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To alleviate bottlenecks in storing and accessing data on high-performance computing (HPC) systems, I/O libraries are enabling computation while data is in-transit, such as HDFS filters. For scientific applications that commonly use floating-point data, error-bounded lossy compression methods are a critical technique to significantly reduce the storage and bandwidth requirements. Thus far, deciding when and where to schedule in-transit data transformations, such as compression, has been outside the scope of I/O libraries. In this paper, we introduce Runway, a runtime framework that enables computation on in-transit data with an object storage abstraction. Runway is designed to be extensible to execute user-defined functions at runtime. In this effort, we focus on studying methods to offload data compression operations to available processing units based on latency and throughput. We compare the performance of running compression on multi-core CPUs, as well as offloading it to a GPU and a Data Processing Unit (DPU). We implement a state-of-the-art error-bounded lossy compression algorithm, SZ3, as a Runway function with a variant optimized for DPUs. We propose dynamic modeling to guide scheduling decisions for in-transit data compression. We evaluate Runway using four scientific datasets from the SDRBench benchmark suite on a the Perlmutter supercomputer at NERSC. 

Parallel I/O is an effective method to optimize data movement between memory and storage for many scientific applications. Poor performance of traditional disk-based file systems has led to the design of I/O libraries which take advantage of faster memory layers, such as on-node memory, present in high-performance computing (HPC) systems. By allowing caching and prefetching of data for applications alternating computation and I/O phases, a faster memory layer also provides opportunities for hiding the latency of I/O phases by overlapping them with computation phases, a technique called asynchronous I/O. Since asynchronous parallel I/O in HPC systems is still in the initial stages of development, there hasn't been a systematic study of the factors affecting its performance.In this paper, we perform a systematic study of various factors affecting the performance and efficacy of asynchronous I/O, we develop a performance model to estimate the aggregate I/O bandwidth achievable by iterative applications using synchronous and asynchronous I/O based on past observations, and we evaluate the performance of the recently developed asynchronous I/O feature of a parallel I/O library (HDF5) using benchmarks and real-world science applications. Our study covers parallel file systems on two large-scale HPC systems: Summit and Cori, the former with a GPFS storage and the latter with a Lustre parallel file system. 

Lossy compression is one of the most efficient solutions to reduce storage overhead and improve I/O performance for HPC applications. However, existing parallel I/O libraries cannot fully utilize lossy compression to accelerate parallel write due to the lack of deep understanding on compression-write performance. To this end, we propose to deeply integrate predictive lossy compression with HDF5 to significantly improve the parallel-write performance. Specifically, we propose analytical models to predict the time of compression and parallel write before the actual compression to enable compression-write overlapping. We also introduce an extra space in the process to handle possible data overflows resulting from prediction uncertainty in compression ratios. Moreover, we propose an optimization to reorder the compression tasks to increase the overlapping efficiency. Experiments with up to 4,096 cores from Summit show that our solution improves the write performance by up to 4.5× and 2.9× over the non-compression and lossy compression solutions, respectively, with only 1.5% storage overhead (compared to original data) on two real-world HPC applications. 

Error-bounded lossy compression is one of the most effective techniques for reducing scientific data sizes. However, the traditional trial-and-error approach used to configure lossy compressors for finding the optimal trade-off between reconstructed data quality and compression ratio is prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on the prediction-based lossy compression framework, which can effectively foresee the reduced data quality and compression ratio, as well as the impact of lossy compressed data on post-hoc analysis quality. Our analytical model significantly improves the prediction-based lossy compression in three use-cases: (1) optimization of predictor by selecting the best-fit predictor; (2) memory compression with a target ratio; and (3) in-situ compression optimization by fine-grained tuning error-bounds for various data partitions. We evaluate our analytical model on 10 scientific datasets, demonstrating its high accuracy (93.47% accuracy on average) and low computational cost (up to 18.7× lower than the trial-and-error approach) for estimating the compression ratio and the impact of lossy compression on post-hoc analysis quality. We also verify the high efficiency of our ratio-quality model using different applications across the three use-cases. In addition, our experiment demonstrates that our modeling-based approach reduces the time to store the 3D RTM data with HDF5 by up to 3.4× with 128 CPU cores over the traditional solution.