xmyqsh / gpu_coords_map

Multi-thread Multi-Head GPU Coordinates Mapping with Shared SLAB Router and Memory

What's news?

• Light Head: the table head has been shrinked 32 times.

• Multi-Head: one slab router and memory, multi table head.

• Singleton: reuse slab router and memory by making SlabAlloc singleton, prevent allocating and releasing large GPU memory frequently.

• Random Ring Hash: one random number per table head as the memory block offset to make the worse space usage best and uniform.

• Compact slab memory layout design: support any dim of coordinate with high gpu memory usage(around 50% ~ 100%), without lossing speed.

• Bug free: No insertion while deletion bug(due to read and write sequence in lock-free logic) in origin SlabHash(Saman Ashkiani version). No insertion bug(do not support duplicate insertion due to lock-free logic or wrap programing logic) and Remove bug(due to wrap programing logic) in GPU CoordinateHash(Wei Dong version).

Usage example:

more details in test_unique_with_remove_multithread_with_query_coords.cu

int main() {
  // stress test
  for (int j = 0; ; ++j) {
    std::cout << "@@@@@@@@@@@@@@ j: " << j << std::endl;

    std::vector<std::thread> vt;
    vt.reserve(4);
    for (int i = 0; i != 4; ++i) {
        vt.emplace_back(std::thread([i] { TEST_COORDS(2400000*2, i); std::cout << "Finish " << i << "th TEST_COORDS" << std::endl; }));
    }

    for (int i = 0; i != 4; ++i) {
        vt[i].join();
    }

    sleep(1);

  }
}

TODO

1. General improvment: [Easy]

• move GPU Memory configuration into template
• change pass-by-value to pass-by-pointer for Key
• support any Value type internelly(only support int currently).

2. Custom it to specific usage:

• custom kernel
• custom memory handling

Best Practice

Features supported by embedded it into MinkowskiEngine

• Mapping As Indices
• Iteration As Insertion
• Insertion As Search
• Accelerate any sparse, including query-ball in pointcloud and pv-rcnn.

Acknowledge

• cuda_unordered_map
• SlabHash

• Saman Ashkiani, Martin Farach-Colton, John Owens, A Dynamic Hash Table for the GPU, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)