Depth First Search (DFS) is a fundamental graph traversal algorithm with broad applications. While existing work-stealing DFS approaches achieve strong performance on CPUs, mapping them to modern GPUs faces three major challenges: (1) limited shared memory cannot accommodate deep stacks, (2) frequent stack operations hinder efficient intra-block execution, and (3) irregularworkloads complicate scalable inter-block execution. In this paper, we propose DiggerBees, a GPU-optimized parallel DFS algorithm with hierarchical block-level stealing, consisting of three components. First, we introduce a twolevel stack structure to mitigate shared memory limitations. Second, we employ warp-level DFS with intra-block work stealing to enable efficient execution within a block. Third, we implement inter-block work stealing to achieve scalable execution across blocks and sustain high parallelism. Experimental results on the latest NVIDIA GPUs show that DiggerBees outperforms existing DFS approaches, CKL-PDFS, ACR-PDFS, and NVG-DFS, achieving average speedups of 1.37x, 1.83x, and 30.18x, respectively. Moreover, DiggerBees even surpasses high-performance GPU BFS implementations on graphs with deep and narrow traversal paths, and scales efficiently across GPU generations.
With the exponential growth of computing power, large-scale scientific simulations are producing massive volumes of data, leading to critical storage and I/O challenges. Error-bounded lossy compression has become one of the most effective solutions for reducing data size while preserving accuracy. Leveraging GPU is increasingly essential for high-performance compression on such large datasets, but GPU-based lossy compressors usually support only single-precision decompression, limiting their ability to meet the diverse accuracy requirements of scientific workflows. Progressive compressors can address this limitation by enabling on-demand precision retrieval. However, existing progressive lossy compressors on GPU still suffer from low throughput. To overcome these challenges, we present PRISM, a GPU-based progressive lossy compressor that achieves both high throughput and multi-precision retrieval, which introduces a high performance progressive framework that integrates the multiple interpolation predictors, efficient bitplane extraction, and an enhanced lossless compression that combines sign-absolute coding with zero-aware parallel algorithms. Evaluations on representative real-world datasets from five scientific domains show that PRISM significantly outperforms state-of-the-art progressive compressors on GPU, reducing retrieval data volume by over 15.6x and achieving up to 20.1x higher throughput on the NVIDIA H100 GPU under the same error bounds.
Sparse matrix-sparse vector multiplication (SpMSpV) is a core primitive in graph analytics and scientific computing, also arising in spiking neural networks for event-driven spike propagation. On GPUs, the performance of the prevalent and efficient SpMSpV paradigm is often bottlenecked by the write-back phase of accumulating non-zero multiply-accumulate results; its many-to-one index scatter pattern causes severe conflicts and poor bandwidth utilization on GPUs. We present VDHA, a GPU-based weighted SpMSpV kernel that leverages block-private hash tables for local aggregation, substantially reducing write conflicts and improving memory coalescing. To further amplify this benefit, we incorporate column splitting with lightweight reordering to expose more locality, and employ a fetch-computewriteback pipeline to overlap hash computation with memory accesses. Extensive evaluation on over 300 matrices with more than 5 million nonzeros, including web-scale graphs (Konect/LAW) and scientific workloads (SuiteSparse), shows that VDHA consistently outperforms state-of-the-art baselines. On web graphs, it achieves a 1.41x geometric-mean speedup (up to 3.42x), while on SuiteSparse it delivers 1.13x (up to 2.55x). We also provide a lightweight predictive model that identifies matrices favorable to VDHA with 91.3% accuracy.
Space-partitioning indexes are widely used for managing multi-dimensional data, but their throughput is often memorybottlenecked. Processing-in-memory (PIM), an emerging architectural paradigm, mitigates memory bottlenecks by embedding processing cores directly within memory modules, allowing computation to be offloaded to these PIM cores. In this paper, we present PIM-zd-tree, the first spacepartitioning index specifically designed for real-world PIM systems. PIM-zd-tree employs a tunable multi-layer structure, with each layer adopting distinct data layouts, partitioning schemes, and caching strategies. Its design is theoretically grounded to achieve load balance, minimal memory-channel communication, and low space overhead. To bridge theory and practice, we incorporate implementation techniques such as practical chunking and lazy counters. Evaluation on a real-world PIM system shows that PIM-zd-tree's throughput is up to 4.25x and 99x higher than two state-of-the-art shared-memory baselines.
Diffusion models have become the dominant approach for generative tasks in images, videos, and other domains. However, diverse data properties in generation requests, which are critical for efficient serving, remain underexploited. To address this issue, we propose a diffusion model serving system ChituDiffusion. ChituDiffusion leverages the locality of data properties to recompose a diffusion pipeline into subgraphs with shared optimization opportunities, enabling thorough compile-time and runtime co-optimizations. During compilation, ChituDiffusion compiles each subgraph into multiple execution engines optimized for specific data properties. At runtime, heterogeneous requests are elaborately reorganized into fine-grained batching tasks with similar properties and then efficiently executed by matched engines. Evaluation on five diffusion applications shows that ChituDiffusion improves the throughput by up to 2.13x (1.58x on average) on A100 and 2.19x (1.51x on average) on H100 compared with existing frameworks. The code for ChituDiffusion and the production traces have been made open-source at https://github.com/thu- pacman/chitu/tree/ Diffusion.
