The n-body problem involves calculating the effect of bodies on each other. n-body simulations are ubiquitous in the fields of physics and astronomy and notoriously computationally expensive. The naïve algorithm for n-body simulations has the prohibiting O(n2) time complexity. Reducing the time complexity to O(n · lg(n)), the tree-based Barnes-Hut algorithm approximates the effect of bodies beyond a certain threshold distance. Other than algorithmic improvements, extensive research has gone into accelerating n-body simulations on GPUs and multi-core systems. However, Barnes-Hut is a tree-traversal algorithm, which makes it a poor target for acceleration using traditional GPU shader cores. In contrast, recent work shows that, for tree-based computations, GPU ray-tracing (RT) cores dominate shader cores. In this work, we reformulate the Barnes-Hut algorithm as a ray-tracing problem and implement it with NVIDIA OptiX. Our evaluation shows that the resulting system, RT-BarnesHut, outperforms current state-of-the-art GPU-based implementations.

High-performance implementations of k-Nearest Neighbor Search (kNN) in low dimensions use tree-based data structures. Tree algorithms are hard to parallelize on GPUs due to their irregularity. However, newer Nvidia GPUs offer hardware support for tree operations through ray-tracing cores. Recent works have proposed using RT cores to implement kNN search, but they all have a hardware-imposed constraint on the distance metric used in the search—the Euclidean distance. We propose and implement two reductions to support kNN for a broad range of distances other than the Euclidean distance: Arkade Filter-Refine and Arkade Monotone Transformation, each of which allows non-Euclidean distance-based nearest neighbor queries to be performed in terms of the Euclidean distance. With our reductions, we observe that kNN search time speedups range between 1.6x-200x and 1.3x-33.1x over various state-of-the-art GPU shader core and RT core baselines, respectively. In evaluation, we provide several insights on RT architectures’ ability to efficiently build and traverse the tree by analyzing the kNN search time trends.

The problem of identifying the k-Nearest Neighbors (kNNS) of a point has proven to be very useful both as a standalone application and as a subroutine in larger applications. Given its far-reaching applicability in areas such as machine learning and point clouds, extensive research has gone into leveraging GPU acceleration to solve this problem. Recent work has shown that using Ray Tracing cores in recent GPUs to accelerate kNNS is much more efficient compared to traditional acceleration using shader cores. However, the existing translation of kNNS to a ray tracing problem imposes a constraint on the search space for neighbors. Due to this, we can only use RT cores to accelerate fixed-radius kNNS, which requires the user to set a search radius a priori and hence can miss neighbors. In this work, we propose TrueKNN, the first unbounded RT-accelerated neighbor search. TrueKNN adopts an iterative approach where we incrementally grow the search space until all points have found their k neighbors. We show that our approach is orders of magnitude faster than existing approaches and can even be used to accelerate fixed-radius neighbor searches.

General Purpose computing on Graphical Processing Units (GPGPU) has resulted in unprecedented levels of speedup over its CPU counterparts, allowing programmers to harness the computational power of GPU shader cores to accelerate other computing applications. But this style of acceleration is best suited for regular computations (e.g., linear algebra). Recent GPUs feature new Ray Tracing (RT) cores that instead speed up the irregular process of ray tracing using Bounding Volume Hierarchies. While these cores seem limited in functionality, they can be used to accelerate n-body problems by leveraging RT cores to accelerate the required distance computations. In this work, we propose RT-DBSCAN, the first RT-accelerated DBSCAN implementation. We use RT cores to accelerate Density-Based Clustering of Applications with Noise (DBSCAN) by translating fixed-radius nearest neighbor queries to ray tracing queries. We show that leveraging the RT hardware results in speedups between 1.3x to 4x over current state-of-the-art, GPU-based DBSCAN implementations.