HSF is about SW: project support an important goal
Good practices and appropriate tools can help sharing SW
Plenty of things to consider, often forgotten or postponed
A list of best practices have been drafted
On GitHub as everything done by HSF (documents repository)
Feedback is more than welcome!
License is one of the critical topic: HSF Technical Note available to help
Importance of documenting contribution workflow to get contributors
Recommendations are not HEP specific which is as it should be
A template is available for new projects: hsf_create_project.py
Build a CMake project
Configure build, tests, packaging, install: some technologies chosen among the possible ones
Created files with detailed documentation
Genesis of the starter template was a real project stripped down to its essentials
Test resources: plenty of resources available (for free) for continuous integration
Define a set of standards to be met to be accepted as an HSF packages?
Checklist is a good start
Should not be exclusive or prescriptive, point is to give guidelines
Oriented towards standalone projects. Another aspect is a project that needs to build against a number of other large pieces, and adds incremental functionality. Define standard ways of interfacing to existing projects.
ROOT, for example.
If project is cmake based, have a set of tools to interface to any other cmake based package
A point of the packaging WG is to define a canonical stack to enable projects following guidelines to work together
It’s an objective of the spack code sprint tomorrow
Set things up so that “if you use ROOT, just uncomment this line” etc
Should include requirement for documentation of needed env variables, other environment setup
Please give feedback on the draft best practices guideline document linked in Benedikt’s slides. Would be a very good output of the workshop to have a document representing the collective view of this group.
HSF objectives remembered: share expertize, raise awareness around projects, make easier for people to start new projects…
HSF concrete work organized in 6 WGs: information exchange, training, SW packaging, SW licensing, SW projects, dev tools and services
Communication: web, mailing list/fora, knowledge base, technical notes
Topical forums: SW Technology (former Concurrency Forum), Reconstruction Algorithms, Machine Learning
KB: emphasize easy adding/editing of contents, cross-linking
TN: anything useful that you want to share, already 4 available
Training: focus on sharing training material
SW Packaging: see this morning and tomorrow’s hackathon
Licensing: guidelines summarized in a TN
SW projects: see this morning too
HSF is also about fostering collaboration around SW: some successes in the last year (Next-generation Conditions DB, Track reconstruction, Gaudi
Food for thoughts during the workshop: to be discussed on Wednesday
HSF need a logo: vote for your preferred logo!
Project reviews: recently asked by GeantV as a first candidate. Review as an open process but report and conclusions by the review committee. Other interested project should contact HSF.
HSF StackExchange? A complement/alternative to forums. Overlap with other tools like RootTalk?
HSF Journal: publish S&C papers in peer-reviewed journals. May be good for larger visibility across communities, may help with career recognition. Springer ready to collaborate.
HSF need more decicated resources/people: required to achieve our goals, needs to be funded. Endorsement from ICFA or similar bodies?
HSF as a legal entity: currently not a foundation! May help with IPR/copyright handling, facilitate collaboration with industry… Also cons… discuss during the workshop,try to agree on a plan on Wednesday
Conda missing from packaging tools report? Didn’t have a Conda advocate in the group, though it was presented. Document was in circulation for comment for weeks, could have had input at any time. Did try to do some Conda follow-up and didn’t find volunteers. Can still welcome a revision that includes Conda properly. Could do it in this workshop.
In seeking resources by ‘HSF resources’ we do not mean HSF owned resources. We mean people identified and assigned with a fraction of their time to work through HSF projects. E.g. someone working on re-engineering tracking, work on that through the HSF to make the effort common as much as possible.
HSF demonstrated some initial collaborative activities but to address the challenges ahead of us (e.g. HL-LHC) we need more and dedicated resources
2 projects recently funded, DIANA (NSF) and AIDA2020 WP3 (EU): not started as part of HSF but they are examples of projects which will contribute to the overall HSF effort
Would help to put together a community roadmap describing HEP S&C challenges and directions (similar to P5 for HEP experiments in the US)
Community White Paper (CWP): R&D to prepare TDR, common framework that could help…
Could also provide a better context for applying for resources, engage computing people…
An NSF Software Institute plan is one example of a project proposal which could benefit from a clear community roadmap
CWP process: the proposal is for a series of HSF-branded workshops, starting this Fall and converging on a final workshop next Spring.
Conditions data are used at different stages of our workflows and are also refined during these stages (online processing, prompt reconstruction, bulk processing, …)
CMS developed during LS1 a new schema trying to simplify what was done in Run 1: this schema is in production in Run 2
ATLAS expressed interest: matching its own ideas of useful metadata after Run 1 experience
Condition data as Blobs: storage system not aware about the internal structure. Metadata in the tag table provides the necessary information to deserialize the object.
CMS chose Boost library for the serialization: several others choices possible (Google Protobuf, HDF5, ROOT or ascii formats like JSON and XML)
Architecture requirement: decouple clients from backend using a REST API, support many different kinds of backends including several relational platforms (Oracle, PostgreSQL,…), but eventually also NoSQL or file system. All business code to manage the conditions data is inside a server. The clients can be implemented in several languages because the communication is done via HTTP using JSON for the messages exchanged with the server.
Current prototype implemented in Java (based on JEE, Spring): easier integration into Frontier, the idea being to profit at most of the experience gained by the Frontier development group in terms of caching etc….
Client implementation for the moment on going. For python easy via automatic generation of client API based on swagger documentation.
AIDA2020: detector R&D, not specifically SW
WP3: SW related WP, promote commonalities, develop SW specific to projects only if needed
7 tasks around SW challenges
Strong links with current and future experiments: milestones to ensure that what is done in WP3 will be useful
DD4HEP: 1 of the HSF incubator projects providing a generic detector description toolkit
USolids/VecGeom: generic shape library, introduced in G4 10.x
Alignement: developed a fully automatized fast alignment procedure for LHCb VELO, in production
EDM Toolkit: PODIO project, also in HSF incubator, efficient I/O with PODs, currently being evaluated in the context of FCC/lcio
Framework extensions: improving parallel scheduling in frameworks, currently focused on Gaudi, later plan to use it in Marlin and PandoraSDK
DDG4: interface between DD4HEP and G4
Advanced tracking tools: follow-up of aidaTT from AIDA project
Parallelized tracking algorithms
Integration of LHC tracking algorithms into aidaTT
Connected to the Common Tracking Forum started recenly
Advance Particle Flow Algorithms (Pandora PFA toolkit)
LC, HL-LHC, LAr neutrino…
Data intensive ANAlisys for HEP: collaborative efforts around anlysis tools to make them a sustainable infrastructure in our community
Not a new SW
Not only about SW development but SW eco-system, carreer recognition, training…
ROOT as one core component: performance, ROOT I/O
Several PIs with a strong experience in complementary fields for addressing these challenges, involved in various HEP experiments or computer sciences (e.g. machine learning)
http://hepsoftware.org: the last generation (hopefully the last one, works nicely!)
No support for access restricted links yet (experiment-specific links may come)
Software projects organized by categories and can belong to one or several
Also allow descriptions and links to institutions, experiments…
Links to related projects
Also descriptions/links to sciences
Add your software, publicize SW&C KB!
See slides: many examples in them
Collaborative textbooks: promote collaboration around training materials
Centered on students/trainees
Currently 160 contributors, among them 40 core editors
Text at the heart of the system (external links or pdf are accepted but not encouraged)
Every user can make a book from a piece of training material
Support for draft pages (basically personal version of the material)
Tracking and notifications of modifications
Result of collaboration: sharing of effort
Ability to run snippets of code in the browser or in a container (AKHET, desktop as a container with Webdav file access)
Features to come
Collaborative editor à la Google Docs
A use case illustrating the challenge in biomed: leukemia, a disease with an heterogeneity of causes posing a real challenge for treatment, drug research…
Also heterogeneity of data sources/types
SW required to extract value from data
Bioconductor: collaborative and open-source SW
Interoperable components, rapid development, code reuse
Based on R
Low barrier of entry for users, training
10K users: world largest bioinformatics project
Driven by scientists
Contributor community increased over time
Several modes of interaction developed, including web site, mailing list, video-conference…
Importance of documentation: not only manual pages but user “vignettes” (narrative overview), workflows, citable papers with peer review
Increased use of GitHub for package development to allow early and open peer-review
Trade-off between standardization and being open and inclusive
Initial resistance of contributors afraid of losing their credits
Need a core team
Career of people involved have benefited
An organisation bridging scientific communities and computing infrastructures
A core team of experts on optimized data handling, big data analytics, efficient computing
Covering all scientific fields
Reuse community solution in other contexts: open-source, open access. Build upon expertise gained in different contexts
Funding done by NeSC: allow to retain expertise and to do the “technology transfer”
Very similar to HSF in a way… but cross-discipline
One example: analysis with ROOT for Xenon1T (dark matter).
Interconnection with Panda/mongoDB. Learned about the difficulty to glue different pieces! For example, problem with a bug in ROOT6 (memory leak with Python 3) not yet fixed upstream…
packaging/dependencies with Conda: Conda recipes for ROOT6 available on GitHub (nlesc project): available in Anaconda cloud (NLeSC/Packages/root-numpy)
Depsy: NSF funded project
Promote credits for SW contributions
Retrieve statistics about SW packages, projects reusing them, contributors…
Currently focused on Python and R: using the central repositories for packages like PyPi
SW citation often only informal and doesn’t permit identification and credit of contributors
Depsy takes into account indirect contributions (transitive credits): contribution to a project heavily reused
Aggregated impact of people across many projects
Motivations, potential: see slides
GPUs critical for performance
Need to provision a large amount of resources: no longer embarassingly //
DL in reconstruction: will require distribution of large datasets for training DNN
Proposal of a R&D project consisting to build a HEP framework on top of DL
Use Google TensorFlow framework to the DL part (open-source)
Success would mean HEP science is closer to other sciences: would benefit from more experts and offer a greater potential for collaboration with Data Scientists (and industry)
Need to start now if we want to be ready in 10 years
DL is not necessary easy… can also complicate things…
Potential of DL to find “unexpected things” still to be assessed in our context
A lot of investment in industry: interesting contributions by several companies. Many good reasons to collaborate but must also take into account the different culture regarding collaboration. OpenLab and its NDAs infrastructure can help.
Event classification: interest by LHCb and ALICE to meet the Run3 challenges, CMS also has plans
Object identification: great potential, raised by all experiments. Could benefit from industry experience with image recognition (self-driving cars…) if we could formulate the problem in a similar way…
Anomaly detection: a growing use case in the industry (e.g. security), may be relevant to HEP
Data analysis: all experiments with plan to have events almost ready for analysis when the leave the detector (online reconstruction for ALICE and LHCb). Potential for streamlined analysis, want to look at tools like Spark/Haddoop, OpenLab to help setting up a testbed
A lot of tools available to optimize data access and analysis, produced by industry but open-source
Event classification and triggering probably the most challenging use case: nothing really matching this in the industry but several frameworks can help
TMVA future discussed last September. Core requirements identified.
Maintain a set of core algorithms for HEP
Be able to interface with Python and R to allow the use of modern ML tools (done, PyMVA and RMVA)
Workflows integrated with ML frameworks
Several actions decided, most of them already done or in progress: see slides
New method to compute Feature Importance based on contribution to the classifier
New DL classes recently added supporting recent developments in the field: currently being optimized by TMVA developers
Cross validation also recently added
Work in progress in TMVA
Better separation of classification and regression and improved regression
Improved perf and memory usage
Better support for parallelization
Integration with ROOT-Books (Jupyter notebooks): Service for Web-based Analysis (SWAN). No SW installation required
Use general ROOT I/O instead of being limited to XML/JSON, import training output from other frameworks, import data from non ROOT formats (e.g. HDF5)
5 students will work this summer funded by Google (Google Summer of Code program)
Welcoming more contributions to TMVA development
V. Innocente: CMS has faced performance/mem footprint issues with TMVA DL classifiers in the context of reconstruction. We need to find solutions, e.g. reduced precision (FP16) which proves to be enough in many case.
Amir Farben: like TVMA interface but skeptical about trying to integrate anything into TMVA. Results is that it makes everything more complex, need to reinvent parallelisation optimisations…
YANDEX: 2 companies related to data science (YANDEX Data Factory) and research education (YANDEX School of Data Analytics, non profit)
School of Data Analytics member of LHCb since 2011
Several contributions to LHCb: topological trigger, data storage optimisation…
Several developments targeting reproducible research (all on GitHub, open-source, Apache 2.0)
Keras: Python library interfacing with tensor manipulation frameworks like TensorFlow (TF) and Theano
Keras provides building blocks for users to construct deep learning models at a high level of abstraction
Theano and TF are high level wrappers around C++: efficiently call C++ from high level language
Similar in many ways:
Computation described as a directed acyclic graph (DAG) with tensors flowing along the edges
Theano older, more stable API, faster for CNNs + interfaces with CuDNN (NVIDIA optimized kernels for NN operations)
TF backed by Google + easier for distributed/cluster computing
Theano: dynamic C code generation, stability optimization, execution speed optimisation, support for GPUs
TensorFlow: meant for large scale deployment on heterogeneous systems/HW, support synchronous and asynchronous training, evolved from DistBelief
Ongoing work to try to integrate TensorFlow with Athena
Challenge: knowledge preservation
We are good at keeping/saving “immediate” metadata related to data taking conditions
Not as good to save the context allowing to analyze properly the data: things trivial at the time of the experiment but easily lost
OpenData/preservation effort forces to address the “context metadata” preservation challenge. Beneficial for HEP community.
Lesson learned: much better to start the open data effort at the same time as data analysis is done but competing for human and computing resources…
Code optimizations for Run1/2 can allow to meet the CPU challenge (ATLAS and CMS already achieved a 5x since beginning of Run1) in trigger/tracker: not the case for HL-LHC
Need to start a R&D but many barriers
Small community, but many challenges ahead (HL-LHC,FCC)
ML currently used in tracking mainly for classification: just opening to pattern recognition with ML Tacking challenge
LHC detector SW has really been stress-tested: idea of starting an experiment-agnostic toolkit based on the experience gained (ACTS)
At the same time, often used on very old idea: such a toolkit may help to test new ideas
Data model of tracking was designed to serve both tracking experts and end-users. Lead to many compromises. Turned out not to be needed / not being used as anticipated.
Core tracking software put into standalone package ACTS now - on CERN’s gitlab. Decoupled from Gaudi/Athena, but with required adaptors to it for ease of integration.
Doesn’t want to replace/duplicate framework features: persistency, geometry… plugins for integration with frameworks. One the same line, do not duplicate G4.
ACTS will be used by ATLAS at the core piece of its Run3 tracking
Proof of concept by FCC connecting ACTS with DD4HEP
ACTS example for the tracking machine learning challenge
Hope to demonstrate GaudiHive integration in ATLAS TIM workshop (June)
License a question still to be tackled; question to the audience for help
Tracking ML challenge: can be an important step but need to agree on what we expect from it. CMS and ATLAS have different expectations from the LVL1 trigger, need to build kind of an hybrid detector description.
IML founded mi-2015: community effort to modernize ML tools used in HEP
By the community and for the community
Monthly open meeting with work on-going between meetings: continuous effort, many topics on the list of future meetings
All LHC experiments and some non LHC (e.g. DUNE)
Discussing various ideas, including new ermeging ones
Creating an HEP team around the topic
Promotes common ground for testing/evaluating tools
Contributing to providing interfaces to interesting ML tools in our SW ecosystem
Recent new activity: connection between MEM (Matrix Element Methods) and ML
IML working closely with CERN software group to ensure that ML packages provide good perfs and are long-term supported
Tutorials: critical to attract new users
IML also wants to promote/increase collaboration with ML experts
Ensure HEP experts have knownledge about the ML tools
Allow ML experts to understand HEP
Good synergy with DS@LHC (DataScience@LHC) workshops
Wants to contribute back to ML community HEP developments of general interest
IML evolved so much since its inception that an update of SW&C KB is needed! Will do it…
IML and HSF are sharing many objectives
Proposal made that IMF is used at the HSF forum for ML discussion: great idea, as long as we keep in mind that IMF has participants outside HSF
RAMP: the ML challenge idea with a group of people in the same room during 1 day
Emphazing collaboration rather than competition
Objective: get codes that can be reused in real life: code accessible, not just the results
Several RAMPs run in last year with a good success
Dependencies defined as a DAG: checked at installation time
Spack add-on repo created for HEP: on HSF GitHub
About 10 people in the room, more on video. Reports on G4 10.2 status, performance, em and hadronic physics developments, review of outstanding requirements being worked, detailed physics developments.
Current situation: fast online reconstruction + offline reconstruction
Most compute-intensive task is tracking
GPU-based tracker: found the balanced config was 1 GPU per CPU
Same code used for GPU and CPU: macros used for specific things, 90+% common code. Can use CUDA, OpenCL…
Had to accept different floating rounding leading to a different result for a very small fraction of the clusters
Managed to get tracker CPU time increasing linearly with the number of tracks: confident it will work for Run3
Run 3 challenge: currently <2 Khz readout rate, moving to continuous readout at 50 Khz interaction rate
Much more data: more efficient compression needed, based on reconstruction. Thus the need for full online reconstruction.
Reconstruction requires precise calibration, thus online calibration
New framework developed for Run 3: O2. Common development with FAIR (ALFA/FairROOT)
Better exploit of GPU concurrency: currently the number of tracks matches the internal GPU cores but will not be the case in the future: need to process several events concurrently in the same GPU. First tests promising: improved throughput. Current setup should be able to sustain 40 Khz.
ATLAS Performance Monitoring Board (PMB) in charge of monitoring information from job instrumentation and to report/understand in significant change in performance, memory footprint…
Every change has to be justified: many related to bugs
A justified change generally has to be compensated by another reduction
LS1 lesson: too many things to do (implement/test) in //
Linear algebra: CLHEP too slow, replaced by Eigen after a detailed evaluation
SIMD implementation available
Painful migration but done implementing an abstract interface to help future migration if needed
Magnetic field access was a big CPU consumer: big impact on simulation, a lot of improvements in this area
xAOD EDM: an important step to make analysis more efficient
Emphasis on code quality: critical to understand the code to improve it!
Use several compilers, gcc plugin to detect some ATLAS coding standard violation
Run3: framework evolution, no radical change
More concurrency, parallelism
Gaudi work in conjunction with LHCb and FCC
Offline code reviews can be a good occasion to make progress, identify problems… and ensure that developers document their design and implementation!
As others, reconstruction is the primary performance target: target, particle flow based object identification, high granularity calorimeter
Already a lot of improvements done since the beginning of data taking to improve tracking and reconstruction time
Need more to meet the HL-LHC challenge: computing resources are not going to match the increased time required by current implementation/approach
A lot of recent work in several different area: igprof has been an important tool to identify hotspots
Challenge of a fluid dynamics simulation: large number of points O(1000) in the 3 directions, 10 double precision variables
One step depends of the previous one: synchronisation required at each step
At least 2 steps at the same time in memory
Parallelization was a requirement: based on MPI, each process has its own data, data exchanges at their border
Bootlenecks: communications, border data duplication, load balancing
Currently works up to 10k processors: not enough
On the current HPC machine used, a run lasts 10 days of WC time (not counting data transfer, maintenance, etc): limited number of simulations per campaign (Õ(4))
Future directions explored:
Multiple threads per MPI tasks (openMP, pthreads…): benefit from shared memory
Use GPUs: vectorize as much as possible, optimize data exchange
Analysis is another challenge: many algorithms I/O bound
Try to read data once
Challenge of evolving a large code-base (O(100K) lines)… still work in progress
20 year old: reengineering/rewriting required in several areas, collaboration with the community required
Aim to get much of the changes for performances transparent for the users
Profiling is one particular area where collaboration is beneficial: see the igprof experience
Parallelisation: multithreading and multi-processing
PROOF remains the most used approach for perf improvement
New MultiProc package
Threading: new ThreadProc, same interface as multi-proc, output merging properly handled
Multi-node, on-demand analysis: SWAN
I/O perf also deserves work: exploring several approaches, including new serialization formats
Plan to exploit JIT capability of LLVM/CLANG for perf optimization at run time
Exploring functional chains à la Spark: user specifies what, ROOT decides how, providing room for optimizations
Need to explore multiple approaches to meet the challenges: not a single solution for all the use cases
Recent experience in MC G4 following an identified problem with memory footprint: problem turned out to be more complex than initially thought, a team of experts via different profiles assembled
People from experiments + G4 + experts in C++ profiling
Was used as a good occasion for mentoring
Importance of validation tests: experiment tests are not enough. Changes should not impact physics output. Was critical to convince users to use the new code.
Clear out cached data as soon as they are no longer needed in member functions
Don’t cache data into algorithmic objects
Avoid complex objects for small lookups: high memory cost
Concrete changes in a short time motivate people
Would be better better to catch design problems earlier: main way to achieve it is collaboration during development, like for the development of an analysis
Simulation represents 50% of LHC computing: GV wants to improve perf by a 2.5 to 5x
Redesign code with fine-grained granularity at the core
Scalar & vector interfaces (efficient use of SIMD instructions)
Concentrate first on geometry: 30-40% of the simulation CPU
Every component/class has a test and a benchmark both for scalar and vector interfaces
Importance of I/O
Basketization is another critical part
Several developments could be backported to G4.
Graeme: hard limit to perf improvement set by HW. Changes in HW have a much higher latency than SW
Olivier: relying on HPC, the HW question is different. Resources provided by national facilities. No control of it, GPUs will be part of next generation of machines.
D. Rohr (D.R.): GPUs are here, no discussion about using it. The question is the right balance between GPU and CPU: may change over time, also a chance to do it incrementally.
Liz: emphasize Oliver’s point of view. Same in the US: HEP encourage to join the HPC community and the HPC community has a roadmap that includes GPUs as a significant part of the next machine generation. Need to deal with HW heterogeneity increases the pressure on the build/packaging system.
Amir: need to work together as a community around the R&D about these issues and come with a common framework that could be used as the basis for the computing infrastructure in 10 years. HSF could be the good place for this effort.
Pere: first step with GPU is to demonstrate the gain and for this, we need to identify the areas to concentrate on.
D.R.: easier to rewrite/adapt a specialized application (like tracking) than frameworks like ROOT and GEANT
Vincenzo: is there still a place for commodity HW? Pressed to join the HPC community: at the same time, less opportunity for commodity with automatic power-off capabilities in new HW… ARM vs. HPC: may achieve the same throughput without the big pressure on parallelization.
Graeme: no doubt we’ll have to rewrite a significant part of our code but it needs to match a large part of the architecture phase space. Need to rely on compiler and build systems to help with this.
J. Apostolakis (J.A.): impossible to say what will be the dominating infrastructure 5 years from now, need to remain flexible and be able to support multiple infrastructures at a low cost.
Olivier: current approach is to put the implementation details for each architecture in libraries and hide them from the users. No clear standard that could simplify the problem: still need to adapt to each architecture.
D.R.: ALICE took the opportunity of the requirement to rewrite the tracker to redesign it with GPU in mind. But originally not written for GPU.
Amir: expect to see different GPU HW for gaming/deep learning (16-bit precision for higher perf) and HPC (double precision). AMD being out of the HW business, it is in a better position to change the SW landscape around these new HW (well aware of the need to support many HW/programming environments).
Pere: need to prevent users specifying low-level things. If the user gives a high level description, it is easier for the system to do internal optimization.
Amir: need to look at what big players did to offload things like compression to GPUs. Highly connected with I/O and efficient I/Os from GPUs.
Pere: we need to assess the exact impact on the overall workflow of offloading one particular part to specialized HW. And compare it with the effort to support them.
Amir: HSF could have the role of identifying the needed R&D and convince funding agencies to support it. We need topical workshops or topical sessions in regular meetings. Need to be proactive.
M. Sokoloff: if current NSF proposal moves forward, we’ll need to produce the Community White Paper in the next 15 months: The HSF is the natural organisation to do it.
Jeff: we need a metric for HSF work: maximize efficiency of overall people contributions.
Liz: ultimate goal remains to get additional people, not to play a zero-sum game.
Dario: importance of trainings to meet the challenge by increasing existing people expertise.
Thank you to all contributors for the high quality entries!
Thanks particularly and congratulations to Joschka Lingemann, CERN Fellow working with EP-SFT, who produced the winner preferred by a strong plurality (17 votes in 37 responses). A bottle of wine will be forthcoming for Joschka :-)
Michel, in particular, has kept comprehensive meeting notes, thank you!
Next HSF Newsletter (next week?) will provide a summary of the workshop
Clear message from the Doodle poll that the preferred date is week of Oct 24-28, probably early that week. Location not yet firmly decided, probably CERN.
If this doesn’t work for you, and you very much want to attend, let us know
Proposal to have a journal refereed, abstracted, indexed about HEP computing
Authoritative and central reference archive
Help with career paths
Do not restrict to HEP strictly: open to Data-Intensive Physics. Fields organized as large collaborations around large-scale experiments
Scope should cover all aspects of computing: from infrastructure to data analysis…
Continuous publishing: no paper/volume
Several open questions
Business model: hybrid (OA access per article, based on authors choice/constraints) vs. pure OA. Pure OA requires sponsorship.
Article types: regular articles, reviews, advanced tutorials, no letter, no proceedings
Allow publishing software (with DOI) or only articles about software?
May be useful to recognise merit of individuals, not so much internally in HEP but rather for people who will leave the field
Should be wider than HEP: we have more and more collaborations with other communities, we have common issues. At the same time must be somewhat restricted to people with similar (scale) problems.
Should we set up hepswcomp.stackexchange.com?
If yes, who will initiate it and take care of it?
Need an initial set of 40 questions
Andrea will try to bootstrap it
Proposal from Elizabeth Sexton-Kennedy and Fons Rademakers to seed HSF as a legal entity with an anchor project – much as Apache Software Foundation did with httpd – namely ROOT
Do this in parallel with – and without interference with – HSF’s ongoing activities
Would imply adding a Foundation Board, otherwise should not necessarily drive governance changes
Main motivation is to have an entity that can take ownership of SW contributions in the community: clear ownership required to allow clear licensing and collaboration with other fields.
Has to be neutral
Need a “core product” like Apache Server for ASF: ROOT?
Need to find sponsors, including funding agencies
Vincenzo: counter-example of Geant4, with a different form of organization, namely the G4 collaboration
Samir: if we want to increase collaboration with others, not clear that they all want to give up their IPR.
Andy: supportive of HSF being able of taking ownership of IPR but skeptical that existing big projects with huge IPR are good gunieapigs.
Markus: any Foundation would be established under a national law; would US institutes/agencies agree to give IP to a foundation under EU/CH law, or viceversa?
So long as HSF remains an entirely unresourced volunteer activity, there is no motivation or justification to go to a heavier governance
If/when the HSF becomes ‘resourced’ somehow, governance will need to be addressed
A suggestion from SLAC (Andy Hanushevsy): invite institutes to establish an ‘HSF Center’ as an anchor for their material (not person power) contributions to HSF
E.g. if institute X supports a Jenkins service for the use of HSF & associated projects, the contribution comes as part of their HSF Center
Would allow both the HSF and the institute to clearly identify contributions
For the institute, a means of clearly identifying participation and contribution to HEP S&C common projects via HSF support
For the HSF, concretely identifies contributing institutes and their contributions, and can help incite more
Maintain a list of services we would like Centers to pick up
What can/should be said, concluded, done?
Make a wish/priority list for dedicated effort?
To be defined by CWP & roadmap
Do people feel informed enough about what we do?
Should we continue like now or do something differently?
Broad community white paper that can be referenced, used, distilled, adapted for particular uses.
Such as the the white paper output of the NSF SI2-S2I2 project conceptualization phase
Such as WLCG’s HL-LHC directed computing planning?
Seems there is consensus that the HSF is the right place to do this
An investment towards gaining new resources through successful proposals
Look soon at what has to be investigated, what R&D is needed, do we need task forces to be formed, …
John: LHCC asked WLCG to write a TDR about HL-LHC computing. Same timescale, covering similar issues
Amir: collect the list of problems/challenges already existing
Peter: need more than a list of problems and possible solutions, funding agencies/governance bodies would like to see a roadmap.
R&D vs. production SW: roadmap should define the steps
M. Sokoloff: one important goal of CWP is to answer requests from funding agencies that experiments develop common solutions to common problems. At least common elements that can be picked/assembled by the different experiments.
Kickoff meeting in the US this Fall: “co-locate” with CHEP? Peter: should be driven/branded by HSF.
New activity around SW performance? Web site area?Should happen inside the SW technology forum but give more visibility to it in the “HSF space”.
Project template, packaging
The way the Mon morning discussion developed, towards managing a software stack and the issues taken up in the Packaging WG, suggests that we’re on the right track with the Packaging WG’s investigations into solutions
Great interest in Spack but recognition in the packaging discussion that there’s no silver bullet
Discussions here affirm the usefulness of the document describing and charting the features, pros, cons of the alternative
Input here towards a V2 of the document
Project template considered a good idea to show/implement best practices
Should be maintained actively
Feature request for putting a ROOT example in there
Common github repo created for next-gen conditions DB
HSF status, directions, scope
Once we have HSF road map, consider seeking ICFA (& ECFA) endorsement
No new things have popped up that ought to be added to HSF scope?
ML is the hot item of the day, and there we have the IML here today, and whatever relationship with the IML develops in the future. IML will use SW KB. Could also be part of HEP StackExchange.
Next F2F: CHEP
Report status, discuss hot topics, look for ways to leverage CHEP content
Investigate possibility of a plenary slot
A couple of hours? No approaches made yet to CHEP to schedule it
Investigate the possibility of the CWP kickoff meeting at the beginning of the week after CHEP (or before…)
Next workshop like this: ~ a year from now
Smaller topical workshop(s) according to need
Investigate the possibility of a community white paper meeting at the beginning of the week after CHEP, or before CHEP, in the Bay Area (SLAC willing to host)
Andrea Valassi will follow up on trying out the idea of hepswcomp.stackexchange.com; first will be to come up with 40 typical questions