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1. Improving the Speed of gem5’s GPU Regression Tests

   James Braun; Matthew D. Sinclair (June 2023, 5th gem5 Users' Workshop)

   n recent years, we have been enhancing and updating gem5’s GPU support. First, we have enhanced gem5’s GPU support for ML workloads such that gem5 can now run. Moreover, as part of this support, we created, validated, and released a Docker image that contains the proper software and libraries needed to run GCN3 and Vega GPU models in gem5. With this container, users can run the gem5 GPU model, as well as build the ROCm applications that they want to run in the GPU model, out of the box without needing to properly install the appropriate ROCm software and libraries. Additionally, we have updated gem5 to make it easier to reproduce results, including releasing support for a number of GPU workloads in gem5-resources and enabling continuous integration testing on future GPU commits. However, in an effort to provide sufficient coverage, the cur- rent testing support for GPU tests requires significant runtime both for the nightly and weekly regression tests. Currently most of these regression tests test the GPU SE mode support, since GPU FS mode support is still nascent. Unfortunately, much of this time is spent parsing input files to create arrays and other data structures that the GPU subsequently computes on. Although SE mode does not simulate the system calls needed to read these input files, nevertheless this still represents a significant overhead that increases runtime and prevents other tests (potentially providing additional coverage) from being run in that same timeframe. In an effort to address this, in the work we have been working on utilizing SE mode’s avoiding modeling system calls to speed up the runtime of the GPU regression tests. Specifically, we redesign the input reading phase of these GPU tests to create and use mmap’d files for their input arrays (which SE mode completes all at once) instead of reading in the files entry by entry. In doing so, we see significant reductions in runtime of at least 29% 

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2. Improving the Speed of gem5’s GPU Regression Tests

   James Braun; Matthew D. Sinclair (June 2023, 5th gem5 Users' Workshop)

   In recent years, we have been enhancing and updating gem5’s GPU support. First, we have enhanced gem5’s GPU support for ML workloads such that gem5 can now run. Moreover, as part of this support, we created, validated, and released a Docker image that contains the proper software and libraries needed to run GCN3 and Vega GPU models in gem5. With this container, users can run the gem5 GPU model, as well as build the ROCm applications that they want to run in the GPU model, out of the box without needing to properly install the appropriate ROCm software and libraries. Additionally, we have updated gem5 to make it easier to reproduce results, including releasing support for a number of GPU workloads in gem5-resources and enabling continuous integration testing on future GPU commits. However, in an effort to provide sufficient coverage, the cur- rent testing support for GPU tests requires significant runtime both for the nightly and weekly regression tests. Currently most of these regression tests test the GPU SE mode support, since GPU FS mode support is still nascent. Unfortunately, much of this time is spent parsing input files to create arrays and other data structures that the GPU subsequently computes on. Although SE mode does not simulate the system calls needed to read these input files, nevertheless this still represents a significant overhead that increases runtime and prevents other tests (potentially providing additional coverage) from being run in that same timeframe. In an effort to address this, in the work we have been working on utilizing SE mode’s avoiding modeling system calls to speed up the runtime of the GPU regression tests. Specifically, we redesign the input reading phase of these GPU tests to create and use mmap’d files for their input arrays (which SE mode completes all at once) instead of reading in the files entry by entry. In doing so, we see significant reductions in runtime of at least 29% 

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3. River Plumes and Idealized Coastal Corners: ROMS and MATLAB files

   https://doi.org/10.17632/m4ndry2dw3.2  

   Whitney, Michael (January 2024, Mendeley Data)

   This dataset is associated with a manuscript on river plumes and idealized coastal corners with first author Michael M. Whitney. The dataset includes source code, compilation files, and routines to generate input files for the Regional Ocean Modeling System (ROMS) runs used in this study. ROMS output files in NetCDF format are generated by executing the compiled ROMS code with the input files. The dataset also includes MATLAB routines and datafiles for the analysis of model results and generation of figures in the manuscript. The following zip files are included: ROMS_v783_Yan_code.zip [ROMS source code branch used in this study] coastalcorner_ROMS_compilation.zip [files to compile ROMS source code and run-specific Fortran-90 built code] coastalcorner_ROMS_input_generate_MATLAB.zip [ROMS ASCII input file and MATLAB routines to generate ROMS NetCDF input files for runs] coastalcorner_MATLAB_output_analysis.zip [MATLAB data files with selected ROMS output fields and custom analysis routines and datafiles in MATLAB formats used in this study] coastalcorner_MATLAB_figures.zip [custom MATLAB routine for manuscript figure generation and MATLAB data files with all data fields included in figures] coastalcorner_tif_figures.zip [TIF image files of each figure in manuscript] 

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4. Electric field and conductivity measurements in the stratosphere

   https://doi.org/10.5061/dryad.q2bvq83qh  

   McCarthy, Michael; Holzworth, Robert; Thomas, Jeremy (May 2023, Dryad)

   This dataset is a compressed archive that includes 2 data files in binary format, 4 files in csv format, and 2 metadata files (pdf documents) that provide information on how to interpret the data. This data was collected from instruments deployed on two stratospheric balloons, launched a day apart in late June 2021 from central Oregon. They flew on upper level winds to the west, out over the northeastern Pacific Ocean. The measurement objective was a multi-day set of vertical electric field and polar conductivity measurements at roughly a 10 minute cadence, and from widely separated locations in the stratosphere. The binary format data is comprehensive, including everything that was measured. The csv files have been processed from the raw data files into calibrated, timed, and time-ordered ASCII files containing the primary science measurements and some essential auxiliary measurements such as measurement location and time. This data was collected in an effort to (1) compare the fair-weather return current density at different geographic locations and (2) to compare the fair-weather current density with global thunderstorm activity. 

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5. Replication Data for: Long-Range Attraction between Graphene and Water/Oil Interfaces

   https://doi.org/10.7910/DVN/MEBRW1  

   Abeywickrama, Avishi; Adamson, Douglas H; Schniepp, Hannes C; Schniepp, Hannes C (January 2023, Harvard Dataverse)

   Atomic force microscopy (AFM) image raw data, force spectroscopy raw data, data analysis/data plotting, and force modeling. File Formats The raw files of the AFM imaging scans of the colloidal probe surface are provided in NT-MDTs proprietary .mdt file format, which can be opened using the Gwyddion software package. Gwyddion has been released under the GNU public software license GPLv3 and can be downloaded free of charge at http://gwyddion.net/. The processed image files are included in Gwyddions .gwy file format. Force spectroscopy raw files are also provided in .mdt file format, which can be opened using NT-MDTs NOVA Px software (we used 3.2.5 rev. 10881). All the force data were converted to ASCII files (*.txt) using the NOVA Px software to also provide them in human readable form with this data set. The MATLAB codes used for force curve processing and data analysis are given as *.m files and can be opened by MATLAB (https://www.mathworks.com/products/matlab) or by a text editor. The raw and processed force curve data and other values used for data processing are stored in binary form in *.mat MATLAB data files, which can be opened by MATLAB. Organized by figure, all the raw and processed force curve data are given in Excel worksheets (*.xlsx), one per probe/substrate combination. Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. Codes for Force Curve Processing The three MATLAB codes used for force curve processing are contained in this folder. The text file Read me.txt provides all the instructions to process raw force data using these three MATLAB codes. Figure 3B, 3C – AFM images The raw (.mdt) and processed (.gwy) AFM images of the colloidal probe before and after coating with graphene oxide (GO) are contained in this folder. Figure 4 – Force Curve GO The raw data of the force curve shown in Figure 4 and the substrate force curve data (used to find inverse optical lever sensitivity) are given as .mdt files and were exported as ASCII files given in the same folder. The raw and processed force curve data are also given in the variables_GO_Tip 18.mat and GO_Tip 18.xlsx files. The force curve processing codes and instructions can be found in the Codes for Force Curve Processing folder, as mentioned above. Figure 5A – Force–Displacement Curves GO, rGO1, rGO10 All the raw data of the force curves (GO, rGO1, rGO10) shown in Figure 5A and the corresponding substrate force curve data (used to find inverse optical lever sensitivity) are given as .mdt files and were exported as ASCII files given in the same folder. The raw and processed force curve data are also given in *.mat and *.xlsx files. Figure 5B, 5C – Averages of Force and Displacement for Snap-On and Pull-Off Events All the raw data of the force curves (GO, rGO1, rGO10) for all the probes and corresponding substrate force curve data are given as .mdt files and were exported as ASCII files given in this folder. The raw and processed force curve data are also provided in *.mat and *.xlsx files. The snap-on force, snap-on displacement, and pull-off displacement values were obtained from each force curve and averaged as in Code_Figure5B_5C.m. The same code was used for plotting the average values. Figure 6A – Force–Distance Curves GO, rGO1, rGO10 The raw data provided in Figure 5A – Force Displacement Curves GO, rGO1, rGO10 folder were processed into force-vs-distance curves. The raw and processed force curve data are also given in *.mat and *.xlsx files. Figure 6B – Average Snap-On and Pull-Off Distances The same raw data provided in Figure 5B, 5C – Average Snap on Force, Displacement, Pull off Displacement folder were processed into force-vs-distance curves. The raw and processed force curve data of GO, rGO1, rGO10 of all the probes are also given in *.mat and *.xlsx files. The snap-on distance and pull-off distance values were obtained from each force curve and averaged as in Code_Figure6B.m. The code used for plotting is also given in the same text file. Figure 6C – Contact Angles Advancing and receding contact angles were calculated using each processed force-vs-distance curve and averaged according to the reduction time. The obtained values and the code used to plot is given in Code_Figure6C.m. Figure 9A – Force Curve Repetition The raw data of all five force curves and the substrate force curve data are given as .mdt files and were exported as ASCII files given in the same folder. The raw and processed force curve data are also given in *.mat and *.xlsx files. Figure 9B – Repulsive Force Comparison The data of the zoomed-in region of Figure 9A was plotted as Experimental curve. Initial baseline correction was done using the MATLAB code bc.m, and the procedure is given in the Read Me.txt text file. All the raw and processed data are given in rGO10_Tip19_Trial1.xlsx and variables_rGO10_Tip 19.mat files. The MATLAB code used to model other forces and plot all the curves in Figure 9B is given in Exp_vdW_EDL.m. 

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