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Complex applications and workflows needs are often exclusively expressed in terms of computational resources on HPC systems. In many cases, other resources like storage or network are not allocatable and are shared across the entire HPC system. By looking at the storage resource in particular, any workflow or application should be able to select both its preferred data manager and its required storage capability or capacity. To achieve such a goal, new mechanisms should be introduced. In this work, we introduce such a mechanism for dynamically provision a data management system on top of storage devices. We particularly focus our effort on deploying a BeeGFS instance across multiple DataWarp nodes on a Cray XC50 system. However, we also demonstrate that the same mechanism can be used to deploy BeeGFS on non-Cray system.

As dataset sizes increase, data analysis tasks in high performance computing (HPC) are increasingly dependent on sophisticated dataflows and out-of-core methods for efficient system utilization. In addition, as HPC systems grow, memory access and data sharing are becoming performance bottlenecks. Cloud computing employs a data processing paradigm typically built on a loosely connected group of low-cost computing nodes without relying upon shared storage and/or memory. Apache Spark is a popular engine for large-scale data analysis in the cloud, which we have successfully deployed via job submission scripts on production clusters. In this paper, we describe common parallel analysis dataflows for both Message Passing Interface (MPI) and cloud based applications. We developed an effective benchmark to measure the performance characteristics of these tasks using both types of systems, specifically comparing MPI/C-based analyses with Spark. The benchmark is a data processing pipeline representative of a typical analytics framework implemented using map-reduce. In the case of Spark, we also consider whether language plays a role by writing tests using both Python and Scala, a language built on the Java Virtual Machine (JVM). We include performance results from two large systems at Argonne National Laboratory including Theta, a Cray XC40 supercomputer on which our experiments run with 65,536 cores (1024 nodes with 64 cores each). The results of our experiments are discussed in the context of their applicability to future HPC architectures. Beyond understanding performance, our work demonstrates that technologies such as Spark, while typically aimed at multi-tenant cloud-based environments, show promise for data analysis needs in a traditional clustering/supercomputing environment., Comment: Submitted to IEEE Cloud 2019