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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

Optimizing checkpoint data placement with guaranteed burst buffer endurance in large-scale hierarchical storage systems

Non-volatile devices, such as SSDs, will be an integral part of the deepening storage hierarchy on large-scale HPC systems. These devices can be on the compute nodes as part of a distributed burst buffer service or they can be external. Wherever they are located in the hierarchy, one critical design issue is the SSD endurance under the write-heavy workloads, such as the checkpoint I/O for scientific applications. For these environments, it is widely assumed that checkpoint operations can occur once every 60 min and for each checkpoint step as much as half of the system memory can be written out. Unfortunately, for large-scale HPC applications, the burst buffer SSDs can be worn out much more quickly given the extensive amount of data written at every checkpoint step. One possible solution is to control the amount of data written by reducing the checkpoint frequency. However, a direct effect caused by reduced checkpoint frequency is the increased vulnerability window of system failures and therefore potentially wasted computation time, especially for large-scale compute jobs.

Computer Science↗

Distributed metadata in a high performance computing environment

A computer-executable method, system, and computer program product for managing meta-data in a distributed storage system, wherein the distributed storage system includes one or more burst buffers enabled to operate with a distributed key-value store, the co computer-executable method, system, and computer program product comprising receiving a request for meta-data associated with a block of data stored in a first burst buffer of the one or more burst buffers in the distributed storage system, wherein the meta data is associated with a key-value, determining which of the one or more burst buffers stores the requested metadata, and upon determination that a first burst buffer of the one or more burst buffers stores the requested metadata, locating the key-value in a portion of the distributed key-value store accessible from the first burst buffer.

97 MATHEMATICS AND COMPUTING↗

Evaluation of a high-performance storage buffer with 3D XPoint devices for the DUNE data acquisition system

The DUNE detector is a neutrino physics experiment that is expected to take data starting from 2028. The data acquisition (DAQ) system of the experiment is designed to sustain several TB/s of incoming data which will be temporarily buffered while being processed by a software based data selection system. In DUNE, some rare physics processes (e.g. Supernovae Burst events) require storing the full complement of data produced over 1-2 minute window. These are recognised by the data selection system which fires a specific trigger decision. Upon reception of this decision data are moved from the temporary buffers to local, high performance, persistent storage devices. In this paper we characterize the performance of novel 3DXPoint SSD devices under different workloads suitable for high-performance storage applications. We then illustrate how such devices may be applied to the DUNE use-case: to store, upon a specific signal, 100 seconds of incoming data at 1.5 TB/s distributed among 150 identical units each operating at approximately 10GB/s.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Hybrid data storage system in an HPC exascale environment

A computer-executable method, system, and computer program product for managing I/O requests from a compute node in communication with a data storage system, including a first burst buffer node and a second burst buffer node, the computer-executable method, system, and computer program product comprising striping data on the first burst buffer node and the second burst buffer node, wherein a first portion of the data is communicated to the first burst buffer node and a second portion of the data is communicated to the second burst buffer node, processing the first portion of the data at the first burst buffer node, and processing the second portion of the data at the second burst buffer node.

97 MATHEMATICS AND COMPUTING↗

The Design of a Distributed Key-Value Store for Petascale Hot Storage in Data Acquisition Systems

Data acquisition systems for high energy physics experiments readout terabytes of data per second from a large number of electronic components. They are thus inherently distributed systems and require fast online data selection, otherwise requirements for permanent storage would be enormous. Still, incoming data need to be buffered while waiting for this selection to happen. Each minute of an experiment can produce hundreds of terabytes that cannot be lost before a selection decision is made. In this context, we present the design of DAQDB (Data Acquisition Database) - a distributed key-value store for high-bandwidth, generic data storage in event-driven systems. DAQDB offers not only high-capacity and low-latency buffer for fast data selection, but also opens a new approach in high-bandwidth data acquisition by decoupling the lifetime of the data analysis processes from the changing event rate due to the duty cycle of the data source. This is achievable by the option to extend its capacity even up to hundreds of petabytes to store hours of an experiment’s data. Our initial performance evaluation shows that DAQDB is a promising alternative to generic database solutions for the high luminosity upgrades of the LHC at CERN.

97 MATHEMATICS AND COMPUTING↗

Toward Managing HPC Burst Buffers Effectively: Draining Strategy to Regulate Bursty I/O Behavior

HPC (high-performance computing) applications usually show bursty I/O behaviors. In order to expedite the applications, permanent storage systems are usually provisioned to serve such I/O bursts. Approaching the era of exascale computing, non-volatile RAM is introduced as burst buffers, to absorb the bursty bulk data and relax the I/O provisioning requirement of the permanent storage systems. However, without judiciously draining the burst buffers, I/O bursts are passed down to the underlying storage systems, which causes severe I/O contention issues.In order to minimize the I/O provisioning requirement and resolve the issues caused by I/O bursts, we propose a proactive draining scheme to manage the draining process of distributed node-local burst buffers. In addition, we develop an I/O provisioning model to predict the minimized I/O provisioning requirement for permanent storage systems. Evaluation results show that applying the proactive draining scheme largely relaxes the I/O provisioning requirement while preserving the I/O performance of underlying storage systems.

97 MATHEMATICS AND COMPUTING↗

Optimizing End-to-End Big Data Transfers over Terabits Network Infrastructure

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink, for instance, the data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this study, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulk data movement framework for terabit networks, called LADS. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to benefit from hardware-level zero-copy, and operating system bypass capabilities when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and data transfer rates across the high speed networks. We also investigate the performance degradation problems of LADS due to I/O contention on the parallel file system (PFS), when multiple LADS tools share the PFS. We design and evaluate a meta-scheduler to coordinate multiple I/O streams while sharing the PFS, to minimize the I/O contention on the PFS. Finally, with our evaluations, we observe that LADS with meta-scheduling can further improve the performance by up to 14 percent relative to LADS without meta-scheduling.

97 MATHEMATICS AND COMPUTING↗

Quota management in dCache or making a perfectly normal file system normal

dCache (https://dcache.org) is a highly scalable storage system providing location-independent access to data. The data are stored across multiple data servers as complete files presented to the end-user via a single-rooted namespace. From its inception, dCache has been designed as a caching disk buffer to a tertiary tape storage system with the assumption that the latter has virtually unlimited capacity. dCache can also be configured as a disk-only storage system with no tape backend. Owing to the idea that a tape resource is infinite, or purely physically limited by budget considerations, the system has never provided for any restrictions on how much data can be stored on tape. Likewise, in the disk-only configuration, the capacity of the system is only limited by the aggregate disk capacity of the data servers. In a multi-user environment, however, this has become problematic. This presentation will describe the design and implementation of a user- and group-based quota system, that allows to manage tape and disk space allocations, as part of dCache namespace.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Accelerating shared file checkpoint with local burst buffers

A data management system and method for accelerating shared file checkpointing. Written application data is aggregated in an application data file created in a local burst buffer memory at a compute node, and an associated data mapping built index to maintain information related to the offsets into a shared file at which segments of the application data is to be stored in a parallel file system, and where in the buffer those segments are located. The node asynchronously transfers a data file containing the application data and the associated data mapping index to a file server for shared file storage. The data management system and method further accelerates shared file checkpointing in which a shared file, together with a map file that specifies how the shared file is to be distributed, is asynchronously transferred to local burst buffer memories at the nodes to accelerate reading of the shared file.

Gooding, Thomas↗

Parallel checksumming of data chunks of a shared data object using a log-structured file system

Checksum values are generated and used to verify the data integrity. A client executing in a parallel computing system stores a data chunk to a shared data object on a storage node in the parallel computing system. The client determines a checksum value for the data chunk; and provides the checksum value with the data chunk to the storage node that stores the shared object. The data chunk can be stored on the storage node with the corresponding checksum value as part of the shared object. The storage node may be part of a Parallel Log-Structured File System (PLFS), and the client may comprise, for example, a Log-Structured File System client on a compute node or burst buffer. The checksum value can be evaluated when the data chunk is read from the storage node to verify the integrity of the data that is read.

97 MATHEMATICS AND COMPUTING↗

ExaHDF5: Delivering Efficient Parallel I/O on Exascale Computing Systems

Scientific applications at exascale generate and analyze massive amounts of data. A critical requirement of these applications is the capability to access and manage this data efficiently on exascale systems. As such, parallel I/O, the key technology enables moving data between compute nodes and storage, faces monumental challenges from new applications, memory, and storage architectures considered in the designs of exascale systems. As the storage hierarchy is expanding to include node-local persistent memory, burst buffers, etc., as well as disk-based storage, data movement among these layers must be efficient. Parallel I/O libraries of the future should be capable of handling file sizes of many terabytes and beyond. In this paper, we describe new capabilities we have developed in Hierarchical Data Format version 5 (HDF5), the most popular parallel I/O library for scientific applications. HDF5 is one of the most used libraries at the leadership computing facilities for performing parallel I/O on existing HPC systems. The state-of-the-art features we describe include: Virtual Object Layer (VOL), Data Elevator, asynchronous I/O, full-featured single-writer and multiple-reader (Full SWMR), and parallel querying. In this paper, we introduce these features, their implementations, and the performance and feature benefits to applications and other libraries.

97 MATHEMATICS AND COMPUTING↗

Computing erasure metadata and data layout prior to storage using a processing platform

Techniques are provided for computing data and metadata layout prior to storage in a storage system using a processing platform. An exemplary processing platform comprises one or more of a compute node and a burst buffer appliance. The processing platform communicates with a plurality of the compute nodes over a network, wherein a plurality of applications executing on the plurality of compute nodes generate a plurality of data objects; computes erasure metadata for one or more of the data objects on at least one of the compute nodes; and provides the erasure metadata with the corresponding one or more data objects to a storage system. The processing platform optionally determines a full set of the data objects to be stored and queries the storage system to determine an anticipated layout of the full set of the data objects to be stored. The anticipated layout allows special handling, for example, for small files and large files that are identified based on predefined criteria.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Parallel log structured file system collective buffering to achieve a compact representation of scientific and/or dimensional data

Collective buffering and data pattern solutions are provided for storage, retrieval, and/or analysis of data in a collective parallel processing environment. For example, a method can be provided for data storage in a collective parallel processing environment. The method comprises receiving data to be written for a plurality of collective processes within a collective parallel processing environment, extracting a data pattern for the data to be written for the plurality of collective processes, generating a representation describing the data pattern, and saving the data and the representation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Electrifying Airport GSE: Monte Carlo Grid Impacts

Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.

Bose, Ranjan [ORNL] (ORCID:0009000791026327)↗

GPU Direct I/O with HDF5

Exascale HPC systems are being designed with accelerators, such as GPUs, to accelerate parts of applications. In machine learning workloads as well as large-scale simulations that use GPUs as accelerators, the CPU (or host) memory is currently used as a buffer for data transfers between GPU (or device) memory and the file system. If the CPU does not need to operate on the data, then this is sub-optimal because it wastes host memory by reserving space for duplicated data. Furthermore, this “bounce buffer” approach wastes CPU cycles spent on transferring data. A new technique, NVIDIA GPUDirect Storage (GDS), can eliminate the need to use the host memory as a bounce buffer. Thereby, it becomes possible to transfer data directly between the device memory and the file system. This direct data path shortens latency by omitting the extra copy and enables higher-bandwidth. To take full advantage of GDS in existing applications, it is necessary to provide support with existing I/O libraries, such as HDF5 and MPI-IO, which are heavily used in applications. In this paper, we describe our effort of integrating GDS with HDF5, the top I/O library at NERSC and at DOE leadership computing facilities. We design and implement this integration using a HDF5 Virtual File Driver (VFD). The GDS VFD provides a file system abstraction to the application that allows HDF5 applications to perform I/O without the need to move data between CPUs and GPUs explicitly. We compare performance of the HDF5 GDS VFD with explicit data movement approaches and demonstrate superior performance with the GDS method.

Ravi, J↗

Challenges and Opportunities of User-Level File Systemsfor HPC

The performance gap between magnetic disks and data processing on HPC systems has become that huge that an efficient data processing can only be achieved by introducing non-volatile memory (NVRAM) as a new storage tier. Although the benefits of hierarchical storage have been adequately demonstrated to the point that the newest leadership class HPC systems will employ burst buffers, critical questions remain for supporting hierarchical storage systems, including: How should we present hierarchical storage systems to user applications, such that they are easy to use and that application code is portable across systems? How should we manage data movement through a storage hierarchy for best performance and resilience of data? How do the particular I/O use cases mandate the way we manage data? There have been many efforts to explore this space in the form of file systems, with increasingly more implemented at the user level. This is because it is relatively easy to swap in new, specialized user-level file systems for use by applications on a case-by-case basis, as opposed to the current mainstream approach of using general-purpose, system-level file systems which may not be optimized for HPC workloads and must be installed by administrators. In contrast, file systems at the user level can be tailored for specific HPC workloads for high performance and can be used by applications without administrator intervention. Many user-level file system developers have found themselves “having to reinvent the wheel” to implement various optimizations in their file systems. Thus, a main goal of this meeting was to bring together experts in I/O performance, file systems, and storage, and collectively explore the space of current and future problems and solutions for I/O on hierarchical storage systems in order to begin a community effort in enabling user-level file system support for HPC systems. We had a lively week of learning about each other’s approaches as well as unique I/O use cases that can influence the design of a community-driven file and storage system standards. The agenda for this meeting contained talks from participants on the following high level topics: HPC storage and I/O support today; what do HPC users need for I/O; existing userlevel file system efforts; object stores and other alternative storage systems; and components for building user-level file systems. The talks were short and intended to be conversation starters for more in-depth discussions with the whole group. The participants engaged in lengthy discussions on various questions that arose from the talks including: Are we ready to program to a memory hierarchy versus block devices? Are the needs of HPC users reflected in our existing file systems and storage systems? Should we drop or keep POSIX moving forward? What do we mean when we say “user-level file system”? Do we all mean the same thing? How should the IO 500 benchmark be defined so it is fair and useful? and How are stage-in and stage-out actually going to work? The report for this seminar contains a record of the talks from the participants as well as the resulting discussions. Our hope is that the effort initiated during this seminar will result in long-term collaborations that will benefit the HPC community as a whole.

97 MATHEMATICS AND COMPUTING↗

Exascale Storage Systems the SIRIUS Way

As the exascale computing age emerges, data related issues are becoming critical factors that determine how and where we do computing. Popular approaches used by traditional I/O solution and storage libraries become increasingly bottlenecked due to their assumptions about data movement, re-organization, and storage. While, new technologies, such as "burst buffers", can help address some of the short-term performance issues, it is essential that we reexamine the underlying storage and I/O infrastructure to effectively support requirements and challenges at exascale and beyond. In this paper we present a new approach to the exascale Storage System and I/O (SSIO), which is based on allowing users to inject application knowledge into the system and leverage this knowledge to better manage, store, and access large data volumes so as to minimize the time to scientific insights. Central to our approach is the distinction between the data, metadata, and the knowledge contained therein, transferred from the user to the system by describing "utility" of data as it ages.

97 MATHEMATICS AND COMPUTING↗