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README.md

README.md

mini-gunrock

This little project is the result of my study of moderngpu 2.0 and my attempt to create a minimalism-style graph processing library for the GPU. The reasons of creating this project are:

  • Gunrock main project is getting huge and it is difficult for new developers to get the core idea of our original purpose: giving graph processing library on the GPU both programmability and performance.
  • The integration with Multi-GPU and template-based design make it difficult to process micro-benchmarks for single workload mapping strategies and operations.
  • The Gunrock main project uses a mix of C++03, C++11, C99 and old-fashioned CUDA programming style, an alternative of refactoring such a large project is to build a small project with minimal components so that we can quickly try out new ideas.

Introduction

mini-gunrock's core components are graph traversal operators that based on moderngpu 2.0 transforms. The use of moderngpu 2.0 makes the code size small without losing performance. Here is a brief introduction of all the components in mini-gunrock.

  • graph_device_t contains CSR, CSC, and edge list presentations, it is the main data structure for storing our graph topology and node/edge values. It also contains auxiliary data, such as d_scanned_row_offsets, to be used by traversal operators.
  • frontier_t is the input and output for all traversal operators. It maps to Gunrock's frontier_queue using 1DArray.
  • problem_t is the base class for different graph problems. It contains a shared_ptr to graph_device_t called gslice. Each graph primitive will derive this class and define their own problem data structure which contains per-node/per-edge data.
  • filter and advance map to Gunrock's filter and advance operators. Underneath, they both use moderngpu 2.0's transforms (transform_compact for filter, and transform_scan + transform_lbs for advance). Currently I have implemented the baseline implementation which equals to Gunrock's LB strategy, the idempotence capability LB strategy, the dynamic group workload mapping strategy, and flexible uniquification filter. The direction optimal advance has also been implemented as in Ligra. I will gradually add more features as I explore more workload mapping strategies.
  • neighborhood reduce is a new operator enabled thanks to moderngpu 2.0's transform_segreduce operator. Different from a spmv where user always multiplies a vector with the whole matrix (in graph form, visits all the nodes neighbors), this operator takes dynamic generated input frontier, visits only these nodes' neighbors, and reduce over each neighborhood according to user-specific reduce operator by two functors: get_value_to_reduce and write_reduce_value.

mini/gunrock/tests/bfs/test_bfs.cu shows the power of mini-gunrock. After loading graph and setting up frontier and problem, the actual algorithm part only contains 8 lines of code. It is a truly data-centric framework and basically achieved our original idea of "the flow of frontier between multiple operators".

TODOS (with no perticular orders)

  • Add a pure compute operator with no filter (maps to Gunrock's bypass_filter).
  • Add launch_box and restrict settings to further improve the performance.
  • Add the batch-intersection operator.
  • Add more graph primitives and their CPU validation code.

To Gunrock Team

As I'm still actively working on this project, you will see more changes in the coming up months, please try it out, provide feedbacks, and contribute to the code if you want. Hope this project can serve as both a quick experiment playground for new ideas and also a better way to get familiar with our data-centric framework.

YZH