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Toward GPUs being mainstream in analytic processing An initial argument using simple scan- aggregate queries Jason Power || Yinan Li || Mark D. Hill Jignesh M. Patel || David A. Wood < powerjg@cs.wisc.edu> DaMoN 2015 6/1/2015 UNIVERSITY

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Toward GPUs being mainstream in analytic processing

An initial argument using simple scan- aggregate queries

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Jason Power || Yinan Li || Mark D. Hill Jignesh M. Patel || David A. Wood <powerjg@cs.wisc.edu>

DaMoN 2015

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Summary

▪ GPUs are energy efficient

▪ Discrete GPUs unpopular for DBMS ▪ New integrated GPUs solve the problems

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▪ Scan-aggregate GPU implementation

▪ Wide bit-parallel scan ▪ Fine-grained aggregate GPU offload

▪ Up to 70% energy savings over multicore CPU

▪ Even more in the future

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Analytic Data is Growing

▪ Data is growing rapidly ▪ Analytic DBs increasingly important

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Want: High performance Need: Low energy

Source: IDC’s Digital Universe Study. 2012.

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GPUs to the Rescue?

▪ GPUs are becoming more general

▪ Easier to program ▪ Integrated GPUs are everywhere

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[Govindaraju ’04, He ’14, He ’14, Kaldewey ‘12, Satish ’10, and many others]

▪ GPUs show great promise

▪ Higher performance than CPUs ▪ Better energy efficiency

▪ Analytic DBs look like GPU workloads

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GPU Microarchitecture

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L2 Cache Graphics Processing Unit I-Fetch/Sched

SP SP SP SP

L1 Cache

Scratchpad Cache

Register File

Compute Unit

SP SP SP SP SP SP SP SP

CU

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Discrete GPUs

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Memory Bus CPU chip Memory Bus Discrete GPU Cores PCIe Bus

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Discrete GPUs

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Memory Bus CPU chip Memory Bus Discrete GPU Cores PCIe Bus

➊ ➋

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Discrete GPUs

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Memory Bus CPU chip Memory Bus Discrete GPU Cores PCIe Bus

➌ ➍ And repeat

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Discrete GPUs

▪ Copy data over PCIe

▪ Low bandwidth ▪ High latency

▪ Small working memory ▪ High latency user→kernel calls ▪ Repeated many times

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98% of time spent not computing

➊ ➋ ➌ ➍

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Integrated GPUs

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Memory Bus

Heterogeneous chip

CPU cores GPU CUs

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▪ No need for data copies

▪ Cache coherence and shared address space

▪ No OS kernel interaction

▪ User-mode queues

Heterogeneous System Arch.

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▪ API for tightly-integrated accelerators ▪ Industry support

▪ Initial hardware support today ▪ HSA foundation (AMD, ARM,Qualcomm, others)

➊➋ ➌ ❹

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Outline

▪ Background ▪ Algorithms

▪ Scan ▪ Aggregate

▪ Results

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Analytic DBs

▪ Resident in main-memory ▪ Column-based layout

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▪ WideTable & BitWeaving [Li and Patel ‘13 & ‘14]

▪ Convert queries to mostly scans by pre-joining tables ▪ Fast scan by using sub-word parallelism ▪ Similar to industry proposals [SAP Hana, Oracle Exalytics, IBM DB2 BLU]

▪ Scan-aggregate queries

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Running Example

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Shirt Color Shirt Amount Green 1 Green 3 Blue 1 Green 5 Yellow 7 Red 2 Yellow 1 Blue 4 Yellow 2 Color Code Red Blue 1 Green 2 Yellow 3 Shirt Color 2 2 1 2 3 3 1 3

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Running Example

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Shirt Color Shirt Amount Green 1 Green 3 Blue 1 Green 5 Yellow 7 Red 2 Yellow 1 Blue 4 Yellow 2 Shirt Color 2 2 1 2 3 3 1 3

Count the number of green shirts in the inventory

➊ ➋

Scan the color column for green (2) Aggregate amount where there is a match

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10 10 01 110 11010000 0000...

Traditional Scan Algorithm

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Column Data Compare Code (Green)

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Result BitVector 11

10 10

Shirt Color 2 (10) 2 (10) 1 (01) 2 (10) 3 (11) 0 (00) 3 (11) 1 (01) 3 (11)

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Vertical Layout

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Color c0 2 (10) c1 2 (10) c2 1 (01) c3 2 (10) c4 3 (11) c5 0 (00) c6 3 (11) c7 1 (01) c8 3 (11) c9 0 (00) word c0 c1 c2 c3 c4 c5 c6 c7 w0 1 1 1 1 1 w1 1 1 1 1 c8 c9 w2 1 w3 1 word c0 w0 1 w1 word c0 c1 w0 1 1 w1

110110110 00101011 10000000 ...

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1101

CPU BitWeaving Scan

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Column Data Compare Code Result BitVector

11111111 00000000 11010000 0000... 11011011 00101011 10000000 CPU width: 64-bits, up to 256-bit SIMD

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GPU BitWeaving Scan

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11011011 00101011 10000000

Column Data Compare Code Result BitVector

11111111 11111111 11111111 11010000 0000... GPU width: 16,384-bit SIMD

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GPU Scan Algorithm

▪ GPU uses very wide “words”

▪ CPU: 64-bits or 256-bits with SIMD ▪ GPU: 16,384 bits (256 lanes × 64-bits)

▪ Memory and caches optimized for bandwidth ▪ HSA programming model

▪ No data copies ▪ Low CPU-GPU interaction overhead

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Shirt Amount 1 3 1 5 7 2 1 4 2

1+3 1+3+5+...

CPU Aggregate Algorithm

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Result BitVector 11010000 0000... Result

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GPU Aggregate Algorithm

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Result BitVector 11010000 0000... Column Offsets

0,1 0,1,3,... On CPU

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GPU Aggregate Algorithm

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Column Offsets

0,1,3,...

Result

1+3+5+... On GPU

Shirt Amount 1 3 1 5 7 2 1 4 2

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Aggregate Algorithm

▪ Two phases

▪ Convert from BitVector to offsets (on CPU) ▪ Materialize data and compute (offload to GPU)

▪ Two group-by algorithms (see paper) ▪ HSA programming model

▪ Fine-grained sharing ▪ Can offload subset of computation

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Outline

▪ Background ▪ Algorithms ▪ Results

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Experimental Methods

▪ AMD A10-7850

▪ 4-core CPU ▪ 8-compute unit GPU ▪ 16GB capacity, 21 GB/s DDR3 memory ▪ Separate discrete GPU

▪ Watts-Up meter for full-system power ▪ TPC-H @ scale-factor 10

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Scan Performance & Energy

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Scan Performance & Energy

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Takeaway: Integrated GPU most efficient for scans

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TPC-H Queries

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Query 12 Performance

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TPC-H Queries

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Query 12 Performance Query 12 Energy Integrated GPU faster for both aggregate and scan computation

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TPC-H Queries

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Query 12 Performance Query 12 Energy

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TPC-H Queries

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Query 12 Performance Query 12 Energy More energy: Decrease in latency does not offset power increase Less energy: Decrease in latency AND decrease in power

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Future Die Stacked GPUs

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▪ 3D die stacking ▪ Same physical & logical integration ▪ Increased compute ▪ Increased bandwidth

Board

Power et al. Implications of 3D GPUs on the Scan Primitive SIGMOD Record. Volume 44, Issue 1. March 2015

DRAM CPU GPU

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Conclusions

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Discrete GPUs Integrated GPUs 3D Stacked GPUs

Performance

High ☺ Moderate High ☺

Memory Bandwidth

High ☺ Low ☹ High ☺

Overhead

High ☹ Low ☺ Low ☺

Memory Capacity

Low ☹ High ☺ Moderate

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?

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HSA vs CUDA/OpenCL

▪ HSA defines a heterogeneous architecture

▪ Cache coherence ▪ Shared virtual addresses ▪ Architected queuing ▪ Intermediate language

▪ CUDA/OpenCL are a level above HSA

▪ Come with baggage ▪ Not as flexible ▪ May not be able to take advantage of all features

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Scan Performance & Energy

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Group-by Algorithms

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All TPC-H Results

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Average TPC-H Results

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Average Performance Average Energy

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What’s Next?

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▪ Developing cost model for GPU

▪ Using the GPU is just another algorithm to choose ▪ Evaluate exactly when the GPU is more efficient

▪ Future “database machines”

▪ GPUs are a good tradeoff between specialization and commodity

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Conclusions

▪ Integrated GPUs viable for DBMS?

▪ Solve problems with discrete GPUs ▪ (Somewhat) better performance and energy

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▪ Looking toward the future...

▪ CPUs cannot keep up with bandwidth ▪ GPUs perfectly designed for these workloads