The Next Generation of EMPRESS: A Metadata Management System For Accelerated Scientific Discovery at Exascale

Scientific data sets have grown rapidly in recent years, outpacing the growth in memory and network bandwidths. This I/O bottleneck has made it increasingly difficult for scientists to read and search outputted datasets in an attempt to find features of interest. In this paper, we will present the next generation of EMPRESS, a scalable metadata management service that offers the following solution: users can tag features of interest and search these tags without having to read in the associated datasets. EMPRESS provides, in essence, a digital scientific notebook where scientists can write down observations and highlight interesting results, and an efficient way to search these annotations. EMPRESS also provides storage-system independent physical metadata, providing a portable way for users to read both metadata and the associated data. EMPRESS offers scalability through two different deployment modes: local , which runs on the compute nodes and dedicated, which uses a set of dedicated, shared-nothing servers. EMPRESS also provides robust fault tolerance and transaction management, which is crucial to supporting workflows

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in Materials Science and Engineering; Address any correspondence to Vikram Gavini ([email protected]) and Danny Perez ([email protected]

A Persistent Storage Model for Extreme Computing

The continuing technological progress resulted in a dramatic growth in aggregate computational performance of the largest supercomputing systems. Unfortunately, these advances did not translate to the required extent into accompanying I/O systems and little more in terms of architecture or effective access latency. New classes of algorithms developed for massively parallel applications, that gracefully handle the challenges of asynchrony, heavily multi-threaded distributed codes, and message-driven computation, must be matched by similar advances in I/O methods and algorithms to produce a well performing and balanced supercomputing system. This dissertation proposes PXFS, a storage model for persistent objects inspired by the ParalleX model of execution that addresses many of these challenges. The PXFS model is designed to be asynchronous in nature to comply with ParalleX model and proposes an active TupleSpace concept to hold all kinds of metadata/meta-object for either storage objects or runtime objects. The new active TupleSpace can also register ParalleX actions to be triggered under certain tuple operations. An first implementation of PXFS utilizing a well-known Orange parallel file system as its back-end via asynchronous I/O layer and the implementation of TupleSpace component in HPX, the implementation of ParalleX. These details are also described along with the preliminary performance data. A house-made micro benchmark is developed to measure the disk I/O throughput of the PXFS asynchronous interface. The results show perfect scalability and 3x to 20x times speedup of I/O throughput performance comparing to OrangeFS synchronous user interface. Use cases of TupleSpace components are discussed for real-world applications including micro check-pointing. By utilizing TupleSpace in HPX applications for I/O, global barrier can be replaced with fine-grained parallelism to overlap more computation with communication and greatly boost the performance and efficiency. Also the dissertation showcases the distributed directory service in Orange file system which process directory entries in parallel and effectively improves the directory metada operations

Roadmap on Electronic Structure Codes in the Exascale Era

Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing

Heterogeneous hierarchical workflow composition

Workflow systems promise scientists an automated end-to-end path from hypothesis to discovery. However, expecting any single workflow system to deliver such a wide range of capabilities is impractical. A more practical solution is to compose the end-to-end workflow from more than one system. With this goal in mind, the integration of task-based and in situ workflows is explored, where the result is a hierarchical heterogeneous workflow composed of subworkflows, with different levels of the hierarchy using different programming, execution, and data models. Materials science use cases demonstrate the advantages of such heterogeneous hierarchical workflow composition.This work is a collaboration between Argonne National Laboratory and the Barcelona Supercomputing Center within the Joint Laboratory for Extreme-Scale Computing. This research is supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC02- 06CH11357, program manager Laura Biven, and by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), by Generalitat de Catalunya (contract 2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware