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Distributed Join Project

Overview

This proof-of-concept repo implements the distributed repartitioned join algorithm. The algorithm consists of three steps:

  1. Hash partition: reorder input tables into partitions based on the hash values of the key columns.
  2. All-to-all communication: send each partition to the corresponding MPI rank so that rows with the same hash values end up in the same rank.
  3. Local join: each MPI rank performs local join independently.

For more information about the algorithm used and optimizations, please refer to the ADMS'21 paper and the presentatiton.

For production-quality distributed join implementation, checkout cuDF's Dask integration.

The following plot shows the weak-scaling performance when joining the l_orderkey column from lineitem table with the o_orderkey and the o_orderpriority columns from the orders table on TPC-H dataset with SF100k.

weak scaling performance

Compilation

This project depends on CUDA, UCX, NCCL, MPI, cuDF 0.19 and nvcomp 2.0.

To compile, make sure the variables CUDA_ROOT, CUDF_ROOT, MPI_ROOT, UCX_ROOT, NCCL_ROOT and NVCOMP_ROOT are pointing to the installation path of CUDA, cuDF, MPI, UCX, NCCL and nvcomp repectively.

The wiki page contains step-by-step instructions for setting up the environment.

To compile, run

mkdir build && cd build
cmake ..
make -j

Running

To run on systems not needing Infiniband (e.g. single-node DGX-2):

UCX_MEMTYPE_CACHE=n UCX_TLS=sm,cuda_copy,cuda_ipc mpirun -n 16 --cpus-per-rank 3 bin/benchmark/distributed_join

On systems needing Infiniband communication (e.g. single or multi-node DGX-1Vs):

  • GPU-NIC affinity is critical on systems with multiple GPUs and NICs, please refer to this page from QUDA for more detailed info. Also, you could modify run script included in the benchmark folder.
  • Depending on whether you're running with srun or mpirun, update run_sample.sh to set lrank to $SLURM_LOCALID or $OMPI_COMM_WORLD_LOCAL_RANK correspondingly.

Example run on a single DGX-1V (all 8 GPUs):

$ mpirun -n 8 --bind-to none --mca btl ^openib,smcuda benchmark/run_sample.sh
rank 0 gpu list 0,1,2,3,4,5,6,7 cpu bind 1-4 ndev mlx5_0:1
rank 1 gpu list 0,1,2,3,4,5,6,7 cpu bind 5-8 ndev mlx5_0:1
rank 2 gpu list 0,1,2,3,4,5,6,7 cpu bind 10-13 ndev mlx5_1:1
rank 3 gpu list 0,1,2,3,4,5,6,7 cpu bind 15-18 ndev mlx5_1:1
rank 4 gpu list 0,1,2,3,4,5,6,7 cpu bind 21-24 ndev mlx5_2:1
rank 6 gpu list 0,1,2,3,4,5,6,7 cpu bind 30-33 ndev mlx5_3:1
rank 7 gpu list 0,1,2,3,4,5,6,7 cpu bind 35-38 ndev mlx5_3:1
rank 5 gpu list 0,1,2,3,4,5,6,7 cpu bind 25-28 ndev mlx5_2:1
Device count: 8
Rank 4 select 4/8 GPU
Device count: 8
Rank 5 select 5/8 GPU
Device count: 8
Rank 3 select 3/8 GPU
Device count: 8
Rank 7 select 7/8 GPU
Device count: 8
Rank 0 select 0/8 GPU
Device count: 8
Rank 1 select 1/8 GPU
Device count: 8
Rank 2 select 2/8 GPU
Device count: 8
Rank 6 select 6/8 GPU
========== Parameters ==========
Key type: int64_t
Payload type: int64_t
Number of rows in the build table: 800 million
Number of rows in the probe table: 800 million
Selectivity: 0.3
Keys in build table are unique: true
Over-decomposition factor: 1
Communicator: UCX
Registration method: preregistered
Compression: false
================================
Elasped time (s) 0.392133

For the arguments accepted by each benchmark, please refer to the source files in the benchmark folder.

Code formatting

This repo uses clang-format for code formatting. To format the code, make sure clang-format is installed and run

./run-clang-format.py -p <path to clang-format>