publist.yml

- title: Cling–the new interactive interpreter for ROOT 6
  author: V Vassilev, Ph Canal, A Naumann, P Russo
  abstract: |
    Cling is an interactive C++ interpreter, built on top of Clang and LLVM
    compiler infrastructure. Like its predecessor Cint, Cling realizes the
    read-print-evaluate-loop concept, in order to leverage rapid application
    development. Implemented as a small extension to LLVM and Clang, the
    interpreter reuses their strengths such as the praised concise and expressive
    compiler diagnostics. We show how to match the interpreter concept to the
    compiler library and generalize common set of requirements for building up
    an interactive interpreter. We reason the design and implementation
    decisions as solution to the challenge of implementing interpreter behaviour
    as an extension of the compiler library. We present the new features, eg
    how C++11 will come to Cling and how CINT-specific extensions are being
    adopted. We clarify the state of integration in the ROOT framework and the
    induced change set. We explain how ROOT ...
  cites: '19'
  cites_id: '17462954415562379042'
  eprint: https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071/pdf
  number: '5'
  pages: '052071'
  url: https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071/meta
  volume: '396'
  year: '2012'

- title: Clad—automatic differentiation using Clang and LLVM
  author: V Vassilev, M Vassilev, A Penev, L Moneta, V Ilieva
  abstract: |
   Differentiation is ubiquitous in high energy physics, for instance in
   minimization algorithms and statistical analysis, in detector alignment and
   calibration, and in theory. Automatic differentiation (AD) avoids well-known
   limitations in round-offs and speed, which symbolic and numerical
   differentiation suffer from, by transforming the source code of functions. We
   will present how AD can be used to compute the gradient of multi-variate
   functions and functor objects. We will explain approaches to implement an AD
   tool. We will show how LLVM, Clang and Cling (ROOT's C++ 11 interpreter)
   simplifies creation of such a tool. We describe how the tool could be
   integrated within any framework. We will demonstrate a simple proof-of-concept
   prototype, called Clad, which is able to generate n-th order derivatives of
   C++ functions and other language constructs. We also demonstrate how Clad can
   offload laborious computations ...
  cites: '5'
  cites_id: '3614309831712318570'
  eprint: https://iopscience.iop.org/article/10.1088/1742-6596/608/1/012055/pdf
  journal: 'Journal of Physics: Conference Series'
  number: '1'
  pages: '012055'
  publisher: IOP Publishing
  url: https://iopscience.iop.org/article/10.1088/1742-6596/608/1/012055/meta
  volume: '608'
  year: '2015'

- title: Optimizing ROOT’s Performance Using C++ Modules
  author: V Vassilev
  abstract: |
    ROOT comes with a C++ compliant interpreter cling. Cling needs to understand
    the content of the libraries in order to interact with them. Exposing the
    full shared library descriptors to the interpreter at runtime translates
    into increased memory footprint. ROOT’s exploratory programming concepts
    allow implicit and explicit runtime shared library loading. It requires the
    interpreter to load the library descriptor. Re-parsing of descriptors’
    content has a noticeable effect on the runtime performance. Present
    state-of-art lazy parsing technique brings the runtime performance to
    reasonable levels but proves to be fragile and can introduce correctness
    issues. An elegant solution is to load information from the descriptor lazily
    and in a non-recursive way.The LLVM community advances its C++ Modules
    technology providing an io-efficient, on-disk representation capable to
    reduce build times and peak memory usage. The feature ...
  cites: '4'
  cites_id: '860139365460275907'
  eprint: https://iopscience.iop.org/article/10.1088/1742-6596/898/7/072023/pdf
  journal: 'Journal of Physics: Conference Series'
  number: '10.1088'
  pages: 1742-6596
  url: https://iopscience.iop.org/article/10.1088/1742-6596/898/7/072023/pdf
  volume: '898'
  year: '2016'

- title: Optimizing Frameworks’ Performance Using C++ Modules Aware ROOT
  author: Y Takahashi, V Vassilev, O Shadura, R Isemann
  abstract: |
    ROOT is a data analysis framework broadly used in and outside of High Energy
    Physics (HEP). Since HEP software frameworks always strive for performance
    improvements, ROOT was extended with experimental support of runtime C++
    Modules. C++ Modules are designed to improve the performance of C++ code
    parsing. C++ Modules offers a promising way to improve ROOT’s runtime
    performance by saving the C++ header parsing time which happens during ROOT
    runtime. This paper presents the results and challenges of integrating C++
    Modules into ROOT.
  cites: '2'
  cites_id: '7331821473567233508'
  eprint: https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_02011.pdf
  journal: EPJ Web of Conferences
  pages: '02011'
  publisher: EDP Sciences
  url: https://www.epj-conferences.org/articles/epjconf/abs/2019/19/epjconf_chep2018_02011/epjconf_chep2018_02011.html
  volume: '214'
  year: '2019'

- title: GPU Accelerated Automatic Differentiation With Clad
  author: Ioana Ifrim, Vassil Vassilev, David J Lange 
  abstract: |
    Automatic Differentiation (AD) is instrumental for science and industry. 
    It is a tool to evaluate the derivative of a function specified through a 
    computer program. The range of AD application domain spans from Machine 
    Learning to Robotics to High Energy Physics. Computing gradients with 
    the help of AD is guaranteed to be more precise than the numerical 
    alternative and have a low, constant factor more arithmetical operations 
    compared to the original function. Moreover, AD applications to domain 
    problems typically are computationally bound. They are often limited by 
    the computational requirements of high-dimensional parameters and thus can
    benefit from parallel implementations on graphics processing units (GPUs). 
    Clad aims to enable differential analysis for C/C++ and CUDA and is a 
    compiler-assisted AD tool available both as a compiler extension and in ROOT. 
    Moreover, Clad works as a plugin extending the Clang compiler; as a plugin 
    extending the interactive interpreter Cling; and as a Jupyter kernel 
    extension based on xeus-cling. We demonstrate the advantages of parallel 
    gradient computations on GPUs with Clad. We explain how to bring forth a new 
    layer of optimization and a proportional speed up by extending Clad to 
    support CUDA. The gradients of well-behaved C++ functions can be 
    automatically executed on a GPU. The library can be easily integrated into 
    existing frameworks or used interactively. Furthermore, we demonstrate the 
    achieved application performance improvements, including (~10x) in ROOT 
    histogram fitting and corresponding performance gains from offloading to GPUs.
  cites: '0'
  eprint: https://arxiv.org/abs/2203.06139
  journal: arXiv preprint arXiv:2203.06139
  url: https://arxiv.org/abs/2203.06139
  year: '2022'

- title: Automatic Differentiation in ROOT
  author: Vassil Vassilev, Aleksandr Efremov, Oksana Shadura
  abstract: |
    In mathematics and computer algebra, automatic differentiation (AD) is
    a set of techniques to evaluate the derivative of a function specified by a
    computer program. AD exploits the fact that every computer program, no matter
    how complicated, executes a sequence of elementary arithmetic operations
    (addition, subtraction, multiplication, division, etc.), elementary functions
    (exp, log, sin, cos, etc.) and control flow statements. AD takes source code
    of a function as input and produces source code of the derived function. By
    applying the chain rule repeatedly to these operations, derivatives of
    arbitrary order can be computed automatically, accurately to working
    precision, and using at most a small constant factor more arithmetic
    operations than the original program.This paper presents AD techniques
    available in ROOT, supported by Cling, to produce derivatives of arbitrary
    C/C++ functions through implementing source code transformation and
    employing the chain rule of differential calculus in both forward mode and
    reverse mode. We explain its current integration for gradient computation in
    TFormula. We demonstrate the correctness and performance improvements in
    ROOT's fitting algorithms.
  cites: '1'
  cites_id: '5151320058278720217'
  eprint: https://arxiv.org/pdf/2004.04435
  journal: arXiv preprint arXiv:2004.04435
  url: https://arxiv.org/abs/2004.04435
  year: '2020'

- title: 'arXiv: Software Challenges For HL-LHC Data Analysis'
  author: Kim Albertsson Brann, Sitong An, Vassil Vassilev, Enric Tejedor
    Saavedra, Jakob Blomer, Lorenzo Moneta, Axel Naumann, Enrico Guiraud,
    Alja Mrak Tadel, Pere Mato Vila, Vincenzo Eduardo Padulano, Stephan
    Hageboeck, Matevz Tadel, Stefan Wunsch, Bertrand Bellenot, Oksana
    Shadura, Guilherme Amadio, Fons Rademakers, Massimiliano Galli, Xavier
    Valls Pla, Sergey Linev, Philippe Canal, Olivier Couet
  abstract: |
    The high energy physics community is discussing where investment is needed
    to prepare software for the HL-LHC and its unprecedented challenges. The ROOT
    project is one of the central software players in high energy physics since
    decades. From its experience and expectations, the ROOT team has distilled a
    comprehensive set of areas that should see research and development in the
    context of data analysis software, for making best use of HL-LHC's physics
    potential. This work shows what these areas could be, why the ROOT team
    believes investing in them is needed, which gains are expected, and where
    related work is ongoing. It can serve as an indication for future research
    proposals and cooperations.
  cites: '0'
  eprint: https://cds.cern.ch/record/2715455/files/2004.07675.pdf
  number: 'arXiv: 2004.07675'
  url: https://cds.cern.ch/record/2715455
  year: '2020'
  
- title: C++ Modules in ROOT and Beyond
  author: Vassil Vassilev, David Lange, Malik Shahzad Muzzafar, Mircho Rodozov,
    Oksana Shadura, Alexander Penev
  abstract: |
    C++ Modules come in C++ 20 to fix the long-standing build scalability
    problems in the language. They provide an io-efficient, on-disk
    representation capable to reduce build times and peak memory usage. ROOT
    employs the C++ modules technology further in the ROOT dictionary system to
    improve its performance and reduce the memory footprint.ROOT with C++ Modules
    was released as a technology preview in fall 2018, after intensive
    development during the last few years. The current state is ready for
    production, however, there is still room for performance optimizations. In
    this talk, we show the roadmap for making the technology default in ROOT.
    We demonstrate a global module indexing optimization which allows reducing
    the memory footprint dramatically for many workflows. We will report user
    feedback on the migration to ROOT with C++ Modules.
  cites: '0'
  eprint: https://arxiv.org/pdf/2004.06507
  journal: arXiv preprint arXiv:2004.06507
  url: https://arxiv.org/abs/2004.06507
  year: '2020'

- title: Migrating large codebases to C++ Modules
  author: Yuka Takahashi, Oksana Shadura, Vassil Vassilev
  abstract: |
    ROOT has several features which interact with libraries and require implicit
    header inclusion. This can be triggered by reading or writing data on disk,
    or user actions at the prompt. Often, the headers are immutable, and
    reparsing is redundant. C++ Modules are designed to minimize the reparsing
    of the same header content by providing an efficient on-disk representation
    of C++ Code. ROOT has released a C++ Modules-aware technology preview, which
    intends to become the default for the ROOT 6.20 release.
  cites: '0'
  eprint: https://iopscience.iop.org/article/10.1088/1742-6596/1525/1/012051/pdf
  journal: 'Journal of Physics: Conference Series'
  number: '1'
  pages: '012051'
  publisher: IOP Publishing
  url: https://iopscience.iop.org/article/10.1088/1742-6596/1525/1/012051/meta
  volume: '1525'
  year: '2020'

- title: IDD – A Platform Enabling Differential Debugging
  author: Martin Vassilev, Vassil Vassilev, Alexander Penev
  abstract: |
    Debugging is a very time consuming task which is not well supported by
    existing tools. The existing methods do not provide tools enabling optimal
    developers' productivity when debugging regressions in complex systems. In
    this paper we describe a possible solution aiding differential debugging.
    The differential debugging technique performs analysis of the regressed
    system and identifying the cause of the unexpected behavior by comparing to
    a previous version of the same system. The prototype, idd, inspects two
    versions of the executable – a baseline and a regressed version. The
    interactive debugging session runs side by side both executables and allows
    to examine and to compare various internal states. The architecture can work
    with multiple information sources comparing data from different tools. We
    also show how idd can detect performance regressions using information from
    third-party performance facilities. We illustrate how in practice we can
    quickly discover regressions in large systems such as the clang compiler.
  cites: '0'
  eprint: https://content.sciendo.com/downloadpdf/journals/cait/20/1/article-p53.xml
  journal: Cybernetics and Information Technologies
  number: '1'
  pages: 53-67
  publisher: Sciendo
  url: https://content.sciendo.com/view/journals/cait/20/1/article-p53.xml
  volume: '20'
  year: '2020'

- title: Relaxing the one definition rule in interpreted C++
  author: Javier López-Gómez, Javier Fernández, David del Rio Astorga, Vassil
    Vassilev, Axel Naumann, J Daniel García
  abstract: |
    Most implementations of the C++ programming language generate binary
    executable code. However, interpreted execution of C++ sources has its own
    use cases as the Cling interpreter from CERN's ROOT project has shown. Some
    limitations are derived from the ODR (One Definition Rule) that rules out
    multiple definitions of entities within a single translation unit (TU). ODR
    is there to ensure uniform view of a given C++ entity across translation
    units. Ensuring uniform view of C++ entities helps when producing ABI
    compatible binaries. Interpreting C++ presumes a single ever-growing
    translation unit that define away some of the ODR use-cases. Therefore, it
    may well be desirable to relax the ODR and, consequently, to support the
    ability of developers to override any existing definition for a given
    declaration. This approach is especially well-suited for iterative
    prototyping. In this paper, we extend Cling, a Clang/LLVM ...
  cites: '0'
  eprint: https://dl.acm.org/doi/pdf/10.1145/3377555.3377901
  pages: 212-222
  url: https://dl.acm.org/doi/abs/10.1145/3377555.3377901
  year: '2020'

- title: 'P2072R0: Differentiable programming for C++'
  author: Marco Foco, Max Rietmann, Vassil Vassilev, Michael Wong, Dmitry
    Duka, Vinod Grover
  abstract: |
    Derivatives are vital a wide variety of computing applications, including
    numerical optimization, solution of nonlinear equations, sensitivity
    analysis, and nonlinear inverse problems. Virtually every process could be
    described with a mathematical function. A mathematical function can be
    thought of as an association between elements from different sets.
    Derivatives can track how a varying quantity depends on another quantity,
    for example how the position of a planet as the time varies. Derivatives and
    gradients allows us to explore the properties of a function and thus the
    described process as a whole. Also, gradients are an essential component in
    gradient-based optimization methods, that have become more and more important
    in recent years, in particular with its application training of (Deep) Neural
    Networks. Derivatives can be computed numerically, but unfortunately the
    finite differences methods are problematic due to the approximation operated
    and the finite precision of floating point values used, and so the
    implementation of the method faces precision and round off problems which
    can affect the overall precision of the computation. This problem becomes
    worse with higher order derivatives.
  cites: '0'
  eprint: http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2072r0.pdf
  url: http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2072r0.pdf
  year: '2020'

- title: Continuous Performance Benchmarking Framework for ROOT
  author: Oksana Shadura, Vassil Vassilev, Brian Paul Bockelman
  abstract: |
    Foundational software libraries such as ROOT are under intense pressure to
    avoid software regression, including performance regressions. Continuous
    performance benchmarking, as a part of continuous integration and other code
    quality testing, is an industry best-practice to understand how the performance
    of a software product evolves. We present a framework, built from industry best
    practices and tools, to help to understand ROOT code performance and monitor
    the efficiency of the code for several processor architectures. It additionally
    allows historical performance measurements for ROOT I/O, vectorization and
    parallelization sub-systems.
  cites: '0'
  eprint: https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_05003.pdf
  journal: EPJ Web of Conferences
  pages: '05003'
  publisher: EDP Sciences
  url: https://www.epj-conferences.org/articles/epjconf/abs/2019/19/epjconf_chep2018_05003/epjconf_chep2018_05003.html
  volume: '214'
  year: '2019'

- title: Extending ROOT through Modules
  author: Oksana Shadura, Brian Paul Bockelman, Vassil Vassilev
  abstract: |
    The ROOT software framework is foundational for the HEP ecosystem, providing
    multiple capabilities such as I/O, a C++ interpreter, GUI, and math
    libraries. It uses object-oriented concepts and build-time components to
    layer between them. We believe that a new layering formalism will benefit
    the ROOT user community. We present the modularization strategy for ROOT
    which aims to build upon the existing source components, making available
    the dependencies and other metadata outside of the build system, and allow
    post-install additions on top of existing installation as well as in the ROOT
    runtime environment. Components can be grouped into packages and made
    available from repositories in order to provide a post-install step of
    missing packages. This feature implements a mechanism for the more
    comprehensive software ecosystem and makes it available even from a minimal
    ROOT installation. As part of ...
  cites: '0'
  eprint: https://www.epj-conferences.org/articles/epjconf/pdf/2019/19/epjconf_chep2018_05011.pdf
  journal: EPJ Web of Conferences
  pages: '05011'
  publisher: EDP Sciences
  url: https://www.epj-conferences.org/articles/epjconf/abs/2019/19/epjconf_chep2018_05011/epjconf_chep2018_05011.html
  volume: '214'
  year: '2019'

- title: Native Language Integrated Queries with CppLINQ in C++
  author: V Vassilev
  abstract: |
    Programming language evolution brought to us the domain-specific languages
    (DSL). They proved to be very useful for expressing specific concepts,
    turning into a vital ingredient even for general-purpose frameworks.
    Supporting declarative DSLs (such as SQL) in imperative languages (such as
    C++) can happen in the manner of language integrated query (LINQ).
  cites: '0'
  eprint: https://iopscience.iop.org/article/10.1088/1742-6596/608/1/012030/pdf
  journal: 'Journal of Physics: Conference Series'
  number: '1'
  pages: '012030'
  publisher: IOP Publishing
  url: https://iopscience.iop.org/article/10.1088/1742-6596/608/1/012030/meta
  volume: '608'
  year: '2015'

- title: clad - Automatic Differentiation with Clang
  author: Violeta Ilieva, Vassil Vassilev
  abstract: |
    Differentiation is the process of finding a derivative, which measures
    how a function changes as its input changes. Derivatives can be evaluated
    with machine precision accuracy through a method called automatic
    differentiation. Unlike other methods for differentiation, including
    numerical and symbolic, automatic differentiation yields exact derivatives
    even of complicated functions at relatively low processing and storage costs.
  cites: '0'
  eprint: http://www.llvm.org/devmtg/2013-11/slides/Vassilev-Poster.pdf
  url: http://www.llvm.org/devmtg/2013-11/slides/Vassilev-Poster.pdf
  year: '2013'

- title: Automatic Differentiation of Binned Likelihoods With Roofit and Clad
  author: Garima Singh, Jonas Rembser, Lorenzo Moneta, David Lange, Vassil Vassilev
  abstract: |
    RooFit is a toolkit for statistical modeling and fitting used by most experiments
    in particle physics. Just as data sets from next-generation experiments grow,
    processing requirements for physics analysis become more computationally demanding,
    necessitating performance optimizations for RooFit. One possibility to speed-up
    minimization and add stability is the use of Automatic Differentiation (AD). Unlike
    for numerical differentiation, the computation cost scales linearly with the number
    of parameters, making AD particularly appealing for statistical models with many
    parameters. In this paper, we report on one possible way to implement AD in RooFit.
    Our approach is to add a facility to generate C++ code for a full RooFit model
    automatically. Unlike the original RooFit model, this generated code is free of virtual
    function calls and other RooFit-specific overhead. In particular, this code is then
    used to produce the gradient automatically with Clad. Clad is a source transformation
    AD tool implemented as a plugin to the clang compiler, which automatically generates
    the derivative code for input C++ functions. We show results demonstrating the
    improvements observed when applying this code generation strategy to HistFactory and
    other commonly used RooFit models.
  cites: '0'
  eprint: https://arxiv.org/abs/2304.02650
  url: https://arxiv.org/abs/2304.02650
  year: '2023'

- title: Efficient and Accurate Automatic Python Bindings with cppyy & Cling
  author: Baidyanath Kundu, Vassil Vassilev, Wim Lavrijsen
  abstract: |
    The simplicity of Python and the power of C++ force stark choices on a scientific software
    stack. There have been multiple developments to mitigate language boundaries by implementing
    language bindings, but the impedance mismatch between the static nature of C++ and the
    dynamic one of Python hinders their implementation; examples include the use of user-defined
    Python types with templated C++ and advanced memory management.
    The development of the C++ interpreter Cling has changed the way we can think of language
    bindings as it provides an incremental compilation infrastructure available at runtime.
    That is, Python can interrogate C++ on demand, and bindings can be lazily constructed at
    runtime. This automatic binding provision requires no direct support from library authors
    and offers better performance than alternative solutions, such as PyBind11. ROOT pioneered
    this approach with PyROOT, which was later enhanced with its successor, cppyy. However,
    until now, cppyy relied on the reflection layer of ROOT, which is limited in terms of
    provided features and performance.
    This paper presents the next step for language interoperability with cppyy, enabling
    research into uniform cross-language execution environments and boosting optimization
    opportunities across language boundaries. We illustrate the use of advanced C++ in
    Numba-accelerated Python through cppyy. We outline a path forward for re-engineering
    parts of cppyy to use upstream LLVM components to improve performance and sustainability.
    We demonstrate cppyy purely based on a C++ reflection library, InterOp, which offers
    interoperability primitives based on Cling and Clang-Repl.
  cites: '0'
  eprint: https://arxiv.org/abs/2304.02712
  url: https://arxiv.org/abs/2304.02712
  year: '2023'

- title: Fast And Automatic Floating Point Error Analysis With CHEF-FP
  author: Garima Singh, Baidyanath Kundu, Harshitha Menon, Alexander Penev, 
    David J. Lange, Vassil Vassilev
  abstract: |
    As we reach the limit of Moore's Law, researchers are exploring different 
    paradigms to achieve unprecedented performance. Approximate Computing (AC), 
    which relies on the ability of applications to tolerate some error in the 
    results to trade-off accuracy for performance, has shown significant promise. 
    Despite the success of AC in domains such as Machine Learning, its 
    acceptance in High-Performance Computing (HPC) is limited due to its 
    stringent requirement of accuracy. We need tools and techniques to identify 
    regions of the code that are amenable to approximations and their impact on 
    the application output quality so as to guide developers to employ selective 
    approximation. To this end, we propose CHEF-FP, a flexible, scalable, and 
    easy-to-use source-code transformation tool based on Automatic 
    Differentiation (AD) for analysing approximation errors in HPC applications. 
    CHEF-FP uses Clad, an efficient AD tool built as a plugin to the Clang 
    compiler and based on the LLVM compiler infrastructure, as a backend 
    and utilizes its AD abilities to evaluate approximation errors in C++ 
    code. CHEF-FP works at the source level by injecting error estimation 
    code into the generated adjoints. This enables the error-estimation code 
    to undergo compiler optimizations resulting in improved analysis time and 
    reduced memory usage. We also provide theoretical and architectural 
    augmentations to source code transformation-based AD tools to perform FP 
    error analysis. In this paper, we primarily focus on analyzing errors 
    introduced by mixed-precision AC techniques, the most popular approximate 
    technique in HPC. We also show the applicability of our tool in estimating 
    other kinds of errors by evaluating our tool on codes that use approximate 
    functions. Moreover, we demonstrate the speedups achieved by CHEF-FP during 
    analysis time as compared to the existing state-of-the-art tool as a result 
    of its ability to generate and insert approximation error estimate code 
    directly into the derivative source. The generated code also becomes a 
    candidate for better compiler optimizations contributing to lesser 
    runtime performance overhead.
  cites: '0'
  eprint: https://arxiv.org/abs/2304.06441
  journal: '2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)'
  pages: 1018-1028
  publisher: IEEE
  url: https://ieeexplore.ieee.org/document/10177445
  link: /publications/fast-and-automatic-floating-point-error-analysis-with-chef-fp
  volume: '608'
  year: '2023'

- title: Performance Portable Gradient Computations Using Source Transformation
  author: Kim Liegeois, Brian Kelley, Eric Phipps, Sivasankaran Rajamanickam and
    Vassil Vassilev
  abstract: |
    Derivative computation is a key component of optimization, sensitivity
    analysis, uncertainty quantification, and nonlinear solvers.  Automatic
    differentiation (AD) is a powerful technique for evaluating such
    derivatives, and in recent years, has been integrated into programming
    environments such as Jax, PyTorch, and TensorFlow to support derivative
    computations needed for training of machine learning models, resulting in
    widespread use of these technologies.  The C++ language has become the de
    facto standard for scientific computing due to numerous factors, yet
    language complexity has made the adoption of AD technologies for C++
    difficult, hampering the incorporation of powerful differentiable
    programming approaches into C++ scientific simulations.  This is exacerbated
    by the increasing emergence of architectures such as GPUs, which have
    limited memory capabilities and require massive thread-level
    concurrency. Portable scientific codes rely on domain specific programming
    models such as Kokkos making AD for such codes even more complex.<br />
    In this paper, we will investigate source transformation-based automatic
    differentiation using Clad to automatically generate portable and efficient
    gradient computations of Kokkos-based code. We discuss the modifications of
    Clad required to differentiate Kokkos abstractions.  We will illustrate the
    feasibility of our proposed strategy by comparing the wall-clock time of the
    generated gradient code with the wall-clock time of the input function on
    different cutting edge GPU architectures such as NVIDIA H100, AMD MI250x,
    and Intel Ponte Vecchio GPU.  For these three architectures and for the
    considered example, evaluating up to 10,000 entries of the gradient only
    took up to 2.17 times the wall-clock time of evaluating the input function.
  cites: '0'
  eprint: 8th International Conference on Algorithmic Differentiation
  url: https://www.autodiff.org/ad24/
  year: '2024'

- title: Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic
    Shower Simulations
  author: Max Aehle, Mihaly Novak, Vassil Vassilev, Nicolas R. Gauger,
    Lukas Heinrich, Michael Kagan and David Lange
  abstract: |
    Among the well-known methods to approximate derivatives of expectancies
    computed by Monte-Carlo simulations, averages of pathwise derivatives are
    often the easiest one to apply. Computing them via algorithmic
    differentiation typically does not require major manual analysis and
    rewriting of the code, even for very complex programs like simulations of
    particle-detector interactions in high-energy physics. However, the pathwise
    derivative estimator can be biased if there are discontinuities in the
    program, which may diminish its value for applications.<br />
    This work integrates algorithmic differentiation into the electromagnetic
    shower simulation code HepEmShow based on G4HepEm, allowing us to study how
    well pathwise derivatives approximate derivatives of energy depositions in a
    sampling calorimeter with respect to parameters of the beam and geometry. We
    found that when multiple scattering is disabled in the simulation, means of
    pathwise derivatives converge quickly to their expected values, and these
    are close to the actual derivatives of the energy deposition. Additionally,
    we demonstrate the applicability of this novel gradient estimator for
    stochastic gradient-based optimization in a model example.
  cites: '0'
  eprint: https://arxiv.org/pdf/2405.07944
  url: https://arxiv.org/pdf/2405.07944
  year: '2024'