Acquiring data from scientific simulations for analytical purposes is inherently challenging due to the complex and irregularly shaped regions within which the data resides, particularly when using self-describing data formats. The process of region-based data distillation becomes even more arduous when employing persistent memory or parallel file systems. To tackle this challenge, we introduce RASTER (Region-Aware Self-describing daTa optimizER), a lightweight middleware designed for region-aware data preprocessing. RASTER dynamically reorganizes data into variable groups based on regional identifiers during runtime, thereby eliminating the need for sequential searches to locate the required data. We have developed a prototype of RASTER and successfully integrated it into three distinct computing environments: a single-node server equipped with Intel® Optane™ DC persistent memory, the Huawei® OceanStor cloud storage platform, and the Sunway TaihuLight supercomputer. We then conducted a thorough evaluation of the RASTER prototype on the latter two platforms using a real-world scientific application, CESM (Community Earth System Model). Our experimental results demonstrate that RASTER enhances data acquisition performance by up to 2.83× and achieves a 2.36× speedup over conventional netCDF and the state-of-the-art ADIOS2. Additionally, RASTER significantly reduces memory usage by up to 400%, showcasing its scalability potential.

We introduce Portus, an efficient checkpointing system for DNN models. The core of Portus is a three-level index structure and a direct RDMA datapath that enables fast check-points between GPUs and persistent memory in a serialization-free way. Portus offers a zero-copy approach between GPU and persistent memory without involving main memory and kernel crossings to underlying file systems. Portus also applies an asynchronous mechanism to hide the checkpointing overhead in the model training procedures. We integrated a Portus prototype into a high-performance AI cluster with NVIDIA®V100 and A40 GPUs and Intel®Optane™persistent memory, then evaluated its performance in both single-GPU and multi-GPU large model training scenarios. Experiment results show that compared to a state-of-the-art checkpointing system, Portus achieves up to 9.23× and 7.0× speedup in checkpointing and restoring, respectively. Portus achieves up to 2.6× higher throughput and 8× faster checkpointing operation on a large language model, GPT-22B.

Memory-Centric X-ray Computational Tomography(CT) is an iterative reconstruction technique that trades compute simplifications with higher memory accesses. MemXCT implements a sparse matrix-vector multiplication(SpMV) with multi-stage buffering and two-level pseudo-Hilbert ordering for optimization. Motivated by the need to validate conclusions from previous work, we reproduce the numerical results, the algorithm’s performance, and the scaling behavior of the algorithms as the number of MPI processes increases on Azure. Digital artifacts from these experiments are available at: 10.5281/zenodo.5598108