HTCondor Workshop Autumn 2021

20 Sept 2021, 14:00 → 24 Sept 2021, 18:30 Europe/Paris

(teleconference)

Helge Meinhard (CERN), Todd Tannenbaum (University of Wisconsin Madison (US))

The HTCondor Workshop Autumn 2021 has been held as purely on-line a virtual event via videoconferencing, due to current pandemic and related travel restrictions. The workshop was the seventh edition of the series usually hosted in Europe (and usually called "European HTCondor workshop") after the successful events at CERN in December 2014, ALBA in February 2016, DESY in June 2017, RAL in September 2018, JRC in September 2019 and on-line in September 2020. The workshops are opportunities for novice and experienced users of HTCondor to learn, get help and have exchanges between them and with the HTCondor developers and experts. They are open to everyone world-wide; they consist of presentations, tutorials and "office hours" for consultancy, covering the HTCondor CE (Compute Element) as well. They also feature presentations by users on their projects and experiences. The workshops address participants from academia and research as well as from commercial entities alike from all around the world (but expect the session timings to take particular account of European and US timezones).

Group Photo

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Shared document for questions, comments, answers [PDF]

Monday 20 September

Workshop session

1 Welcome and logistics

Speaker: Helge Meinhard (CERN)

Recording

Slides [PDF]

2 What’s new in HTCondor? What is upcoming?

Speaker: Todd Tannenbaum (CHTC, U Wisconsin-Madison)

Recording

WhatsNew_European_Workshop_Sept_2021.pdf

WhatsNew_European_Workshop_Sept_2021.pptx

3 What to measure and why

Speaker: Miron Livny (CHTC, U Wisconsin-Madison)

09 21 CW EU.pdf

09 21 CW EU.pptx

Recording

16:35 Group photograph

Participants wishing to appear on the workshop group photo should be present and activate their camera in Zoom.
Thanks to Sebastian Lopienski (CERN) to serve as "photographer"!

16:40 Break

Workshop session

4 New GPU architectures MIGs, multiple jobs per GPU, etc.

Speaker: John Knoeller (University of Wisconsin-Madison)

Recording

TJs_GPUs_in_HTCondor_CWEU2021.pdf

TJs_GPUs_in_HTCondor_CWEU2021.pptx

5 Dealing with dynamic and mixed workloads

At INFN-T1 several competing groups submit their payloads to the HTCondor pool with a high level of heterogeneity. In particular, the same group can submit both multi core and single core jobs, and the ratio between these two can change quite rapidly; this and other unpredictable user side behaviours can make difficult for HTCondor administrators to provide user groups with a satisfactory fair share of the available computing resources.
As an attempt to reduce usage imbalances between different user groups, a system to self adjust disparities has been developed and it is being used with good results so far.

Speaker: Stefano Dal Pra (Universita e INFN, Bologna (IT))

htc_fairshare_1.pdf

Recording

6 A new HTCondor monitoring for CNAF Tier-1

The CNAF Tier-1, composed of almost 1000 worker nodes and nearly 40000 cores, completed its migration to HTCondor more than one year ago. After having adapted existing monitoring tools (built with Sensu, Influx and Grafana) to work with the new batch system, an effort has started to collect a more rich and “condor oriented” set of metrics that are used to provide better insights on the pool status.
The data are collected into a PostgreSQL database, which makes them also available for further analysis or different applications, and presented by a specifically designed dashboard built using the dash and plotly python libraries.

Speaker: Federico Versari (University of Bologna)

HTCondor Workshop 2021.pdf

Recording

Tuesday 21 September

Workshop session

7 Auto-scaling in the cloud: Intelligent HTCondor resource management

HTCondor is an effective tool to rank and match execute resources against a set of jobs with explicit resource requirements. In the cloud, a subtly different challenge is presented: how to rank execute resource configurations that will be automatically created to run idle jobs (auto-scaled on-demand).

We describe recent work by the HTCondor team and Google Cloud to provide built-in support for commonly desired patterns in cloud auto-scaling. For example, a job can require co-location of execute resources with data stored as Google Cloud Storage objects. Alternatively, a group of jobs might seek to expand into as many cloud regions as possible in search of cost savings or to minimize the wall-clock time of a particular workflow.

Speakers: Dr Ross Thomson (Google), Tom Downes (Google)

HTCondor_ Autoscaling in the Cloud (European Workshop 2021).pdf

Recording

8 Introducing the HTCondor 9.0 Series for Users

Speaker: Christina Koch (CHTC, U Wisconsin-Madison)

2021-09-new-user-features.pdf

Recording

9 Using SciTokens in HTCondor 9

Speaker: Brian Bockelman (CHTC, U Wisconsin-Madison)

Recording

SciTokens_HTCondorWeekEU2021.pdf

16:35 Break

Workshop session

10 Synthetic populations for personalized policy

Public policy design generally targets ideal households and individuals representing average figures of the population. However, statistics only make sense when referring to large numbers, less so when we are trying to represent real people belonging to the actual population. In fact, referring to the characteristics of the average citizen, the policy maker loses the capacity to represent the diversity of the population at large, negatively affecting minorities and under-represented people.
Statistics over the population are usually given as univariate figures. Typically, knowing that e.g. in a certain area live 55% women and 30% university educated people do not give a high quality information for the distribution and we may actually misinterpret what the real issue is.
One way to improve the representation of the diversity is to recur to multivariate distributions in spatial modelling, e.g. creating high quality aggregates for specific use cases.
Using real data to give these representations poses important privacy concerns, because knowing the combination of features in certain areas might give away the identity of some citizens.
In recent years, the performances of supercomputers skyrocketed, and at the same time the access for data scientists to high performance computing technologies has been democratized, offering to policy makers the unprecedented opportunity for creating tailored policy using a completely synthetic population.
Policy simulation models can take as input synthetic individuals that resemble the actual ones but are stripped out of their identities, as they are synthetic by design. Synthetic individuals are created inter-linking census data, behavioural surveys and other available data sets and the result is a synthetic population with average statistics similar to the actual one by design, to the point that one is not able to tell if an individual belongs to the real or to the synthetic population, with the advantage of being relieved from most privacy concerns.
In this context, we have generated the synthetic population of France, based on Census data from INSEE (French Institute of Statistics and Economic Studies) and other data sets available.
The main data sets involved: information at individual level, such as age, sex, level of education, household composition, etc.; information at household level, such as sociodemographic characteristics as well as information about the dwelling and its location, characteristics, category, type of construction, comfort, surface area, number of rooms etc.; information about mobility to the workplace, including their main socio-demographic characteristics, as well as those of the household to which they belong; information about the mobility to education facilities.
Additional datasets included the map of the census tracks used by the INSEE, and data from the cadastre about properties cross linked with geographic data form the French Geographic Institute (IGN) and OpenStreetMap to create as detailed a map with the distribution of dwellings by type. Data about the location of educational establishments was extracted from the Ministry of Education, while the location of economic activities was obtained by cross referencing the data from INSEE which covers 64 different economic activities with the buildings for the OpenStreetMap database.
By linking the datasets above it was possible in the first instance to create families and households and then to attribute them to individual buildings. This combinatorial optimization is known as the Variable Size Multiple Knapsack Problem. This problem can be tackled in different ways, no solution is perfect but there is always a trade-off between precision and computational intensity. Aiming at a better precision is only possible when the input data adds up useful information. Sometimes, the least computationally intensive solutions offer reasonable results as well. In our case, having any additional attribute to houses, e.g. year when built, would make people positioning much more precise. Another source of uncertainty is that, in the absence of better information, we assumed that larger families would inhabit larger housing surfaces, which is obviously not always the case.
Notwithstanding these limitations, we modelled the synthetic population of 63 million people, in 35 million households allocated in 10 million houses in France including their travel to work and study places behaviour. The computations were performed in batch processing on the JRC Big Data Analytics Platform (BDAP), that uses HTCondor as a job scheduler and Docker Universe set up.
Around 35k jobs were performed, one for each French commune, each job taking 1 CPU. At our disposal were 20 servers of 40 CPUs each and 1TB RAM, and relatively unlimited storage space. The machine set was shared with other users.
The scripts were in Bash and Python, including libraries such as Numpy, Pandas, geoPandas and Shapely.
One of the challenges was to deal with very large CSV files in input (e.g. one of 12GB). Opening these files (in Pandas) required that the memory demand in the Condor submit file had to be so large (~200GB) that machines were seldom allocated to our jobs.
The idea was to subset from the large files only the records that belong to the job that is performing, so to make a query for a certain value (zip code processed by the job) along a certain column (zip code column), and subset only those lines that correspond to that query and save the result in a new CSV.
A benchmark of several libraries used for subsetting was performed and eventually the winner was AWK, offering the best speed and inferior memory requirement.

Speaker: Margherita Di Leo

Abstract_HTC_workshop.odt

animation.gif

DiLeo_HTC_workshop_21.pdf

Recording

11 Operations in the HTCondor pool at CERN

During the last year the HTCondor pools at CERN have passed the milestone of 300K cores. In this presentation we will cover some of the operational challenges we have found and the various monitoring and automation solutions deployed to tackle them. We will review as well how we envision the evolution of the service in the coming years.

Speaker: Luis Fernandez Alvarez (CERN)

OperationsHTCondorCERN.pdf

Recording

12 Running multiple experiment workflows on heterogeneous resources, the RAL experience

The RAL Tier-1 runs an almost 50,000 core HTCondor batch farm which supports not only the four major LHC experiments but an increasing number of other experiments in the High Energy Physics, Astronomy and Space communities. Over the last few years there has been an increasing diversification both in the types of jobs the experiments expect to run and also in the hardware available to run jobs. It has proved very difficult to schedule jobs so they run efficiently on the correct hardware, while respecting the experiment fair shares and requiring minimum admin intervention. This talk describes our experiences over the last year, what we have tried and our future plans.

Speaker: Alastair Dewhurst (Science and Technology Facilities Council STFC (GB))

HTCondorWorkshop20210921.pdf

HTCondorWorkshop20210921.pptx

Recording

Wednesday 22 September

Workshop session

13 Introducing HTCondor 9.0 for Admins

Speaker: Gregory Thain (University of Wisconsin-Madison)

NewAdmin.pdf

NewAdmin.pptx

Recording

14 Upgrading to HTCondor 9.0

The upgrade to HTCondor 9.0 isn't as smooth as previous upgrades. This talk discusses why and what to do.

Speaker: Todd Lancaster Miller (University of Wisconsin Madison (US))

Recording

Upgrading HTCondor (v2).pdf

Upgrading HTCondor (v2).pptx

16:20 Break

Workshop session

15 HTCondor Integration with Hashicorp Vault for Oauth Credentials

HTCondor now has an optional integration with open source Hashicorp Vault for managing Java Web Tokens (JWTs) such as Scitokens. In the integration, the condor_submit command calls out to htgettoken (developed at Fermilab) to communicate with a Vault service. Vault takes care of the Open ID Connect protocol (which is based on Oauth 2.0) to communicate with a token issuer and securely storing powerful refresh tokens while returning less powerful Vault tokens that can be used to obtain even less powerful access JWTs. In the initial authentication, htgettoken redirects the user to their web browser for approval, but subsequent requests for access JWTs use either the Vault token or renew the Vault token using Kerberos authentication. A Vault credmon component holds Vault tokens that it exchanges for access JWTs to renew in batch jobs. The submit file can specify just the name of a token issuer configured in Vault, and it can optionally specify specific scopes or audiences to further restrict the power of access JWTs. This talk will describe the HTCondor Vault integration in detail.

Speaker: Dave Dykstra (Fermi National Accelerator Lab. (US))

HTCondorEurope_Talk_Vault20210922.pdf

Recording

16 Open stage - Show Us Your Toolbox, followed by office hours

This session is intended to serve as an opportunity for administrators to show the audience how they do their work with and on HTCondor - what are the most useful tools for them to perform their work? Why are they so useful? What do they look (and feel) like?

In case of interest, the session could be split into breakouts at some point in time.

This session will not be recorded. We would appreciate a 'sanitized' (if needed) slide by the contributors for the records, though.

The "open stage" will be followed by breakouts for office hours - see the 'Videoconference' link in Indico for the links.

Speaker: Todd Tannenbaum (University of Wisconsin Madison (US))

Thursday 23 September

Workshop session

17 The CMS Submission Infrastructure deployment

The CMS experiment at CERN requires vast amounts of computational power in order to process, simulate and analyze the high energy particle collisions data that enables the CMS collaboration to fulfill its research program in Fundamental Physics. A worldwide-distributed infrastructure, the LHC Computing Grid (WLCG), provides the majority of these resources, along with a growing participation from international High Performance Computing facilities. The combined processing power is harnessed for CMS use by means of a number of HTCondor pools operated by the CMS Submission Infrastructure team. This contribution will present a detailed view of our infrastructure, encompassing multiple HTCondor pools running in federation, aggregating hundreds of thousands of CPU cores from all over the world. Additionally, we will describe our High Availability setup, based on distributed (and in some cases replicated) infrastructure, deployed between the CERN and Fermilab centres, to ensure that the infrastructure can support critical CMS operations, such as experimental data taking. Finally, the present composition of this combined set of resources (WLCG, CERN, OSG and HPC) and their roles will be explained.

Speaker: Antonio Perez-Calero Yzquierdo (Centro de Investigaciones Energéticas Medioambientales y Tecnológicas)

20210923_CMS_Submission_Infrastructure_deployment.pdf

Recording

18 Operations and Monitoring of the CMS HTCondor pools

The CMS Submission Infrastructure team manages a set of HTCondor pools to provide the vast amount of computing resources that are required by CMS to perform tasks like data processing, simulation and analysis. A set of tools that enables automation of regular tasks and maintenance of the key components of the infrastructure has been introduced and refined over the years, allowing the successful operation of this infrastructure. In parallel, a complex monitoring system that includes status dashboards and alarms have been developed, enabling this effort to be performed with minimal human intervention. This contribution will describe our technology and implementation choices, how we monitor the performance of our pools in diverse critical dimensions, and how we react to the alarms and thresholds we have configured.

Speaker: Saqib Haleem (National Centre for Physics (PK))

20210923_CMS_HTCondor_Operations_Monitoring.pdf

Recording

19 Self-Checkpointing Jobs in HTCondor

Speaker: Christina Koch (CHTC, U Wisconsin-Madison)

2021-HTCondorEurope-SelfCheckpointing.pdf

Recording

16:35 Group photograph

Participants wishing to appear on the workshop group photo should be present and activate their camera in Zoom. (Those who already attended the session on Monday don't need to be present.)
Thanks to Sebastian Lopienski (CERN) to serve as "photographer"!

16:40 Break

Workshop session

20 Q / A / discussion: HTCondor philosophy and architecture

Links to material to consult beforehand will be published here.

Speaker: Gregory Thain (University of Wisconsin-Madison)

HTCondor Philosophy and architecture

Recording

21 Q / A / Discussion: HTCondor Python Bindings

Prior to this Q&A session, if you are unfamiliar with the HTCondor Python Bindings, please work through the online Python Bindings tutorials. Use the Binder link at the following URL to launch an interactive Jupyter notebook in your web browser with the tutorials already loaded:

https://htcondor.readthedocs.io/en/v9_0/apis/python-bindings/tutorials/index.html

Speaker: Jason Patton (University of Wisconsin-Madison)

HTCondor Python Bindings Tutorials

PythonBindingsQA.pdf

PythonBindingsQA.pptx

Recording

22 Q / A / Discussion: Negotiator policy and configuration

Links to material to consult beforehand will be published here.

Speaker: Gregory Thain (University of Wisconsin-Madison)

Negotiator.pdf

Negotiator.pptx

Or watch this talk about Accounting Groups and Group Quotas

Please review this video about user priories and the negotiator as preparation for our discussion

Recording

23 Q / A / Discussion: Using IDTokens for authentication in HTCondor 9

Links to material to consult beforehand will be published here.

The attached slide deck was designed to (hopefully) be self-explanatory to an HTCSS administrator. Bring forth any questions to the discussion!

Speaker: Todd Tannenbaum (University of Wisconsin Madison (US))

IDTOKENS Documentation in the HTCondor Manual

IDTOKENS_European_Workshop_Sept_2021.pdf

IDTOKENS_European_Workshop_Sept_2021.pptx

Recording

Friday 24 September

Workshop session

24 What new / upcoming for the HTCondor-CE?

Speaker: Brian Lin (CHTC, U Wisconsin-Madison)

2021-09-24.htcondor-ce-new-and-upcoming.pdf

2021-09-24.htcondor-ce-new-and-upcoming.pptx

Recording

25 Job Router Transforms

Speaker: John Knoeller (University of Wisconsin-Madison)

2021-09-24.htcondor-ce-5-jr-transforms.pdf

2021-09-24.htcondor-ce-5-jr-transforms.pptx

Recording

26 How to write a custom file xfer plugin

Speaker: Mark Coatsworth (University of Wisconsin-Madison)

File Transfer Plugins_ Why You Want Them and How To Write Them.pdf

Recording

16:30 Break

Workshop session

27 Campus Research and Facilitation

Speaker: Lauren Michael (CHTC, U Wisconsin-Madison)

2021AutumnHTCondorWorkshop_CampusFacilitation_LMichael.pdf

Recording

28 In silico detection of (CRISPR) spacers matching Betacoronaviridae genomes in gut metagenomics sequencing data

In silico detection of (CRISPR) spacers matching Betacoronaviridae genomes in gut metagenomics sequencing data

Leoni G.1,2, Petrillo M.2, Puertas-Gallardo A.2, Sanges R.1, Patak A.2

1. Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste (Italy);
2. Joint Research Center (JRC), Ispra (Italy).

The CRISPR-Cas system is the major component of the prokaryotic adaptive immune system (Horvath
& Barrangou, 2010). CRISPR, which stand for “Clustered Regularly Interspaced Short Palindromic
Repeats”, are genomics arrays found in the DNA of many bacteria. They consist in short repeated
sequences (size 23-47 base pairs), separated by unique sequences of similar length (spacers), that often
derives from phages and viral infections, plasmids or mobile genetic elements (Shmakov et al., 2017).
CRISPRs are coupled to specific “CRISPR-associated genes” (Cas) to form the so called CRISPR-Cas
system. This system has the primary role to protect prokaryotes from virus and other mobile genetic
elements activity by conferring immunological memory from past infections (Garneau et al., 2010;
Nussenzweig & Marraffini, 2020).
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a single-stranded RNA virus that
rapidly emerged in 2019. In humans, it causes coronavirus disease 2019 (COVID-19), an influenza-like
disease that is primarily thought to infect the lungs with transmission through the respiratory route.
However, clinical evidence suggests that the intestine may present another viral target organ, a potential
hiding place for the virus, which may explain the persistence of COVID-19 symptoms after months
from patients recovery (Lamers et al., 2020). Furthermore, extra-pulmonary clinical manifestations of
COVID-19 are reported. Nonetheless, although a link between SARS-CoV-2 infection and the misregulation
of the gut microbiome was suggested, its involvement remains largely unexplored (Brooks
& Bhatt, 2021).
To simultaneously verify both the potential existence of SARS-CoV-2 in gut and to test whether the
human gut microbiome may be stressed by SARS-CoV-2 infection, we developed a bioinformatic
workflow based on the detection of Betacoronaviridae-specific CRISPR spacers from ~28,000 public
available gut metagenomics data. To process such “Big Biological Data” in a reasonable CPU time, we
relied on a HTCondor High Throughput Computing System, characterized by 10 Tflops of computing
capacity and more than 80 Tbytes of storage. Computing block was composed by 8 Nodes IBM x3550
with two Intel Xeon processor E5-2600 v3 product family CPUs with10 cores 2.6 GHz, two QPI links
up to 9.6 GT/s each and 256 GB of RAM. While our work is still ongoing, preliminary results revealed
the presence of some Betacoronavirus-specific spacers in the human gut metagenomics data, proving
that SARS-like viruses can target human gut and suggesting that the human microbiome can be
stressed by the systemic viral infection. By collecting further data, we aim to strengthen our results as
well as to investigate the effects of the SARS-COV-2-induced microbiome stress to the host.

Bibliography

Brooks, E. F., & Bhatt, A. S. (2021). The gut microbiome: A missing link in understanding the
gastrointestinal manifestations of COVID-19? Molecular Case Studies, 7(2), a006031.
https://doi.org/10.1101/mcs.a006031
Garneau, J. E., Dupuis, M.-È., Villion, M., Romero, D. A., Barrangou, R., Boyaval, P., Fremaux, C.,
Horvath, P., Magadán, A. H., & Moineau, S. (2010). The CRISPR/Cas bacterial immune system
cleaves bacteriophage and plasmid DNA. Nature, 468(7320), 67–71.
https://doi.org/10.1038/nature09523
Horvath, P., & Barrangou, R. (2010). CRISPR/Cas, the Immune System of Bacteria and Archaea.
Science. https://www.science.org/doi/abs/10.1126/science.1179555
Lamers, M. M., Beumer, J., Vaart, J. van der, Knoops, K., Puschhof, J., Breugem, T. I., Ravelli, R. B.
G., Schayck, J. P. van, Mykytyn, A. Z., Duimel, H. Q., Donselaar, E. van, Riesebosch, S.,
Kuijpers, H. J. H., Schipper, D., Wetering, W. J. van de, Graaf, M. de, Koopmans, M., Cuppen,
E., Peters, P. J., … Clevers, H. (2020). SARS-CoV-2 productively infects human gut
enterocytes. Science. https://www.science.org/doi/abs/10.1126/science.abc1669
Nussenzweig, P. M., & Marraffini, L. A. (2020). Molecular Mechanisms of CRISPR-Cas Immunity in
Bacteria. Annual Review of Genetics, 54(1), 93–120. https://doi.org/10.1146/annurev-genet-
022120-112523
Shmakov, S. A., Sitnik, V., Makarova, K. S., Wolf, Y. I., Severinov, K. V., & Koonin, E. V. (2017). The
CRISPR Spacer Space Is Dominated by Sequences from Species-Specific Mobilomes. mBio,
8(5), e01397-17. https://doi.org/10.1128/mBio.01397-17

Speaker: Gabriele Leoni (SISSA)

Leoni.Gabriele.Abstract.2021.pdf

Leoni.HTCondor2021.pdf

Recording

29 Workshop wrap-up

Speaker: Helge Meinhard (CERN)

2021-09-24-HTCondorWorkshop-Wrapup.pdf

Recording