Introduction to the UNIX File System
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Basic UNIX↗
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Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters
Abstract Background The Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs. Results Profiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload. Discussion Optimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.
59 BASIC BIOLOGICAL SCIENCES↗
DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels"
DayCent data and results for:J. L. Field, T. L. Richard, E. A. Smithwick, H. Cai, M. S. Laser, D. S. LeBauer, S. P. Long, K. Paustian, Z. Qin, J. J. Sheehan, P. Smith, M. Q. Wang, L. R. Lynd, Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels. Proceedings of the National Academy of Sciences (2020). https://doi.org/10.1073/pnas.1920877117 This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script 'autodaycent.py' (written for Python 2.7) opens an interactive command line routine that facilitates:* Calibrating the DayCent pine growth model.* Initializing DayCent for a set of case studies sites.* Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation. * Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model (https://www.nrel.colostate.edu/projects/daycent/) compatible with other operating systems are available upon request.Please send questions/comments to John.L.Field@gmail.com
Agro-ecosystem function and prediction↗
BAD2matrix: Phylogenomic matrix concatenation, indel coding, and more
Common steps in phylogenomic matrix production include biological sequence concatenation, morphological data concatenation, insertion/deletion (indel) coding, gene content (presence/absence) coding, removing uninformative characters for parsimony analysis, recording with reduced amino acid alphabets, and occupancy filtering. Existing software does not accomplish these tasks on a phylogenomic scale using a single program. BAD2matrix is a Python script that performs the above-mentioned steps in phylogenomic matrix construction for DNA or amino acid sequences as well as morphological data. The script works in UNIX-like environments (e.g., LINUX, MacOS, Windows Subsystem for LINUX).
59 BASIC BIOLOGICAL SCIENCES↗
High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding
Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. Here, the standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.
59 BASIC BIOLOGICAL SCIENCES↗
Spack-Based Packaging and Development for HEP
The art event-based analysis framework is used by multiple Intensity Frontier and other experiments and projects in High Energy Physics (HEP). A system of tools and scripts based around a Fermilab-originated package management system called UNIX Product Support (UPS) has evolved to provide development, packaging, release management, and distribution of the art suite, related experimental software and external dependencies in relocatable binary form. The existing system has limitations in environments such as macOS with System Integrity Protection (SIP) enabled and the various High Performance Computing (HPC) systems that the field of HEP is increasingly looking to leverage. This has led to a search for other ways of providing experiments with a packaging and build system that meets their needs. We describe our efforts to develop a packaging and release management system based on the Spack HPC package management tool to replace the existing UPS-based system. We additionally describe a companion system: SpackDev and cetmodules, allowing simultaneous development of multiple HEP software packages in a consistent environment. A successful implementation of the intended system would have applicability across HEP rather than merely to the users of the art framework.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Pseudonymization at Scale: OLCF’s Summit Usage Data Case Study
The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems, which offer reliable and efficient management of large-scale computational and data resources. Understanding these analyses’ needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to extract workload characteristics from system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user’s point of view with ordinary Unix commands.In this paper, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about the users of OLCF which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset, which reduced the linkability of the dataset to the users’ identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, which is approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.
Maheshwari, Ketan↗
Greggd
greg(g)d - Global runtime for eBPF-enabled gathering (w/ gumption) daemon Recently the linux kernel has added support for low-level kernel monitoring and profiling through a in-kernel virtual machine. The tooling around these new features (the extended Berkley Packet Filter or eBPF for short) is not mature and is difficult to use. Benefits from eBPF are especially hard to realize while trying to do large scale deployments and integrate with existing metric analysis stacks. A tool was needed to enable loading and collecting data from eBPF programs on large scale HPC systems. Given the problems above it was obvious we needed some wrapper program to compile, load, and collect data from eBPF programs running in the kernel. This tool needed to be lightweight without a heavy set of dependencies, relatively stable between different kernel versions, and integrate nicely with existing widely used metric collection tools. We wrote a program that wraps the eBPF tooling and sends data to our metric gathering tool. eBPF programs are either compiled using the host compiler stack, or loaded in the kernel directly from an object file. These programs are then attached to the system calls that we want to profile. Whenever these system calls are run, the eBPF program collects information of interest and writes that to memory. Our wrapper program polls these memory locations, reads and formats the data, then sends the information to a local unix socket. Our other monitoring tools are configured to read from that socket and send it to the rest of our metric monitoring stack for analysis.
Voss, Joseph [Oak Ridge National Lab. (ORNL), Oak ↗
DSS-SimPy-RL (Open-DSS and SimPy based Cyber-Physical RL environment) [SWR-23-29]
Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. With the advancement of reinforcement learning (RL) based techniques, gradually the conventional optimization based solvers are being replaced with RL approach where there is uncertainty in the environment such as renewable generation or cyber system emulation. However, to train an agent efficiently, it requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for the power systems based on some well-known simulators, similarly there are environment for communication domains. While majority of the cyber emulators are based in an UNIX environment, the power simulators are based in the Windows-based operating system, the generation of cyber-physical mixed domain RL environment has been challenging. Existing co-simulation methods are efficient but resource and time intensive to generate large scale data set for training RL agents. Hence, this software focuses on development and validation of a mixed domain RL environment using Open DSS for the physical side and leverages a discrete event simulator python package, SimPy, for cyber-side emulation which is Operating Systems agnostic. Further utilizing this software co-simulation and training RL agents for re-routing based resilient control for network reconfiguration and volt-var control in power distribution feeder are performed.
Sahu, Abhijeet↗
Replete v. 2.0
SAND2021-15136 O Replete is a Java library that contains several common and useful utilities in a variety of categories including expression parsing, multi-threading, data pipelines, user interface, database connectivity, Unix shell emulation, information generalization, and Maven standardization. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
McClain, Jonathan↗
Scientific Core Library Stack (SCLS) v2026
SCLS (Scientific Core Library Stack) is an opinionated build and packaging system for scientific computing libraries developed at Lawrence Berkeley National Laboratory. It produces a coherent, reproducible stack of numerical libraries — including BLAS/LAPACK, MPI, sparse direct and iterative solvers, graph partitioners, and parallel I/O libraries (e.g., PETSc, SLEPc, HDF5, NetCDF, MUMPS, OpenBLAS) — that work together without manual repair by downstream scientific software. From a single recipe-and-flavor model, SCLS produces native RPM packages for RHEL-family Linux, DEB packages for Debian/Ubuntu, direct Unix-style prefix installs for HPC and locked-down environments, and native macOS builds. Multiple build "flavors" (e.g., GCC+OpenBLAS, GCC+MKL, Intel+MKL, debug) coexist in distinct prefixes on the same host. Compared to general-purpose meta-build frameworks, SCLS is deliberately curated rather than infinitely configurable. It enforces deterministic, audit-friendly behavior: explicit build dependencies, no silent feature autodetection, a clear open-source license policy, and rpath-based runtime linkage so installs integrate cleanly with standard package-manager workflows.
Messe, Christian [Lawrence Berkeley National Labor↗
Data for Robust Paths to Net Greenhouse Gas Mitigation and Negative Emissions via Advanced Biofuels
This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script “autodaycent.py” (written for Python 2.7) opens an interactive command line routine that facilitates: Calibrating the DayCent pine growth model; Initializing DayCent for a set of case studies sites; Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation; and Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model compatible with other operating systems are available upon request.
Feedstock Production↗
HERO WEC Belt Test Data
The following submission includes raw and processed data from the 2024 Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) belt tests conducted using NREL's Large Amplitude Motion Platform (LAMP). A description of the motion profiles run during testing can be found in the run log document. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in the form of TDMS files. Data was then processed using Python and MATLAB and converted to MATLAB workspace, parquet, and csv file formats. During Data processing, a low pass filter was applied to each array and the arrays were then resampled to common 10Hz timestamps. A MATLAB data viewer script is provided to quickly visualize these data sets. The following arrays are contained in each test data file: - Time: Unix seconds timestamp - Test_Time: Time in seconds since beginning of test - POS_OS_1001: Encoder position in degrees (the encoder is located on the secondary shaft of the spring return and is driven by the winch after a 4.5:1 gear reduction) - LC_ST_1001: Anchor load cell data in lbf - PRESS_OS_2002: Air spring pressure in psi This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.
16 TIDAL AND WAVE POWER↗
Converting from NIS to Redhat Identity Management
The Jefferson Lab (Jlab) accelerator controls network has transitioned to a new authentication and network service interface. The new system uses the Redhat Identity Manager (IdM) as a single integrating front end to the Lightweight Directory Access Protocol (LDAP) and a replacement for NIS and the Kerberos authentication service. This system allows for integration of access and authentication across Unix and Windows environments and across different Jlab computing environments, including across firewalls. The decision making process, conversion steps, issues and solutions will be discussed.
McGuckin, T. S.↗
Verification and Validation of the PLTEMP/ANL Code for Thermal-Hydraulic Analysis of Experimental and Test Reactors
This report documents the verification and validation (V&V) of the PLTEMP/ANL Version 4.3 code. A brief description of the code and its history are given in Section 1.1. Section 1.2 provides an overview of PLTEMP/ANL verification and validation work done to date. Section 1.3 lists some publications by other authors on PLTEMP/ANL verification and validation. Section 1.4 describes a UNIX script developed to routinely verify the code after a code modification. The V&V work documented in this report was performed over several years, as code development progressed. In each verification/validation testing discussion, the version of PLTEMP/ANL which was initially used for the V&V work is referenced, but all of the testing presented in this report is applicable to current release version 4.3 of the code.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Summary of NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training
The NNSA Seismic Cooperation Program (SCP) sponsored Stephen Myers (LLNL), Michael Begnaud (LANL), Brian Young (SNL) and Istvan Bondar (Research Center for Astronomy and Earth Sciences, Hungary) to serve as a presenters/trainers at the “NDC Capacity Building Workshop and Regional Seismic Travel Time (RSTT) in combination with Data Sharing and Integration Training” September 4-8 2022 in Muscat, Oman (See Appendix A for the agenda). The workshop and training (workshop from here forward) was organized by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) Provisional Technical Secretariat (PTS). The first half of the week was devoted to NDC workshop activities, and the second half was devoted to RSTT training. Fifty-five participants from 27 countries and the CTBTO-PTS attended the 5-day workshop (See Appendix B for list of participants and countries of origin). Presentations from the PTS described the International Monitoring System (IMS), International Data Centre (IDC) products, and metrics of regional data utilization. Contributed presentations from each country’s scientists included descriptions of regional and national networks, methods of data analysis, and needs for material and technical assistance. Training included an overview of the RSTT method and instruction on how to locate seismic events with the iLoc program, which utilizes RSTT travel times to reduce bias in event location estimates. Methods of seismic tomography and the need for a high-quality tomographic set, including seismological “ground truth”, were emphasized. Seismological “ground truth” or “GT” is a term that has come to mean both events with known location and events with well-characterized locations that are estimated using seismological data, typically with epicenter accuracy of 5 km or better. Notably, the instructional platform has migrated from UNIX shell scripts to Jupyter Notebooks. Jupyter Notebooks have the advantage being more visually intuitive, including display of graphics within the notebook. Each notebook includes every processing step that participants need to reproduce the entire exercise.
58 GEOSCIENCES↗
The Perils of AI-Assisted Time Conversion Critical Errors in J2000 Time to UNIX/UTC Conversions Using AI Assistants (Technical Memorandum)
This memo addresses a critical issue encountered when using AI assistants for time system conversions, specifically converting data tagged with J2000 seconds through UNIX time to UTC. Initial AI responses to this common conversion task were fundamentally incorrect, with errors exceeding 37 seconds - enough to cause significant issues in many applications. This document demonstrates the problem, quantifies the error, and provides best practices for using AI tools safely in technical work. Both Claude 4.5 Sonnet and ChatGPT GPT-5.5 Thinking gave the same answer.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Teaching Computational Reproducibility for Neuroimaging
We describe a project-based introduction to reproducible and collaborative neuroimaging analysis. Traditional teaching on neuroimaging usually consists of a series of lectures that emphasize the big picture rather than the foundations on which the techniques are based. The lectures are often paired with practical workshops in which students run imaging analyses using the graphical interface of specific neuroimaging software packages. Our experience suggests that this combination leaves the student with a superficial understanding of the underlying ideas, and an informal, inefficient, and inaccurate approach to analysis. To address these problems, we based our course around a substantial open-ended group project. This allowed us to teach: (a) computational tools to ensure computationally reproducible work, such as the Unix command line, structured code, version control, automated testing, and code review and (b) a clear understanding of the statistical techniques used for a basic analysis of a single run in an MR scanner. The emphasis we put on the group project showed the importance of standard computational tools for accuracy, efficiency, and collaboration. The projects were broadly successful in engaging students in working reproducibly on real scientific questions. We propose that a course on this model should be the foundation for future programs in neuroimaging. We believe it will also serve as a model for teaching efficient and reproducible research in other fields of computational science.
60 APPLIED LIFE SCIENCES↗