Applications of Composable System Technology for BeeOND
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Jump to search filtersTools to Rapidly Develop Sophisticated HPC Software Libraries
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Hobbes Node Virtualization Layer: System Software Infrastructure for Application Composition and Performance Isolation
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Hobbes Node Virtualization Layer: System Software Infrastructure for Application Composition and Performance Isolation
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Remora: A MPI runtime for Composed Applications at Extreme Scale
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Toward Automatic Test Synthesis for Performance Portable Programs
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Automated Test Generation for Performance Portable Programs Using Clang/LLVM and Formal Methods
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Faodel: Data Management for Next-Generation Application Workflows
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SNL ATDM: I/O and Data Management
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EMPRESS-Extensible metadata provider for extreme-scale scientific simulations
Proceedings of PDSW-DISCS 2017 - 2nd Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems - Held in conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis
Significant challenges exist in the efficient retrieval of data from extreme-scale simulations. An important and evolving method of addressing these challenges is application-level metadata management. Historically, HDF5 and NetCDF have eased data retrieval by offering rudimentary attribute capabilities that provide basic metadata. ADIOS simplified data retrieval by utilizing metadata for each process' data. EMPRESS provides a simple example of the next step in this evolution by integrating per-process metadata with the storage system itself, making it more broadly useful than single file or application formats. Additionally, it allows for more robust and customizable metadata.
Faodail: Enabling In Situ Analytics for Next-Generation Systems
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EMPRESS?Extensible Metadata PRovider for Extreme-scale Scientific Simulations
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ATDM Data Warehouse: Data Management Services for Exascale Computing
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ATDM Data Warehouse
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Hobbes: A Multi‐Stack Approach for Application Composition and Performance Isolation
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ATDM Data Management FY2015: Data Warehouse Progress Report
The Advanced Technology Development and Mitigation (ATDM) program at Sandia National Laboratories is a new effort to build next-generation simulation codes that will map well to upcoming exascale computing platforms. Rather than follow traditional single- program, multiple data (SPMD) programming techniques, ATDM is developing applications in an asynchronous many task (AMT) form that describes work as a graph of tasks that have data dependencies. The data management team is focused on developing a data warehouse for ATDM that will enable tasks to store and exchange data objects efficiently. This report summarizes the data management teams efforts during FY15, and documents: (1) an initial API and implementation for the data warehouses key/value store, (2) API requirements for use with ATDMs runtime, (3) initial requirements for storing ATDM-specific data, and (4) the current organization of software components that will be used by the data warehouse.
Enabling Capabilities for Intergrated Application Workflows
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Kelpie: FY2014 Project Update
The ASC CSSE project Kelpie is a research and development project focused on developing a distributed, in-memory data management system that can be leveraged in a number of high-performance computing (HPC) applications. After FY13s demonstration that a key/value data store could be implemented on top of the Nessie RDMA/RPC library, we began refactoring Kelpie in FY14 in order to make it more usable by other research teams that need it for upcoming milestones. This report provides a summary of the different efforts in FY14 that took place to make Kelpie a more usable system.
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