Presentation
HP-MDR: High-Performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs
DescriptionScientific applications produce vast amounts of data, posing grand challenges for data management and analytics. Progressive compression is an approach to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. This work proposes HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are threefold: (1) we optimize the bit-plane encoding and lossless encoding to achieve high performance on GPUs; (2) we propose pipeline optimization to further enhance the performance for large data processing; (3) we leverage our framework to enable data retrieval with guaranteed error control for quantities-of-interest; (4) we evaluate HP-MDR using five datasets. Evaluations demonstrate HP-MDR achieves 13.68x and 6.31x average throughput improvement for refactoring and progressive retrieval, respectively. It also leads to 11.22x throughput for recomposing data under quantity-of-interest error control and 6.04x performance for the corresponding end-to-end data retrieval compared with state-of-the-art solutions.
Event Type
Paper
TimeThursday, 20 November 20254:36pm - 4:58pm CST
Location261-262-265-266
Data Analytics, Visualization & Storage