In Berlin for the #eve4climate meeting: https://eve4climate.org/. Lots more suit jackets than I am used to … you can draw a number of conclusions from that statement!
Given I complained about the paucity of #pasc23 tooting, I will have to do better!
Modern software engineering in research is increasingly assisted by a plethora of sophisticated development tools. In this presentation, we will take a look at typical tooling and automation processes. Motivated by a human-centric approach for development, we will discuss selected challenges and solutions for open teams in their interactions. On the technical side, will cover aspects of development for C++/Python, testing, review, team coordination and support mechanisms.
Particle accelerators, among the largest, most complex devices, demand increasingly sophisticated computational tools for the design and optimization of the next generation of accelerators that will meet the challenges of increasing energy, intensity, accuracy, compactness, complexity and efficiency. It is key that contemporary software take advantage of the latest advances in computer hardware and scientific software engineering practices, delivering speed, reproducibility and feature composability for the aforementioned challenges. We will describe the Exascale software stack that is being developed at the heart of the Beam pLasma Accelerator Simulation Toolkit (BLAST). As a highlight, we present how the US DOE Exascale Computing Project (ECP) application WarpX uses the power of GPUs at scale, which won the 2022 ACM Gordon Bell Prize. We will present performance results on the first Exascale supercomputer for the modeling of laser-plasma acceleration. We then describe how we are leveraging the ECP experience to develop a new generation ecosystem of codes that, combined with machine learning, will enable modeling from the ultrafast to the ultraprecise for future accelerator design and operations.
"Anti Patterns of Scientific Machine Learning to Fool the Masses: A Call for Open Science" was my keynote at the #PASC23 conference in Davos, Switzerland.
The presentation slides are minimalistic, but you'll get the gist: https://doi.org/10.6084/m9.figshare.23579808.v1
Opening Keynote, PASC23, Monday, June 26, 2023. Davos, Switzerland. Description: An anti-pattern is a frequently occurring pattern that is ineffective and risks being counterproductive. The term comes from software engineering, inspired by the classic book “Design Patterns” (highlighting desirable and effective patterns for code). Over the years, the term has spread beyond software to other fields, like project management. An anti-pattern is recurring, it has bad consequences, and a better solution exists. Documenting anti-patterns is effective in revealing how to make improvements. This talk will call attention to anti-patterns in scientific machine learning—faintly tongue-in-cheek—with a call to do better. Scientific machine learning promises to help solve problems of high consequence in science, facing challenges like expensive or sparse data, complex scenarios, stringent accuracy requirements. It is expected to be domain-aware, interpretable, and robust. But realizing the potential is obstructed by anti-patterns: performance claims out of context, renaming old things, incomplete reporting, poor transparency, glossing over limitations, closet failures, overgeneralization, data negligence, gatekeeping, and puffery. Open science—the culture and practices that lead to a transparent scientific process and elevate collaboration—is the lens through which we can see a path for improvement. In the Year of Open Science, this talk is a call for a better way of doing and communicating science.
We are glad to announce that the #PASC23 proceedings were just released online.
Thank you to all committee members, organizers, reviewers and authors for the high quality contributions!
We had a new record no. of submissions, see the best 26 papers:
https://dl.acm.org/doi/proceedings/10.1145/3592979
Anti Patterns of Scientific #MachineLearning to Fool the Masses
Keynote by Lorena Barba at #PASC23 where she gave several examples
Renaming old things: Just add a NN somewhere and call it “deep”
Opening Keynote, PASC23, Monday, June 26, 2023. Davos, Switzerland. Description: An anti-pattern is a frequently occurring pattern that is ineffective and risks being counterproductive. The term comes from software engineering, inspired by the classic book “Design Patterns” (highlighting desirable and effective patterns for code). Over the years, the term has spread beyond software to other fields, like project management. An anti-pattern is recurring, it has bad consequences, and a better solution exists. Documenting anti-patterns is effective in revealing how to make improvements. This talk will call attention to anti-patterns in scientific machine learning—faintly tongue-in-cheek—with a call to do better. Scientific machine learning promises to help solve problems of high consequence in science, facing challenges like expensive or sparse data, complex scenarios, stringent accuracy requirements. It is expected to be domain-aware, interpretable, and robust. But realizing the potential is obstructed by anti-patterns: performance claims out of context, renaming old things, incomplete reporting, poor transparency, glossing over limitations, closet failures, overgeneralization, data negligence, gatekeeping, and puffery. Open science—the culture and practices that lead to a transparent scientific process and elevate collaboration—is the lens through which we can see a path for improvement. In the Year of Open Science, this talk is a call for a better way of doing and communicating science.
LorenaABarba
Bryan Lawrence
Volodymyr
Balthasar Reuter
ax3l
HPC Guru