WARPCORE

Table of Contents

• Introduction
• Development Status
• Requirements
• Dependencies
• Where to go from here?

Hashing at the speed of light on modern CUDA-accelerators

Introduction

warpcore is a framework for creating high-throughput, purpose-built hashing data structures on CUDA-accelerators.

This library provides the following data structures:

• warpcore::SingleValueHashTable: stores a set of key-value pairs
• warpcore::HashSet: stores a set of keys
• warpcore::CountingHashTable: keeps track of the number of occurrences of each inserted key
• warpcore::BloomFilter: pattern-blocked bloom filter for approximate membership queries
• warpcore::MultiValueHashTable: stores a multi-set of key-value pairs
• warpcore::BucketListHashTable: alternative variant of warpcore::MultiValueHashTable
• warpcore::MultiBucketHashTable: alternative variant of warpcore::MultiValueHashTable

Implementations support key types std::uint32_t and std::uint64_t together with any trivially copyable value type. In order to be adaptable to a wide range of possible usecases, we provide a multitude of combinable modules such as hash functions, probing schemes, and data layouts.

warpcore has won the best paper award at the IEEE HiPC 2020 conference (link to manuscript)(link to preprint) and is based on our previous work on massively parallel GPU hash tables warpdrive which has been published in the prestigious IEEE IPDPS conference (link to manuscript).

Development Status

This library is still under heavy development. Users should expect breaking changes and refactoring to be common. Developement mainly takes place on our in-house GitLab instance. However, we plan to migrate to GitHub in the near future.

Requirements

• CUDA-capable device with device architecture 6.0 or higher (Pascal+; see link)
• NVIDIA CUDA toolkit/compiler version ≥ v11.2
• C++14 or higher

Dependencies

• hpc_helpers - utils, timers, etc.
• kiss_rng - a fast and lightweight GPU PRNG
• CUB - high-throughput primitives for GPUs (already included in newer versions of the CUDA toolkit, i.e., ≥ v10.2)

Note: Dependencies are automatically managed via CMake.

Getting warpcore

warpcore is header only and can be incorporated manually into your project by downloading the headers and placing them into your source tree.

Adding warpcore to a CMake Project

warpcore is designed to make it easy to include within another CMake project. The CMakeLists.txt exports a warpcore target that can be linked ¹ into a target to setup include directories, dependencies, and compile flags necessary to use warpcore in your project.

We recommend using CMake to fetch warpcore into your project.

cmake_minimum_required(VERSION 3.18 FATAL_ERROR)
 
include(FetchContent)
FetchContent_Declare(
  warpcore
  GIT_REPOSITORY https://github.com/sleeepyjack/warpcore.git
  GIT_TAG XXXXX
)
 
FetchContent_MakeAvailable(warpcore)
 
target_link_libraries(my_target warpcore)

This will take care of downloading warpcore from GitHub and making the headers available in a location that can be found by CMake. Linking against the warpcore target will provide everything needed for warpcore to be used by my_target.

1: warpcore is header-only and therefore there is no binary component to "link" against. The linking terminology comes from CMake's target_link_libraries which is still used even for header-only library targets.

Building warpcore

Since warpcore is header-only, there is nothing to build to use it.

To build the tests, benchmarks, and examples:

cd $WARPCORE_ROOT
cmake -S . -B build
cmake --build build -DWARPCORE_BUILD_TESTS=ON -DDWARPCORE_BUILD_BENCHMARKS=ON -DDWARPCORE_BUILD_EXAMPLES=ON

Binaries will be built into:

• build/tests/
• build/benchmarks/
• build/examples/

Where to go from here?

Take a look at the examples, test your own system performance using the benchmark suite and be sure everything works as expected by running the test suite.

How to cite warpcore ?

BibTeX:

@inproceedings{DBLP:conf/hipc/JungerKM0XLS20,
  author    = {Daniel J{\"{u}}nger and
               Robin Kobus and
               Andr{\'{e}} M{\"{u}}ller and
               Christian Hundt and
               Kai Xu and
               Weiguo Liu and
               Bertil Schmidt},
  title     = {WarpCore: {A} Library for fast Hash Tables on GPUs},
  booktitle = {27th {IEEE} International Conference on High Performance Computing,
               Data, and Analytics, HiPC 2020, Pune, India, December 16-19, 2020},
  pages     = {11--20},
  publisher = {{IEEE}},
  year      = {2020},
  url       = {https://doi.org/10.1109/HiPC50609.2020.00015},
  doi       = {10.1109/HiPC50609.2020.00015},
  timestamp = {Wed, 05 May 2021 09:45:30 +0200},
  biburl    = {https://dblp.org/rec/conf/hipc/JungerKM0XLS20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
 
@inproceedings{DBLP:conf/ipps/Junger0S18,
  author    = {Daniel J{\"{u}}nger and
               Christian Hundt and
               Bertil Schmidt},
  title     = {WarpDrive: Massively Parallel Hashing on Multi-GPU Nodes},
  booktitle = {2018 {IEEE} International Parallel and Distributed Processing Symposium,
               {IPDPS} 2018, Vancouver, BC, Canada, May 21-25, 2018},
  pages     = {441--450},
  publisher = {{IEEE} Computer Society},
  year      = {2018},
  url       = {https://doi.org/10.1109/IPDPS.2018.00054},
  doi       = {10.1109/IPDPS.2018.00054},
  timestamp = {Sat, 19 Oct 2019 20:31:38 +0200},
  biburl    = {https://dblp.org/rec/conf/ipps/Junger0S18.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

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warpcore Copyright (C) 2018-2021 Daniel Jünger
modified by Markus Vieth

This program comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under certain conditions. See the file [LICENSE](LICENSE) for details.