High-Throughput Chunk-Based GPU Bitmap Compression for Dense Retrieval Workloads

Compressed bitmap indexes are a means of executing queries efficiently while reducing memory usage. However, most existing bitmap formats are designed for CPUs and exhibit irregular control flow and memory access patterns that limit performance on GPUs. This paper presents HiPES, a GPU-native, chunk-based compressed bitmap scheme. Experimental results show that HiPES can achieve an 8 × increase in compression throughput relative to GPU-WAH in dense workloads, while maintaining competitive compression ratios. In sparse workloads, the efficiency degrades due to internal fragmentation, associated with the fixed-size chunks layout. When the chunk payload bit-width (w) is aligned with shared memory, the throughput is maximized. Additionally, for values of w around 16, we reach a balance in terms of efficiency in which both metadata overhead and internal fragmentation are minimized. This work establishes a basis for GPU-resident computational frameworks, allowing for the elimination of the overhead associated with decompression in high-scale filtering, Graph Neural Networks (GNN) pre-processing, and in-situ scientific analytics.