Solid state storage systems have been very effectively used in small devices; however, their effectiveness for large systems such as supercomputers is not yet proven. Recently, for the first time, a new supercomputer is being deployed with an all-flash storage as its main file system. In this work, we report our preliminary study of the I/O performance on this supercomputer named Perlmutter. We are able to achieve 1.4 TB/s with the default file configuration on the system. This default configuration outperforms dozens of other choices tested, though the current observed performance is still pretty far from the theoretical peak performance of 5 TB/s.

The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of generated data introduces additional constraints on the data layout strategies that can be used when writing data to secondary storage. We review these I/O challenges and introduce two online data layout reorganization approaches for achieving good tradeoffs between read and write performance. We demonstrate the benefits of using these two approaches for the ECP particle-in-cell simulation WarpX, which serves as a motif for a large class of important Exascale applications. We show that by understanding application I/O patterns and carefully designing data layouts we can increase read performance by more than 80 percent.

Scientific discoveries are increasingly relying on analysis of massive amounts of data. The ability to directly access the most relevant data records through query, without shifting through all of them becomes essential. However, scientific datasets are commonly stored on parallel file systems and I/O systems that are optimized for reading/writing large chunks of data, and many scientific datasets have spatial-temporal data similarity, such that the records with similar values often locate in a close proximity of each other. Therefore, our previous work started to investigate the benefit of using block range index technique for scientific datasets, which only records the value range of all the records in a data block. In this paper, we extend our work in several aspects. First, we implement and integrate our blockindex technique with the ADIOS I/O system. Second, we show our proposed method can be significantly better than the existing minmax and bitmaps indexing methods supported in ADIOS, and can also have comparable performance in the worst case. Third, we propose several techniques that can take advantage of the block index information to greatly reduce data retrieval time from query results. Fourth, we evaluate our approach using several real scientific datasets, and analyze the spatial-temporal data similarity characteristics in them. Through our study, we believe block index can be an effective indexing technique for scientific datasets with little implementation and operating overhead. It's size is small enough for building the indexes on-the-fly, and yet its query information is sufficient for efficient data access.

A key trend facing extreme-scale computational science is the widening gap between computational and I/O rates, and the challenge that follows is how to best gain insight from simulation data when it is increasingly impractical to save it to persistent storage for subsequent visual exploration and analysis. One approach to this challenge is centered around the idea of in situ processing, where visualization and analysis processing is performed while data is still resident in memory. This paper examines several key design and performance issues related to the idea of in situ processing at extreme scale on modern platforms: scalability, overhead, performance measurement and analysis, comparison and contrast with a traditional post hoc approach, and interfacing with simulation codes. We illustrate these principles in practice with studies, conducted on large-scale HPC platforms, that include a miniapplication and multiple science application codes, one of which demonstrates in situ methods in use at greater than 1M-way concurrency.

Before scientific analyses run on shared infrastructure, such as the Open Science Grid or XSEDE, scientists must often transfer or stage key data sets those resources. Often these datasets consist of many files that may be transferred by multiple clients in parallel. We study two techniques that improve the use of available resources for these large, long-running, multi-file transfers. First, we adapt transfer parameters for multi-file transfers based on recent transfer performance. Second, we use VO and site policies to influence the allocation of system resources for transfers, such as available transfer streams. We describe our system design and summarize its implementation and performance.

Abstract Many production Grid and e‐Science infrastructures have begun to offer services to end‐users during the past several years with an increasing number of scientific applications that require access to a wide variety of resources and services in multiple Grids. Therefore, the Grid Interoperation Now—Community Group of the Open Grid Forum—organizes and manages interoperation efforts among those production Grid infrastructures to reach the goal of a world‐wide Grid vision on a technical level in the near future. This contribution highlights fundamental approaches of the group and discusses open standards in the context of production e‐Science infrastructures. Copyright © 2009 John Wiley & Sons, Ltd.

On-Line Analytical Processing (OLAP) systems based on a multidimensional view of data have found widespread use in business applications and are being used increasingly in non-standard applications.These systems provide good performance and ease-of-use.However, the complex structures and relationships inherent in data in non-standard applications are not accommodated well by OLAP systems.In contrast, object database systems are built to handle such complexity, but do not support OLAP-type querying well.This paper presents the concepts and techniques underlying a flexible, "multi-model" federated system that enables OLAP users to exploit simultaneously the features of OLAP and object database systems.The system allows data to be handled using the most appropriate data model and technology: OLAP systems for multidimensional data and object database systems for more complex, general data.Additionally, physical data integration can be avoided.As a vehicle for demonstrating the capabilities of the system, a prototypical OLAP language is defined and extended to naturally support queries that involve data in object databases.The language permits selection criteria that reference object data, queries that return combinations of OLAP and object data, and queries that group multidimensional data according to object data.The system is designed to be aggregation-safe, in the sense that it exploits the aggregation semantics of the data to prevent incorrect or meaningless query results.A prototype implementation of the system is reported.