Xiaohui Shen

Data management for large-scale scientific computations in high performance distributed systems

With the increasing number of scientific applications manipulating huge amounts of data, effective high-level data management is an increasingly important problem. Unfortunately, so far the solutions to the high‐level data management problem either require deep understanding of specific storage architectures and file layouts (as in high-performance file storage systems) or produce unsatisfactory I/O performance in exchange for ease-of-use and portability (as in relational DBMSs). In this paper we present a novel application development environment which is built around an active meta-data management system (MDMS) to handle high-level data in an effective manner. The key components of our three-tiered architecture are user application, the MDMS, and a hierarchical storage system (HSS). Our environment overcomes the performance problems of pure database-oriented solutions, while maintaining their advantages in terms of ease-of-use and portability. The high levels of performance are achieved by the MDMS, with the aid of user-specified, performance-oriented directives. Our environment supports a simple, easy-to-use yet powerful user interface, leaving the task of choosing appropriate I/O techniques for the application at hand to the MDMS. We discuss the importance of an active MDMS and show how the three components of our environment, namely the application, the MDMS, and the HSS, fit together. We also report performance numbers from our ongoing implementation and illustrate that significant improvements are made possible without undue programming effort.

A Distributed Multi-Storage Resource Architecture and I/O Performance Prediction for Scientific Computing

I/O intensive applications have posed great challenges to computational scientists. A major problem of these applications is that users have to sacrifice performance requirements in order to satisfy storage capacity requirements in a conventional computing environment. Further performance improvement is impeded by the physical nature of these storage media even when state-of-the-art I/O optimizations are employed.In this paper, we present a distributed multi-storage resource architecture, which can satisfy both performance and capacity requirements by employing multiple storage resources. Compared to a traditional single storage resource architecture, our architecture provides a more flexible and reliable computing environment. This architecture can bring new opportunities for high performance computing as well as inherit state-of-the-art I/O optimization approaches that have already been developed. It provides application users with high-performance storage access even when they do not have the availability of a single large local storage archive at their disposal. We also develop an Application Programming Interface (API) that provides transparent management and access to various storage resources in our computing environment. Since I/O usually dominates the performance in I/O intensive applications, we establish an I/O performance prediction mechanism which consists of a performance database and a prediction algorithm to help users better evaluate and schedule their applications. A tool is also developed to help users automatically generate performance data stored in databases. The experiments show that our multi-storage resource architecture is a promising platform for high performance distributed computing.

DPFS: a distributed parallel file system

One of the challenges brought by large-scale scientific applications is how to avoid remote storage access by collectively using enough local storage resources to hold huge amount of data generated by the simulation while providing high performance I/O. DPFS, a Distributed Parallel File System, is designed and implemented to address this problem. DPFS collects locally distributed unused storage resources as a supplement to the internal storage of parallel computing systems to satisfy the storage capacity requirement of large-scale applications. In addition, like parallel file systems, DPFS provides striping mechanisms that divides a file into small pieces and distributes them across multiple storage devices for parallel data access. The unique feature of DPFS is that it provides three file levels with each file level corresponding to a file striping method. In addition to the traditional linear striping method, DPFS also provides a novel multidimensional striping method that can solve performance problems of linear striping for many popular access patterns. Other issues such as load-balancing and user interface are also addressed in DPFS.

A novel application development environment for large-scale scientific computations

Our results demonstrate that our novel application development environment provides both ease-of-use and high performance for large-scale, I/O-intensive scientific applications.

A distributed multi-storage resource architecture and I/O performance prediction for scientific computing

I/O-intensive applications have posed great challenges to computational scientists. A major problem of these applications is that users have to sacrifice performance requirements in order to satisfy storage capacity requirements in a conventional computing environment. Further performance improvement is impeded by the physical nature of these storage media, even if state-of-the-art I/O optimizations are employed. In this paper, we present a distributed multi-storage resource architecture that can satisfy both performance and capacity requirements by employing multiple storage resources. Compared to the traditional single-storage resource architecture, our architecture provides a more flexible and reliable computing environment. It can bring new opportunities for high-performance computing as well as inheriting state-of-the-art I/O optimization approaches that have already been developed. We also develop an application programming interface (API) that provides transparent management and access to various storage resources in our computing environment. As I/O usually dominates the performance in I/O-intensive applications, we establish an I/O performance prediction mechanism which consists of a performance database and a prediction algorithm to help users better evaluate and schedule their applications. A tool is also developed to help users automatically generate the performance database. Experiments show that our multi-storage resource architecture is a promising platform for high-performance distributed computing.

MS-I/O: A Distributed Multi-Storage I/O System

More and more parallel applications are running in a distributed environment to take advantage of easily available and inexpensive commodity resources. For data intensive applications, employing multiple distributed storage resources has many advantages. In this paper, we present a Multi-Storage I/O System (MS-I/O) that can not only effectively manage various distributed storage resources in the system, but also provide novel high performance storage access schemes. MS-I/O employs many state-of-the-art I/O optimizations such as collective I/O, asynchronous I/O etc. and a number of new techniques such as data location, data replication, subfile, superfile and data access history. In addition, many MS-I/O optimization schemes can work simultaneously within a single data access session, greatly improving the performance. Although I/O optimization techniques can help improve performance, it also complicates I/O system. In addition, most optimization techniques have their limitations. Therefore, selecting accurate optimization policies requires ex-pert knowledge which is not suitable for end users who may have little knowledge of I/O techniques. So the task of I/O optimization decision should be left to the I/O system itself, that is, automatic from users point of view. We present a User Access Pattern data structure which is associated With each dataset that can help MS-I/O easily make accurate I/O optimization decisions.

Meta-data Management System for High-Performance Large-Scale Scientific Data Access

Many scientific applications manipulate large amount of data and, therefore, are parallelized on high-performance computing systems to take advantage of their computational power and memory space. The size of data processed by these large-scale applications can easily overwhelm the disk capacity of most systems. Thus, tertiary storage devices are used to store the data. The parallelization of this type of applications requires understanding of not only the data partition pattern among multiple processors but also the underlying storage architectures and the data storage pattern. In this paper, we present a meta-data management system which uses a database to record the information of datasets and manage these meta data to provide suitable I/O interface. As a result, users specify dataset names instead of data physical location to access data using optimal I/O calls without knowing the underlying storage structure. We use an astrophysics application to demonstrate that the management system can provide convenient programming environment with negligible database access overhead.

A distributed multi-storage I/O system for data intensive scientific computing

More and more parallel applications are running in a distributed environment to take advantage of easily available and inexpensive commodity resources. For data intensive applications, employing multiple distributed storage resources has many advantages. In this paper, we present a Multi-Storage I/O System (MS-I/O) that cannot only effectively manage various distributed storage resources in the system, but also provide novel high performance storage access schemes. MS-I/O employs many state-of-the-art I/O optimizations such as collective I/O, asynchronous I/O etc. and a number of new techniques such as data location, data replication, subtile, superfile and data access history. In addition, many MS-I/O optimization schemes can work simultaneously within a single data access session, greatly improving the performance.Although I/O optimization techniques can help improve performance, it also complicates I/ O system. In addition, most optimization techniques have their limitations. Therefore, selecting accurate optimization policies requires expert knowledge which is not suitable for end users who may have little knowledge of I/O techniques. So the task of I/O optimization decision should be left to the I/O system itself, that is, automatic from users point of view. We present a User Access Pattern data structure which is associated with each dataset that can help MS-I/O easily make accurate I/O optimization decisions.

A high-performance distributed parallel file system for data-intensive computations

One of the challenges brought by large-scale scientific applications is how to avoid remote storage access by collectively using sufficient local storage resources to hold huge amounts of data generated by the simulation while providing high-performance I/O. DPFS, a distributed parallel file system, is designed and implemented to address this problem. DPFS collects locally distributed and unused storage resources as a supplement to the internal storage of parallel computing systems to satisfy the storage capacity requirement of large-scale applications. In addition, like parallel file systems, DPFS provides striping mechanisms that divide a file into small pieces and distributes them across multiple storage devices for parallel data access. The unique feature of DPFS is that it provides three file levels with each file level corresponding to a file striping method. In addition to the traditional linear striping method, DPFS also provides a novel Multidimensional striping method that can solve performance problems of linear striping for many popular access patterns. Other issues such as load-balancing and user interface are also addressed in DPFS.