon Unless software is not designed to utilize GPU capability, unable to pull-out the full hardware capability. DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 7 SOURCE: THE HEART OF AMD INNOVATION, Lisa Su, at AMD Developer Summit 2013
- 8. Background (2/4) – Features of GPU ▌Characteristics Larger percentage of ALUs on chip Relatively smaller percentage of cache and control logic Advantages to simple calculation in parallel, but not complicated logic Much higher number of cores per price • GTX750Ti (640core) with $150 GPU CPU Model Nvidia Tesla K20X Intel Xeon E5-2670 v3 Architecture Kepler Haswell Launch Nov-2012 Sep-2014 # of transistors 7.1billion 3.84billion # of cores 2688 (simple) 12 (functional) Core clock 732MHz 2.6GHz, up to 3.5GHz Peak Flops (single precision) 3.95TFLOPS 998.4GFLOPS (with AVX2) DRAM size 6GB, GDDR5 768GB/socket, DDR4 Memory band 250GB/s 68GB/s Power consumption 235W 135W Price $3,000 $2,094 DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 8 SOURCE: CUDA C Programming Guide (v6.5)
- 9. Background (3/4) – How GPU works ●item[0] step.1 step.2 step.4step.3 Computing the sum of array: 𝑖𝑡𝑒𝑚[𝑖] 𝑖=0…𝑁−1 with N-cores of GPU ◆ ● ▲ ■ ★ ● ◆ ● ● ◆ ▲ ● ● ◆ ● ● ◆ ▲ ■ ● ● ◆ ● ● ◆ ▲ ● ● ◆ ● item[1] item[2] item[3] item[4] item[5] item[6] item[7] item[8] item[9] item[10] item[11] item[12] item[13] item[14] item[15] Total sum of items[] with log2N steps Inter core synchronization by HW support
- 10. Background (4/4) – Custom-Plan Interface PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.10 Aggregate SELECT cat, avg(x) FROM t1, t2 WHERE t1.id = t2.id AND y > 100 GROUP BY cat; Scan on t1 Scan on t2 Join t1 t2 key: cat • Hash Join • Merge Join • Nested Loop • Custom Join • Seq Scan • Index Scan • Index-Only Scan • Tid Scan • Custom Scan IndexScan on t1 y > 100 “BulkLoad” on t1 “GpuHashJoin” t1.id = t2.id
- 11. PG-Strom Features ▌Logics GpuScan ... Parallel evaluation of scan qualifiers GpuHashJoin ... Parallel multi-relational join GpuPreAgg ... Two phase aggregation GpuSort ... GPU + CPU Hybrid Sorting GpuNestedLoop (in develop) ▌Data Types Integer, Float, Date/Time, Numeric, Text ▌Function and Operators Equality and comparison operators Arithmetic operators and mathematical functions Aggregates: count, min/max, sum, avg, std, var, corr, regr PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.11
- 12. Automatic GPU code generation PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.12 postgres=# SET pg_strom.show_device_kernel = on; postgres=# EXPLAIN VERBOSE SELECT * FROM t0 WHERE sqrt(x+y) < 10; QUERY PLAN -------------------------------------------------------------------------------- Custom Scan (GpuScan) on public.t0 (cost=500.00..357569.35 rows=6666683 width=77) Output: id, cat, aid, bid, cid, did, eid, x, y, z Device Filter: (sqrt((t0.x + t0.y)) < 10::double precision) Features: likely-tuple-slot Kernel Source: #include "opencl_common.h“ : static pg_bool_t gpuscan_qual_eval(__private cl_int *errcode, __global kern_parambuf *kparams, __global kern_data_store *kds, __global kern_data_store *ktoast, size_t kds_index) { pg_float8_t KPARAM_0 = pg_float8_param(kparams,errcode,0); pg_float8_t KVAR_8 = pg_float8_vref(kds,ktoast,errcode,7,kds_index); pg_float8_t KVAR_9 = pg_float8_vref(kds,ktoast,errcode,8,kds_index); return pgfn_float8lt(errcode, pgfn_dsqrt(errcode, pgfn_float8pl(errcode, KVAR_8, KVAR_9)), KPARAM_0); }
- 13. Implementation (1/3) – GpuScan PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.13 Table DMA Send DMA Recv DMA Send DMA Recv DMA Send DMA Recv Execution of auto-generated GPU code Result Output Stream Input Stream Chunk (16~64MB)
- 14. PostgreSQL PG-Strom Software Architecture (1/2) – current version DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 14 GPU Code Generator Storage Storage Manager Shared Buffer Query Parser Query Optimizer Query Executor SQL Query Breaks down the query to parse tree Makes query execution plan Run the query Custom-PlanAPIs GpuScan GpuHashJoin GpuPreAgg GPU Program Manager PG-Strom OpenCL Server Message Queue GpuSort
- 15. PostgreSQL PG-Strom Software Architecture (2/2) – upcoming version DB Tech Showcase 2014 Tokyo; PG-Strom - GPGPU acceleration on PostgreSQLPage. 15 GPU Code Generator Storage Storage Manager Shared Buffer Query Parser Query Optimizer Query Executor SQL Query Breaks down the query to parse tree Makes query execution plan Run the query Custom-PlanAPIs GpuScan GpuHashJoin GpuPreAgg CUDA controller GpuSort DMA Buffer Just-in-time compilewith NVRTC Copy DMA kernel launch
- 16. Implementation (2/3) – GpuHashJoin PG-Strom Preview Feb-2015Page. 16 Inner relation Outer relation Inner relation Outer relation Hash Table Hash Table Next stage Next stage CPU just references materialized results Hash-Table Search by CPU Sequential Materialization by CPU Parallel Materialization Parallel Hash-Table Search vanilla Hash-Join GpuHashJoin
- 17. Benchmark result (1/2) – simple tables join ▌Benchmark Query: SELECT * FROM t0 NATURAL JOIN t1 [NATURAL JOIN ....]; ▌Environment: t0 has 100million rows (13GB), t1-t9 has 40,000 rows for each, all-data pre-loaded CPU: Xeon E5-2670v3 (12C, 2.3GHz) x2, RAM: 384GB, GPU: Tesla K20c x1 PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.17 18.19 19.45 21.04 23.66 26.69 37.64 43.22 49.57 56.38 64.27 87.73 109.73 132.21 155.10 179.62 207.85 233.31 263.51 0.00 50.00 100.00 150.00 200.00 250.00 300.00 2 3 4 5 6 7 8 9 10 QueryResponseTime[sec] number of tables joined Simple Tables Join Benchmark PG-Strom PostgreSQL
- 18. Implementation (3/3) – GpuPreAgg PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.18 Table 1st Stage Reduction 2nd Stage Reduction Chunk (16~64MB)
- 19. Benchmark result (2/2) – Star Schema Model ▌40 typical reporting queries ▌100GB of retail / start-schema data, all pre-loaded ▌Environment CPU: Xeon E5-2670v3(12C, 2.3GHz) x2, RAM: 384GB, GPU: Tesla K20c x1 PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.19 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00 1600.00 1800.00 2000.00 Q.01 Q.02 Q.03 Q.04 Q.05 Q.06 Q.07 Q.08 Q.09 Q.10 Q.11 Q.12 Q.13 Q.14 Q.15 Q.16 Q.17 Q.18 Q.19 Q.20 Q.21 Q.22 Q.23 Q.24 Q.25 Q.26 Q.27 Q.28 Q.29 Q.30 Q.31 Q.32 Q.33 Q.34 Q.35 Q.36 Q.37 Q.38 Q.39 Q.40 QueryResponseTime[sec] Typical Reporting Queries on Retail / Star-Schema Data PG-Strom PostgreSQL
- 20. Expected Scenario – Reduction of ETL ▌ETL – Its design is human centric task ▌Replication – much automatous task PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.20 ERPCRMSCM BI OLTP database OLAP database ETL OLAP CubesMaster / Fact Tables BI Replication Replica of Master / Fact Tables Optimized to transaction workloads Optimized to analytic workloads Sufficient to analytic workloads also PG-Strom
- 21. Direction of PG-Strom PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.21
- 22. Development Plan Migration of OpenCL to CUDA Add support of GpuNestedLoop Add support multi-functional kernel Standardization of custom-join interface ...and more...? PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.22 Short term target: PostgreSQL v9.5 timeline (2015) Middle term target: PostgreSQL v9.6 timeline (2016) Current version: PG-Strom β + PostgreSQL v9.5devel Integration with funnel executor Investigation to SSD/NvRAM utilization Custom-sort/aggregate interface Add support for spatial data types (?)
- 23. Enhancement Idea (1/3) – GpuNestedLoop PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.23 Inner-Relation (Ny: relatively small) Outer-Relation (Nx:relativelylarge) ● ● ● ● ● ● ● ● ● ● ●●●●●●● blockDim.x blockDim.y Ny items Nx items Thread (X=2, Y=3) Each GPU threads evaluates non-equalar join condition in parallel 2-dimensional GPU kernel launch (Nx×Ny threads at once)
- 24. Enhancement Idea (2/3) – Multi-functional GPU kernel PG-Strom Preview Feb-201524 magnetic storage GpuScan GpuHashJoin GpuPreAgg Final result current query execution Data Chunk on-memory cache Load GpuScan Kernel GpuHashJoin Kernel GpuPreAgg Kernel execution on GPU device Interaction between CPU and GPU multiple times magnetic storage Final result execution in newer version on-memory cache Interaction between CPU and GPU Only once GpuMultiOps GpuScan GpuHashJoin GpuPreAgg GpuMultiOps Kernel •PreAgg •HashJoin •Scan Data Chunk
- 25. Enhancement Idea (3/3) – Funnel executor integration PG-Strom Preview Feb-201525 magnetic storage on-memory cache Scan Join Aggregate Final result current query execution Query Execution Plan PG-Strom may replace GPU version, but host system run with single thread. execution in PostgreSQL v9.6 Funnel Executor Partial Aggregate Partial Scan Partial Aggregate Partial Scan Partial Aggregate Join Partial Scan Join Join magnetic storage SSD device on-memory cache Final result Combined Aggregate Funnel executor assigns a part of query execution task on worker processes Combines multiple partial aggregates to generate the final result
- 26. Let’s try – Deployment on AWS Page. 26 Search by “strom” ! AWS GPU Instance (g2.2xlarge) CPU Xeon E5-2670 (8 xCPU) RAM 15GB GPU NVIDIA GRID K2 (1536core) Storage 60GB of SSD Price $0.898/hour, $646.56/mon (*) Price for on-demand instance on Tokyo region at Nov-2014 The PostgreSQL Conference 2014, Tokyo - GPGPU Accelerates PostgreSQL
- 27. Welcome to your involvement ▌How to be involved? as a user as a developer as a business partner ▌Source code https://github.com/pg-strom/devel ▌Contact US e-mail: [email protected] twitter: @kkaigai PG-Strom - Query Acceleration Engine of PostgreSQL Powered by GPGPU -P.27 check it out!