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DOE OSTI2019

SNAPPy: A snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing

Abstract Human immunodeficiency virus 1 (HIV-1) genome sequencing is routinely done for drug resistance monitoring in hospitals worldwide. Subtyping these extensive datasets of HIV-1 sequences is a critical first step in molecular epidemiology and evolution studies. The clinical relevance of HIV-1 subtypes is increasingly recognized. Several studies suggest subtype-related differences in disease progression, transmission route efficiency, immune evasion, and even therapeutic outcomes. HIV-1 subtyping is mainly done using web-servers. These tools have limitations in scalability and potential noncompliance with data protection legislation. Thus, the aim of this work was to develop an efficient method for large-scale local HIV-1 subtyping. We designed SNAPPy: a snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing. It contains several tasks of phylogenetic inference and BLAST queries, which can be executed sequentially or in parallel, taking advantage of multiple-core processing units. Although it was built for subtyping, SNAPPy is also useful to perform extensive HIV-1 alignments. This tool facilitates large-scale sequence-based HIV-1 research by providing a local, resource efficient and scalable alternative for HIV-1 subtyping. It is capable of analyzing full-length genomes or partial HIV-1 genomic regions (GAG, POL, and ENV) and recognizes more than ninety circulating recombinant forms. SNAPPy is freely available at: https://github.com/PMMAraujo/snappy/releases.

Araújo, Pedro M. M.↗

DOE OSTI2019

dCache - Joining the noWORM Storage Club

For over a decade, dCache.ORG has provided robust software, called dCache, that is used at more than 80 universities and research institutes around the world, allowing these sites to provide reliable storage services for the WLCG experiments and many other scientific communities. The flexible architecture of dCache allows running it in a wide variety of configurations and platforms - from all-in-one Raspberry-Pi up to hundreds of nodes in multi-petabyte infrastructures. The life cycle of scientific data is well defined - collected, processed, archived and finally deleted, when it’s not needed anymore. Moreover, during all those stages the data is never modified: either the original data is used, or new derived data is produced. With this knowledge, dCache was designed to handle immutable files as efficiently as possible. Data replication, HSM connectivity and data-server independent operations are only possible due to the immutable nature of stored data. Nowadays many commercial vendors provide such write-once-read-many or WORM storage systems, as they become more and more demanded with grown demand of audio, photo and video content in the web. On the other hand by providing standard NFSv4.1 interface dCache is often used as a general-purpose file-system, especially by new communities, like photon scientists or microbiologists. Although many users are aware of data immutability, some applications and use cases still require in-place updates of stored files. To satisfy new requirements some fundamental changes have to be applied to dCache’s core design. However, new developments must not compromise any aspect of existing functionality. In this presentation we will show new developments in dCache to turn it into a regular file system. We will discuss the challenges to build a distributed storage system, ‘life’ with POSIX compliance, handling of multiple replicas and backward compatibility by providing WORM and noWORM capabilities within the same storage system.

Mkrtchyan, Tigran↗

DOE OSTI2017

Partial differential equations preconditioner resilient to soft and hard faults

We present a domain-decomposition-based preconditioner for the solution of partial differential equations (PDEs) that is resilient to both soft and hard faults. The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. This reformulation allows us to recast the problem as a set of independent tasks, and exploit data locality to reduce global communication. We discuss two different parallel implementations: (a) a single program multiple data (SPMD) version based on a one-to-one mapping between subdomain and MPI processes responsible for both state and computation; and (b) an asynchronous server–client implementation where all state information is held by the servers and clients are designed solely as computational units. We present a scalability comparison of both implementations under nominal conditions, showing efficiency within ~80% for up to 12,000 cores. We present a resilience analysis under different fault scenarios based on the server–client implementation. This framework provides resiliency to hard faults such that if a client crashes, it stops asking for work, and the servers simply distribute the work among all of the other clients alive. Erroneous subdomain solves (e.g. due to soft faults) appear as corrupted data, which is either rejected if that causes a task to fail, or is seamlessly filtered out during the regression stage through a suitable noise model. Three different types of faults are modeled: hard faults modeling nodes (or clients) crashing; soft faults occurring during the communication of the tasks between server and clients; and soft faults occurring during task execution. We demonstrate the resiliency of the approach for a 2D elliptic PDE, and explore the effect of the faults at various failure rates.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Efficient monitoring of CRAB jobs at CMS

CRAB is a tool used for distributed analysis of CMS data. Users can submit sets of jobs with similar requirements (tasks) with a single request. CRAB uses a client-server architecture, where a lightweight client, a server, and ancillary services work together and are maintained by CMS operators at CERN. As with most complex software, good monitoring tools are crucial for efficient use and longterm maintainability. This work gives an overview of the monitoring tools developed to ensure the CRAB server and infrastructure are functional, help operators debug user problems, and minimize overhead and operating cost. This work also illustrates the design choices and gives a report on our experience with the tools we developed and the external ones we used.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2015

TRIO: Burst Buffer Based I/O Orchestration, In: 2015 IEEE International Conference on Cluster Computing

The growing computing power on leadership HPC systems is often accompanied by ever-escalating failure rates. Checkpointing is a common defensive mechanism used by scientific applications for failure recovery. However, directly writing the large and bursty checkpointing dataset to parallel file systems can incur significant I/O contention on storage servers. Such contention in turn degrades bandwidth utilization of storage servers and prolongs the average job I/O time of concurrent applications. Recently burst buffers have been proposed as an intermediate layer to absorb the bursty I/O traffic from compute nodes to storage backend. But an I/O orchestration mechanism is still desirable to efficiently move checkpointing data from burst buffers to storage backend. In this paper, we propose a burst buffer based I/O orchestration framework, named TRIO, to intercept and reshape the bursty writes for better sequential write traffic to storage servers. Meanwhile, TRIO coordinates the flushing orders among concurrent burst buffers to alleviate the contention on storage server. Our experimental results demonstrated that TRIO could efficiently utilize storage bandwidth and reduce the average job I/O time by 37% on average for data-intensive applications in typical checkpointing scenarios.

Computer Science↗

NASA NTRS1997

International Ultraviolet Explorer Final Archive

CSC processed IUE images through the Final Archive Data Processing System. Raw images were obtained from both NDADS and the IUEGTC optical disk platters for processing on the Alpha cluster, and from the IUEGTC optical disk platters for DECstation processing. Input parameters were obtained from the IUE database. Backup tapes of data to send to VILSPA were routinely made on the Alpha cluster. IPC handled more than 263 requests for priority NEWSIPS processing during the contract. Staff members also answered various questions and requests for information and sent copies of IUE documents to requesters. CSC implemented new processing capabilities into the NEWSIPS processing systems as they became available. In addition, steps were taken to improve efficiency and throughput whenever possible. The node TORTE was reconfigured as the I/O server for Alpha processing in May. The number of Alpha nodes used for the NEWSIPS processing queue was increased to a maximum of six in measured fashion in order to understand the dependence of throughput on the number of nodes and to be able to recognize when a point of diminishing returns was reached. With Project approval, generation of the VD FITS files was dropped in July. This action not only saved processing time but, even more significantly, also reduced the archive storage media requirements, and the time required to perform the archiving, drastically. The throughput of images verified through CDIVS and processed through NEWSIPS for the contract period is summarized below. The number of images of a given dispersion type and camera that were processed in any given month reflects several factors, including the availability of the required NEWSIPS software system, the availability of the corresponding required calibrations (e.g., the LWR high-dispersion ripple correction and absolute calibration), and the occurrence of reprocessing efforts such as that conducted to incorporate the updated SWP sensitivity-degradation correction in May.

Source record↗

NASA NTRS2003

Aviation Safety Modeling and Simulation (ASMM) Propulsion Fleet Modeling: A Tool for Semi-Automatic Construction of CORBA-based Applications from Legacy Fortran Programs

Within NASA's Aviation Safety Program, NASA GRC participates in the Modeling and Simulation Project called ASMM. NASA GRC s focus is to characterize the propulsion systems performance from a fleet management and maintenance perspective by modeling and through simulation predict the characteristics of two classes of commercial engines (CFM56 and GE90). In prior years, the High Performance Computing and Communication (HPCC) program funded, NASA Glenn in developing a large scale, detailed simulations for the analysis and design of aircraft engines called the Numerical Propulsion System Simulation (NPSS). Three major aspects of this modeling included the integration of different engine components, coupling of multiple disciplines, and engine component zooming at appropriate level fidelity, require relatively tight coupling of different analysis codes. Most of these codes in aerodynamics and solid mechanics are written in Fortran. Refitting these legacy Fortran codes with distributed objects can increase these codes reusability. Aviation Safety s modeling and simulation use in characterizing fleet management has similar needs. The modeling and simulation of these propulsion systems use existing Fortran and C codes that are instrumental in determining the performance of the fleet. The research centers on building a CORBA-based development environment for programmers to easily wrap and couple legacy Fortran codes. This environment consists of a C++ wrapper library to hide the details of CORBA and an efficient remote variable scheme to facilitate data exchange between the client and the server model. Additionally, a Web Service model should also be constructed for evaluation of this technology s use over the next two- three years.

Sang, Janche↗

DOE OSTI2016

An asynchronous traversal engine for graph-based rich metadata management

Rich metadata in high-performance computing (HPC) systems contains extended information about users, jobs, data files, and their relationships. Property graphs are a promising data model to represent heterogeneous rich metadata flexibly. Specifically, a property graph can use vertices to represent different entities and edges to record the relationships between vertices with unique annotations. The high-volume HPC use case, with millions of entities and relationships, naturally requires an out-of-core distributed property graph database, which must support live updates (to ingest production information in real time), low-latency point queries (for frequent metadata operations such as permission checking), and large-scale traversals (for provenance data mining). Among these needs, large-scale property graph traversals are particularly challenging for distributed graph storage systems. Most existing graph systems implement a "level synchronous" breadth-first search algorithm that relies on global synchronization in each traversal step. This performs well in many problem domains; but a rich metadata management system is characterized by imbalanced graphs, long traversal lengths, and concurrent workloads, each of which has the potential to introduce or exacerbate stragglers (i.e., abnormally slow steps or servers in a graph traversal) that lead to low overall throughput for synchronous traversal algorithms. Previous research indicated that the straggler problem can be mitigated by using asynchronous traversal algorithms, and many graph-processing frameworks have successfully demonstrated this approach. Such systems require the graph to be loaded into a separate batch-processing framework instead of being iteratively accessed, however. In this work, we investigate a general asynchronous graph traversal engine that can operate atop a rich metadata graph in its native format. We outline a traversal-aware query language and key optimizations (traversal-affiliate caching and execution merging) necessary for efficient performance. We further explore the effect of different graph partitioning strategies on the traversal performance for both synchronous and asynchronous traversal engines. Our experiments show that the asynchronous graph traversal engine is more efficient than its synchronous counterpart in the case of HPC rich metadata processing, where more servers are involved and larger traversals are needed. Furthermore, the asynchronous traversal engine is more adaptive to different graph partitioning strategies.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS1994

MO/DSD online information server and global information repository access

Often in the past, standards and new technology information have been available only in hardcopy form, with reproduction and mailing costs proving rather significant. In light of NASA's current budget constraints and in the interest of efficient communications, the Mission Operations and Data Systems Directorate (MO&DSD) New Technology and Data Standards Office recognizes the need for an online information server (OLIS). This server would allow: (1) dissemination of standards and new technology information throughout the Directorate more quickly and economically; (2) online browsing and retrieval of documents that have been published for and by MO&DSD; and (3) searching for current and past study activities on related topics within NASA before issuing a task. This paper explores a variety of available information servers and searching tools, their current capabilities and limitations, and the application of these tools to MO&DSD. Most importantly, the discussion focuses on the way this concept could be easily applied toward improving dissemination of standards and new technologies and improving documentation processes.

Nguyen, Diem↗

NASA NTRS1996

Object-Oriented Geographical Database Model

Terbase is an Object-Oriented database system under development at the Jet Propulsion Laboratory (JPL). Terbase is designed for flexibility, reusability, maintenace ease, multi-user collaboration and independence, and efficiency. This paper details the design and development of Terbase as a geographic data server...

Terbase Object-Oriented database system Tactical M↗

NASA NTRS2009

Work Coordination Engine

The Work Coordination Engine (WCE) is a Java application integrated into the Service Management Database (SMDB), which coordinates the dispatching and monitoring of a work order system. WCE de-queues work orders from SMDB and orchestrates the dispatching of work to a registered set of software worker applications distributed over a set of local, or remote, heterogeneous computing systems. WCE monitors the execution of work orders once dispatched, and accepts the results of the work order by storing to the SMDB persistent store. The software leverages the use of a relational database, Java Messaging System (JMS), and Web Services using Simple Object Access Protocol (SOAP) technologies to implement an efficient work-order dispatching mechanism capable of coordinating the work of multiple computer servers on various platforms working concurrently on different, or similar, types of data or algorithmic processing. Existing (legacy) applications can be wrapped with a proxy object so that no changes to the application are needed to make them available for integration into the work order system as "workers." WCE automatically reschedules work orders that fail to be executed by one server to a different server if available. From initiation to completion, the system manages the execution state of work orders and workers via a well-defined set of events, states, and actions. It allows for configurable work-order execution timeouts by work-order type. This innovation eliminates a current processing bottleneck by providing a highly scalable, distributed work-order system used to quickly generate products needed by the Deep Space Network (DSN) to support space flight operations. WCE is driven by asynchronous messages delivered via JMS indicating the availability of new work or workers. It runs completely unattended in support of the lights-out operations concept in the DSN.

Zendejas, Silvino↗

DOE OSTI2014

Simplified Access to Massive Climate Modeling Datasets to Support End Users

Explore the source record for details and available documents.

NetCDF, SQL, efficient data subsetting, Climate Mo↗

DOE OSTI2015

Reproduction of the Yucca Mountain Project TSPA-LA Model Runs using TSPA Computing Systems

Sandia National Laboratories (SNL) conducted an evaluation of total system performance assessment (TSPA) related computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment (TSPA) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. This work is a continuation of the ongoing readiness evaluation reported in Lee and Hadgu (2014). The current work examined main components of the computing system identified in the previous work (Lee and Hadgu, 2014) to ensure the operational readiness of the TSPA-LA model capability on the server cluster. The TSPA computing hardware and storage system were replaced in late 2014 to maintain core capability and improve computation efficiency. One floating license of GoldSim Version 9.60.300 was installed on the upgraded cluster head node, and its distributed processing capability was mapped on the cluster processors. Other supporting software was tested and installed to support the TSPA-type analysis on the server cluster. All the TSPA-LA modeling cases were tested and verified for the model reproducibility on the upgraded 2014 server cluster (CL2014). All test runs were executed on multiple processors on the server cluster utilizing the GoldSim distributed processing capability, and all runs completed successfully. The model reproducibility verification was evaluated by two approaches: numerical value comparison and graphical comparison. The analysis demonstrated an excellent reproducibility of the TSPA-LA model runs on the upgraded server cluster. The 2014 server cluster and supporting software systems are fully operational to support TSPA- LA type analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Reproduction of the Yucca Mountain Project TSPA-LA Model Runs using TSPA Computing Systems

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

DGaaS: GPU as a Service on Distributed Computing System

In the rapidly evolving landscape of scientific computing, Graphics Processing Units (GPUs) have become indispensable for their unparalleled ability to handle parallel tasks in complex calculations, simulations, and data analysis. Their utility is further magnified in machine learning and AI applications, where they significantly accelerate model training and predictive analytics. Within this context, the Triton Inference Server emerges as a pivotal open-source tool, specializing in AI inferencing and optimizing GPU utilization across various platforms and frameworks. This paper presents an in-depth study on distributed High Throughput Computing (HTC), specifically focusing on the HTCondor framework and its resource provisioning tools, GlideinWMS and HEPCloud. These systems enable large-scale scientific experiments like CMS and DUNE to efficiently access and utilize vast computational resources. The paper explores the core architectural components of GlideinWMS, including jobs, user pools, and worker nodes, and discusses their integration with GPUs and the Triton server. The primary aim of this research is to develop a solution that optimizes GPU utilization by leveraging Glideins and containers. This approach allows computational jobs, particularly those involving AI models, to use GPUs only when essential, thereby facilitating efficient sharing of limited GPU resources. To validate this architecture, the study conducted three key tests involving custom scripts, container-based servers, and Triton server deployments. However, the study faces challenges, notably in locating the Triton server and ensuring secure remote access. To address these issues, future work will focus on developing a proxy mechanism and enhancing security protocols. In conclusion, this study offers a comprehensive roadmap for effective and efficient GPU utilization in distributed High Throughput Computing. It aims to contribute significantly to the scientific community by solving pressing problems and implementing robust solutions in collaboration with the GlideinWMS and HEPCloud teams. The research sets the stage for a more efficient, scalable, and cost-effective paradigm in scientific computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

UnifyFS: A Distributed Burst Buffer File System - 0.1.0

UnifyFS is a user-level burst buffer file system under active development. UnifyFS supports scalable and efficient aggregation of I/O bandwidth from burst buffers while having the same life cycle as a batch-submitted job. While UnifyFS is designed for N-N write/read, UnifyFS compliments its functionality with the support for N-1 write/read. It efficiently accelerates scientific I/O based on scalable metadata indexing, co-located I/O delegation, and server-side read clustering and pipelining.

ECP↗

DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗

NASA NTRS1980

On the congruent feature in NASTRAN

The congruent feature, which is a capability in NASTRAN that can contribute to significant increases in computational efficiencies, is discussed. The usage of the capability and the software design characteristics affecting it are explained. The factors affecting the efficiency of the feature are pointed out. The details pertaining to the software design of the congruent feature are presented; in particular, the congruent element table is described. Serveral examples employing the congruent feature are considered and comparisons of Element Matrix Generator module computer program update times with and without this feature are presented. The results clearly demonstrate the role of the congruent feature in increasing computational efficiencies and its applicability to large-size problems.

Pamidi, P. R.↗