oa GPU Acceleration of Graph Algorithms in NextVision: A Seismic Data Interpretation Tool

We focus on accelerating graph processing for seismic data interpretation using GPUs, particularly through the optimization of the Breadth First Search (BFS) algorithm. Seismic interpretation tools like NextVision require processing large graphs, which is traditionally compute-intensive. Initially parallelized on multicore CPUs, the code was slow for large datasets. To improve performance, we implemented GPU acceleration using NVIDIA’s cuGraph library.

The approach involved optimizing the BFS algorithm by launching multiple concurrent searches from independent vertices, maximizing parallelism and reducing overhead. Graph data was efficiently managed using RAPIDS Memory Manager (RMM), and the cuGraph API enabled efficient graph creation and BFS execution without redundant data replication. CUDA multi-streaming was also optimized to improve GPU utilization. Additionally, THRUST API was used to handle dynamic graph updates efficiently.

Experimental results showed significant performance improvements, particularly on larger graphs. The performance platform with an A100 GPU achieved speedups of up to 4.95x for a 44GB graph. Future work will explore additional algorithms, like FastAPSP, to further enhance performance. This study demonstrates the potential of GPU acceleration to significantly speed up graph algorithms in seismic data interpretation, benefiting geophysical analysis.