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Scientific research and development campaigns are materialized by workflows of applications executing on high-performance computing (HPC) systems. These applications con-sist of tasks that can have inter- or intra-application flows of data to achieve the research goals successfully. These dataflows create dependencies among the tasks and cause resource con-tention on shared storage systems, thus limiting the aggregated I/O bandwidth achieved by the workflow. However, these I/O performance issues are often solved by tedious and manual efforts that demand holistic knowledge about the data dependencies in the workflow and the information about the infrastructure being utilized. Taking this into consideration, we design DFMan, a graph-based dataflow management and optimization framework for maximizing I/O bandwidth by leveraging the powerful storage stack on HPC systems to manage data sharing optimally among the tasks in the workflows. In particular, we devise a graph-based optimization algorithm that can leverage an intuitive graph representation of dataflow- and system-related information, and automatically carry out co-scheduling of task and data placement. According to our experiments, DFMan optimizes a wide variety of scientific workflows such as Hurricane 3D on Cloud Model 1 (CM1), Montage Carina Nebula (NGC3372), and an emulated dataflow kernel of the Multiscale Machine-learned Modeling Infrastructure (MuMMI I/O) on the Lassen supercomputer, and improves their aggregated I/O bandwidth by up to 5.42 x, 2.12 x and 1.29 x, respectively, compared to the baseline bandwidth. 

With the emergence of versatile storage systems, multi-level checkpointing (MLC) has become a common approach to gain efficiency. However, multi-level checkpoint/restart can cause enormous I/O traffic on HPC systems. To use multilevel checkpointing efficiently, it is important to optimize checkpoint/restart configurations. Current approaches, namely modeling and simulation, are either inaccurate or slow in determining the optimal configuration for a large scale system. In this paper, we show that machine learning models can be used in combination with accurate simulation to determine the optimal checkpoint configurations. We also demonstrate that more advanced techniques such as neural networks can further improve the performance in optimizing checkpoint configurations.