The Crystal library implements a collection of block-wide device functions that can be used to implement high performance implementations of SQL queries on GPUs.
The package contains:
- Crystal:
crystal/contains the block-wide device functions - Implementations:
src/contains SQL query operator implementations and implementations of 13 queries of the Star Schema Benchmark
For full details of the Crystal, see our paper
@inproceedings{shanbhag2020crystal,
author = {Shanbhag, Anil and Madden, Samuel and Yu, Xiangyao},
title = {A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics},
year = {2020},
url = {https://doi.org/10.1145/3318464.3380595},
doi = {10.1145/3318464.3380595},
booktitle = {Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data},
pages = {1617–1632},
numpages = {16},
location = {Portland, OR, USA},
series = {SIGMOD ’20}
}
To use Crystal:
- Copy out the
crystaldirectory into your project. - Include Crystal
#include "crystal/crystal.cuh"
- Add the crystal directory to your include path
To run the operator implementations:
- Compile and run the operator. E.g.,
make bin/ops/project
./bin/ops/project
To run the Star Schema Benchmark implementation:
- Generate the test dataset
cd test/
# Generate the test generator / transformer binaries
cd ssb/dbgen
make
cd ../loader
make
cd ../../
# Generate the test data and transform into columnar layout
# Substitute <SF> with appropriate scale factor (eg: 1)
python util.py ssb <SF> gen
python util.py ssb <SF> transform
- Configure the benchmark settings
cd src/ssb/
# Edit SF and BASE_PATH in ssb_utils.h
- To run a query, say run q11
make bin/ssb/q11
./bin/ssb/q11
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