Efficient 2-Body Statistics Computation on GPUs: Parallelization & Beyond

Various types of two-body statistics (2-BS) are regarded as essential components of data analysis in many scientific and computing domains. Due to the quadratic time complexity, use of modern parallel hardware has become an obvious direction for research and practice in 2-BS computation. This paper presents our recent work in designing and optimizing parallel algorithms for 2-BS computation on Graphics Processing Units (GPUs). First, we classify 2-body applications into three groups based on their data output pattern. Then, we introduce a straightforward parallel algorithm under the CUDA framework. To that end, we split the algorithm into two stages: pairwise distance function computation and writing output. Then, we present modifications to the basic algorithm by integrating various techniques at each stage. Our algorithms design focuses on effective use of hardware/software features that are unique in GPU platforms. Experiments run on modern GPU hardware show that our GPU algorithms outperform the best known CPU program by at least an order of magnitude in various applications. Furthermore, our implementation achieves very high level of GPU resource utilization, indicating near-optimal performance. This work builds a solid foundation towards realizing our vision of a framework that can automatically generate optimized code for any new 2-BS problems.