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Failed to discover a Statement of need
section in paper
Wordcount for paper.tex
is 6545
Software report (experimental):
github.com/AlDanial/cloc v 1.88 T=0.02 s (709.2 files/s, 331315.9 lines/s)
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Language files blank comment code
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TeX 9 425 1744 4676
Ruby 1 8 4 45
YAML 1 1 0 43
Markdown 1 12 0 29
TOML 1 2 0 8
Julia 2 3 1 7
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SUM: 15 451 1749 4808
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Statistical information for the repository '6508ac24f7eee7b98ab151a5' was
gathered on 2023/10/12.
The following historical commit information, by author, was found:
Author Commits Insertions Deletions % of changes
Li Tang - LANL 3 114 57 100.00
Below are the number of rows from each author that have survived and are still
intact in the current revision:
Author Rows Stability Age % in comments
Li Tang - LANL 57 50.0 0.0 7.02
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Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- 10.1137/141000671 is OK
- 10.1287/opre.38.5.911 is OK
- 10.1175/JCLI3996.1 is OK
- 10.5194/gmd-9-1937-2016 is OK
- 10.1175/JCLI-D-16-0465.1 is OK
- 10.1145/3144769.3144778 is OK
- 10.1109/LDAV.2011.6092322 is OK
- 10.2312/EGPGV/EGPGV11/101-109 is OK
- 10.1145/1383529.1383533 is OK
- 10.1177/1094342020935991 is OK
- 10.11578/E3SM/dc.20180418.36 is OK
- 10.1111/j.1467-8659.2011.01964.x is OK
- 10.1109/CLUSTER.2018.00036 is OK
- 10.1109/TVCG.2014.2346458 is OK
- 10.1175/JCLI-D-16-0465.1 is OK
- 10.1109/TVCG.2015.2467411 is OK
MISSING DOIs
- 10.1029/2018jd028927 may be a valid DOI for title: Parametric sensitivity and uncertainty quantification in the version 1 of E3SM atmosphere model based on short perturbed parameter ensemble simulations
- 10.1109/drbsd754563.2021.00009 may be a valid DOI for title: TributaryPCA: Distributed, Streaming PCA for In Situ Dimension Reduction with Application to Space Weather Simulations
- 10.1036/1097-8542.757451 may be a valid DOI for title: Middle atmosphere dynamics
- 10.1109/smartgridcomm.2016.7778841 may be a valid DOI for title: Smartsim: A device-accurate smart home simulator for energy analytics
- 10.1145/3426462.3426465 may be a valid DOI for title: Chimbuko: A workflow-level scalable performance trace analysis tool
- 10.1175/jcli-d-15-0835.1 may be a valid DOI for title: Detection and attribution of changes in extreme temperatures at regional scale
- 10.1007/978-3-540-30218-6_19 may be a valid DOI for title: Open MPI: Goals, concept, and design of a next generation MPI implementation
- 10.2139/ssrn.3287769 may be a valid DOI for title: The Decline of Computers as a General Purpose Technology: Why Deep Learning and the End of Moore’s Law are Fragmenting Computing
- 10.1145/3490138.3490142 may be a valid DOI for title: In Situ Climate Modeling for Analyzing Extreme Weather Events
- 10.1175/1520-0442(2001)014<1959:codaet>2.0.co;2 may be a valid DOI for title: Characteristics of daily and extreme temperatures over Canada
- 10.5194/ascmo-2-79-2016 may be a valid DOI for title: Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions
- 10.1137/080731992 may be a valid DOI for title: Communication-optimal parallel and sequential QR and LU factorizations
INVALID DOIs
- https://doi.org/10.1029/2018MS001603 is INVALID because of 'https://doi.org/' prefix
- https://doi.org/10.1029/2020RG000708 is INVALID because of 'https://doi.org/' prefix
@luraess the links to the conflict of interest policy and code of conduct don't work.
@simonbyrne thanks for flagging, while we work on a fix, you can access those here https://juliacon.github.io/proceedings-guide/reviewer/#review_guidelines_and_process
:wave: @simonbyrne, please update us on how your review is going (this is an automated reminder).
:wave: @williamfgc, please update us on how your review is going (this is an automated reminder).
@simonbyrne @williamfgc Human asking about the status of the revision? Ideally, we can try to get this done in the coming weeks (2 ideally). Thanks so much for your contribution!
I'm on it, will update later this week.
Same here, I did a quick pass, but I need to go through it more carefully.
@simonbyrne @williamfgc also let me know if you still have issue checking tick-boxes.
This was an interesting application of embedding Julia inside another program for in situ analysis. Although I am not able to assess the scientific validity of the analysis (which seems incomplete), from a technical perspective, this is an important problem: as simulations reach higher resolutions, it becomes more expensive and difficult to store all the data for post-processing analysis, thus it is helpful if such analysis can be done while the simulation is running ("in situ").
Overall, I thought the paper was reasonable, but the exposition could be improved in a few places.
There is no linked code, (other than the TributaryPCA.jl package, and this doesn't meet most of the above criteria, such as documentation), so I can't really check off those relevant boxes. While the code may not strictly be required, it is a shame as I think this could be of interest to others who are attempting something similar. I would encourage the authors to make those available if possible.
It is also important to conduct garbage collection (GC) in this C interface for every time step. This is because arr and p_arr in the code snippet are allocated and then deallocated in E3SM for every time step, and the Julia runtime is not aware of this and hence needs explicit GC
This is not what the C code in code 2 is doing: as described in https://docs.julialang.org/en/v1/manual/embedding/#Memory-Management, the JL_GC_PUSH3
macro “roots” the variables so they are not collected, and JL_GC_POP
simply removes that root (so they can be cleaned up by the garbage collector). The Julia garbage collector runs automatically (unless it is disabled, but this is not described in the paper)
Some more details could be useful here: how did you load the Julia code? (e.g. did you make use of a precompiled system image?) How did you switch between (or switch off) the in situ analysis codes? Again, having the actual code available could make this easier.
I'm not sure Algorithm 1 is correct: shouldn't it reset the counter
to 0 if "global zonal mean zonal wind >= 0"? If it is negative for 20 days, does that count as 2 events?
There isn't much detail on the actual GEV fitting, and I found it difficult to understand. Is it fitting a different GEV distribution at every point? What is the prior distribution used for the GEV parameters? Some more details on this would be helpful (as well as linking to the code).
While I understand the problem it is trying to solve, I found it difficult to understand algorithm, and the Code 4 block is somewhat unclear:
X_par
? Is it a matrix or a vector, and what are the dimensions?V_par
?mul!
), yet not others. There are several calls to copy
which seem completely unnecessary.(I don't have access to the TributaryPCA paper, so couldn't look it up there either).
I would suggest either having a mathematical exposition of the algorithm, and/or making the code a bit more readable.
Is the data centered (i.e. do you subtract the mean)? The lack of red in Fig 3, PC1 suggests that it isn't, but that isn't clear. If you aren't then the interpretation changes slightly.
I don't quite understand this: it doesn't actually analyse the SSW case? It just seems to be an illustration of how one might do an analysis.
The details are a bit lacking. Was the data collected every time step? Or is it based on daily snapshots or averages? How long was the simulation?
I disagree with the interpretation of figure 3: you can't really assign "warmer vs colder" to principal components (the sign of eigenvectors doesn't matter). My interpretation of the figures is that:
sin(long)
and cos(long)
: my guess is that they're just capturing the daily temperature changes as the sun rotates around the earth? Can you plot the corresponding eigenvalues?While it's useful that you can capture this, it's not particularly scientifically interesting.
The SSW seems to have negligible overhead (which is not surprising since it is evaluated infrequently), so there is not much to say about the analysis.
It could be helpful if you could add the number of MPI ranks to Table 1?
I'd be interested to know more about the performance bottlenecks of the PCA code? Which parts were the most expensive? Did the Julia garbage collector cause problems with scaling?
Review: Julia for HPC In Situ Data Analysis with Julia for Climate Simulations at Large Scale
The work present the implementation of an “in-situ” framework with components written in Julia coupled with the well-known E3SM framework model. Two main post-processing tasks are evaluated: Sudden Stratospheric Warming (SSW) and principal component analysis (PCA). SSW is a smaller component while PCA is more computationally intensive due to the required Cholesky decomposition execution. I must admit I am not a doing expert in E3SM, but I can appreciate the computational effort in plugging Julia into an existing framework for a novel application.
Computational experiments show that in-situ provides reasonable overheads for a larger number of processing elements (PEs) usage per node.
Overall, it’s a straight-forward application of how Julia can be added to an existing framework and add value for post-processing tasks.
Major points:
Minor points:
Thanks for handing in your revision @simonbyrne @williamfgc . IMPORTANT Please go through the tick-box list and check all points that are addressed leaving the remaining as "to-dos". All tick-boxes will have to be checked upon publication.
@ltang85 now that the 2 reviewers gave you feedback, please address their comments as soon as possible. Also, please take a look at https://github.com/JuliaCon/proceedings-review/issues/134#issuecomment-1759604442 as you'd need to fix those references as well.
@ltang85 just checking in about the status of the revisions, given that you've got feedback since one month ago. Please try to address the issues raised by the reviewers in the coming weeks, and do not hesitate to reach out if there is anything we can help you with. Thanks.
@luraess I am still working on it and should be able to address all the comments in 2 weeks. Thanks.
@luraess I am still working on it and should be able to address all the comments in 2 weeks. Thanks.
Thanks - take your time. Was just checking in about progress.
@ltang85 seems there was no activity update for a while here. Can we get the process to a final stage in the coming week?
@luraess sorry for the delay, I will try to finish it by next week.
Excellent. We would like to wrap up the previous submissions ideally in the coming 1-2 weeks and then open the system for JuliaCon23.
@luraess Hi, here is the update: we have addressed most of the reviewers' comments but will need a few more days for the rest of the comments.
Great, thanks @ltang85 for the update and looking forward to finalising your submission!
@whedon generate pdf
My name is now @editorialbot
@editorialbot generate pdf
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@luraess I have addressed all the comments and uploaded the revised version. However, I failed to generate the pdf here. Will fix it tomorrow or Tuesday.
Thanks! Seems some figure is missing (https://github.com/JuliaCon/proceedings-papers/actions/runs/8683268959/job/23809007037#step:3:543).
@editorialbot generate pdf
:warning: An error happened when generating the pdf.
@editorialbot generate pdf
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
@editorialbot generate pdf
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
@luraess I have fixed the pdf issues and it is working now. I have also uploaded a response letter adressing all the reviewers' comments in the paper folder. Thanks.
@simonbyrne @williamfgc Thank you very much for reviewing this paper and provding the comments.
@ltang85 Thanks for submitting the revised version of the manuscript. I am checking it now and will let you know if any further changes are needed before we can accept the manuscript.
@simonbyrne @williamfgc If you find some time, I'd like to get your general feedback on whether the author addressed your suggestions in a way you are happy with. Also, please take some time to thick the boxes in here https://github.com/JuliaCon/proceedings-review/issues/134#issue-1939993021 .
Thanks!
Bump @simonbyrne @williamfgc - Thanks for swiftly verifying ☝️ such that we can finalise the publication! 🙏
@luraess I couldn't check the boxes in #134 (comment)
@luraess I couldn't check the boxes in #134 (comment)
I recall now this issue we had already at the beginning - let's leave that out. Could you however briefly assess whether the revised version of the manuscript draft adresses your comments and suggestions @williamfgc ? Thanks!
I just spotted typos: e.g. consqeuent...please proof-read. Other than that, my comments were addressed. Thanks!
@williamfgc @luraess
Thanks for the comments. We just proof-read it again and have fixed the typo you mentioned and a few other typos.
@editorialbot generate pdf
:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
@ltang85 Thanks for addressing the proof reading. While in the process of accepting your submission, I bumped on the README you link to from your paper regarding the steps to reproduce your research https://github.com/ltang85/In-Situ-data-Analysis-with-Julia-for-Climate-Simulations-at-Large-Scale/tree/main/src .
Please format this README using standard markdown syntax, in order to clearly separate code blocks from plain text. Use heading hierarchy to structure the content as well, and please remove the redundant horizontal lines. Once this last bit is fixed, we could proceed with final publication. Thanks!
Sorry, it has taken me a while to get to this.
@ltang85 Are you able to quickly summarize your changes, and how they addressed the comments?
@luraess @simonbyrne Sorry for my late reply.
@luraess I have cleaned up the README.md file using the standard markdown syntax. Please check and let me know if you have any comments.
@simonbyrne We have (1) added the code and instructions for reproducibility, (2) corrected the mistakes in the SSW algorithm, (3) added more description texts for SSW and TributaryPCA, (4) made edits to address your other comments such as results, tables, and garbage collection. More details can be found in our response letter to your comments: https://github.com/ltang85/In-Situ-data-Analysis-with-Julia-for-Climate-Simulations-at-Large-Scale/blob/main/paper/Response%20Letter.pdf
Submitting author: !--author-handle-->@ltang85<!--end-author-handle-- (Li Tang) Repository: https://github.com/ltang85/In-Situ-data-Analysis-with-Julia-for-Climate-Simulations-at-Large-Scale Branch with paper.md (empty if default branch): Version: v1.0 Editor: !--editor-->@luraess<!--end-editor-- Reviewers: @simonbyrne, @williamfgc Archive: 10.5281/zenodo.12104097
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Review checklist for @simonbyrne
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file include a list of authors with their affiliations?Content
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[ ] Methodology: is the approach taken in the work justified, presented with enough details and reference to reproduce it?
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Review checklist for @williamfgc
Conflict of interest
[ ] As the reviewer I confirm that I have read the JuliaCon conflict of interest policy and that there are no conflicts of interest for me to review this work.
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Functionality
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Paper format
paper.tex
file include a list of authors with their affiliations?Content