Todd Kordenbrock

Efficient Data-Movement for Lightweight I/O

Efficient data movement is an important part of any high-performance I/O system, but it is especially critical for the current and next-generation of massively parallel processing (MPP) systems. In this paper, we discuss how the scale, architecture, and organization of current and proposed MPP systems impact the design of the data-movement scheme for the I/O system. We also describe and analyze the approach used by the lightweight file systems (LWFS) project, and we compare that approach to more conventional data-movement protocols used by small and mid-range clusters. Our results indicate that the data-movement strategy used by LWFS clearly outperforms conventional data-movement protocols, particularly as data sizes increase

Trilinos I/O Support Trios

Trilinos I/O Support Trios is a new capability area in Trilinos that serves two important roles: 1 it provides and supports I/O libraries used by in-production scientific codes; 2 it provides a research vehicle for the evaluation and distribution of new techniques to improve I/O on advanced platforms. This paper provides a brief overview of the production-grade I/O libraries in Trios as well as some of the ongoing research efforts that contribute to the experimental libraries in Trios.

Experiences Applying Data Staging Technology in Unconventional Ways

Several efforts have shown the potential of using additional compute-area resources to enhance the IO path to storage. Efforts like data staging, IO forwarding, and similar techniques can accelerate IO performance and reduce the impact of IO time to a compute application. Hybrid staging enhanced this path by adding processing functionality to locations along the data path to storage. While these efforts have been effective, they have taken a somewhat limited view of the potential benefits using some additional compute resources can offer both to enhance a compute application as well as to offering a way to exploit HPC-style resources for non-traditional tasks. Over the last few years, we have been experimenting with the potential for other sorts of activities using a staging style approach to add or enable new functionality. The efforts in this area have yielded a collection of small projects that yield some insights into both the potential and limitations of this approach for both achieving exascale computing and for enabling alternative uses for HPC resources.

Faodel: Data Management for Next-Generation Application Workflows

Composition of computational science applications, whether into ad hoc pipelines for analysis of simulation data or into well-defined and repeatable workflows, is becoming commonplace. In order to scale well as projected system and data sizes increase, developers will have to address a number of looming challenges. Increased contention for parallel filesystem bandwidth, accomodating in situ and ex situ processing, and the advent of decentralized programming models will all complicate application composition for next-generation systems. In this paper, we introduce a set of data services, Faodel, which provide scalable data management for workflows and composed applications. Faodel allows workflow components to directly and efficiently exchange data in semantically appropriate forms, rather than those dictated by the storage hierarchy or programming model in use. We describe the architecture of Faodel and present preliminary performance results demonstrating its potential for scalability in workflow scenarios.