2010/04/08 by Z. Jin, Zhefan Jin, Jin, Zhefan +11
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Computer Graphics and Visualization Techniques #Data Visualization and Analytics #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #astro-ph.IM
paper · pdf · doi:10.48550/arxiv.1004.1302
10 pages, accepted for publication at ICCS 2010 conference
arxiv created 2010/04/08 · openalex publication_date 2010/04/08 · arxiv updated 2010/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The scientific community is presently witnessing an unprecedented growth in the quality and quantity of data sets coming from simulations and real-world experiments. To access effectively and extract the scientific content of such large-scale data sets (often sizes are measured in hundreds or even millions of Gigabytes) appropriate tools are needed. Visual data exploration and discovery is a robust approach for rapidly and intuitively inspecting large-scale data sets, e.g. for identifying new features and patterns or isolating small regions of interest within which to apply time-consuming algorithms. This paper presents a high performance parallelized implementation of Splotch, our previously developed visual data exploration and discovery algorithm for large-scale astrophysical data sets coming from particle-based simulations. Splotch has been improved in order to exploit modern massively parallel architectures, e.g. multicore CPUs and CUDA-enabled GPUs. We present performance and scalability benchmarks on a number of test cases, demonstrating the ability of our high performance parallelized Splotch to handle efficiently large-scale data sets, such as the outputs of the Millennium II simulation, the largest cosmological simulation ever performed.