CGA: Accelerating BFS through an Sparsity-Aware Adaptive Framework on Heterogeneous Platforms

Direction optimization determines whether to use Sparse Matrix-Sparse Vector Multiplication (SpMSpV) or Sparse Matrix-Dense Vector Multiplication (SpMV) based on the input vector's sparsity at each iteration of Breadth-First Search (BFS), aiming to achieve the fastest graph traversal. Although prior work on direction optimization has achieved state-of-the-art performance on either CPUs or GPUs, it has not fully leveraged the capabilities of modern heterogeneous platforms. This is because SpMSpV/SpMV execution times on GPUs do not consistently outperform those on CPUs, particularly for SpMSpV. In response, this paper introduces CGA, a machine learning-based adaptive framework for BFS that optimally selects between CPU and GPU kernels, effectively Adapting to diverse real-world graphs, vectors, and computing platforms. Our contributions include a novel set of bucket-based SpMSpV algorithms that significantly enhance kernel performance inhigh-sparsity scenarios, along with a low-overhead decision tree model and reduced CPU-GPU data transfers. Experimental results show that our framework outperforms previous state-of-the-art methods, achieving up to a 4.91x speedup over CPU-only baseline and 3.27x speedup over GPU-only baseline.