Parallel File Systems (PFSs) are frequently deployed on leadership High Performance Computing (HPC) systems to ensure efficient I/O, persistent storage and scalable performance. Emerging Deep Learning (DL) applications incur new I/O and storage requirements to HPC systems with batched input of small random files. This mandates PFSs to have commensurate features that can meet the needs of DL applications. BeeGFS is a recently emerging PFS that has grabbed the attention of the research and industry world because of its performance, scalability and ease of use. While emphasizing a systematic performance analysis of BeeGFS, in this paper, we present the architectural and system features of BeeGFS, and perform an experimental evaluation using cutting-edge I/O, Metadata and DL application benchmarks. Particularly, we have utilized AlexNet and ResNet-50 models for the classification of ImageNet dataset using the Livermore Big Artificial Neural Network Toolkit (LBANN), and ImageNet data reader pipeline atop TensorFlow and Horovod. Through extensive performance characterization of BeeGFS, our study provides a useful documentation on how to leverage BeeGFS for the emerging DL applications.

Large-scale scientific applications on High-Performance Computing (HPC) systems are generating a colossal amount of data that need to be analyzed in a timely manner for new knowledge, but are too costly to transfer due to their sheer size. Many HPC systems have catered to in situ analytics solutions that can analyze temporary datasets as they are generated, i.e., without storing to long-term storage media. However, there is still an open question on how to conduct efficient analytics of permanent datasets that have been stored to the backend persistent storage because of their long-term value. To fill the void, we exploit the analytics shipping model for fast analysis of large-scale scientific datasets on HPC backend storage servers. Through an efficient integration of MapReduce and the popular Lustre storage system, we have developed a Virtualized Analytics Shipping (VAS) framework that can ship MapReduce programs to Lustre storage servers. The VAS framework includes three component techniques: (a) virtualized analytics shipping with fast network and disk I/O; (b) stripe-aligned data distribution and task scheduling and (c) pipelined intermediate data merging and reducing. The first technique provides necessary isolation between MapReduce analytics and Lustre I/O services. The second and third techniques optimize MapReduce on Lustre and avoid explicit shuffling. Our performance evaluation demonstrates that VAS offers an exemplary implementation of analytics shipping and delivers fast and virtualized MapReduce programs on backend Lustre storage servers.

MapReduce is a widely accepted framework for addressing big data challenges. Recently, it has also gained broad attention from scientists at the U.S. leadership computing facilities as a promising solution to process gigantic simulation results. However, conventional high-end computing systems are constructed based on the compute-centric paradigm while big data analytics applications prefer a data-centric paradigm such as MapReduce. This work characterizes the performance impact of key differences between compute- and data-centric paradigms and then provides optimizations to enable a dual-purpose HPC system that can efficiently support conventional HPC applications and new data analytics applications. Using a state-of-the-art MapReduce implementation Spark and the Hyperion system at Lawrence Livermore National Laboratory, we have examined the impact of storage architectures, data locality and task scheduling to the memory-resident MapReduce jobs. Based on our characterization and findings of the performance behaviors, we have introduced two optimization techniques, namely Enhanced Load Balancer and Congestion-Aware Task Dispatching, to improve the performance of Spark applications.