This work is the first major attempt since the 1970s to challenge the idea that the essential engine of medical (and scientific) change in seventeenth-century Britain emanated from puritanism. It ...seeks to reaffirm the crucial role of the period of the civil wars and their aftermath in providing the most congenial context for a re-evaluation of traditional attitudes to medicine. In the process, it rejects the idea that such initiatives were the special preserve of a small religious elite (puritans), claiming instead that enthusiasm for change can be found across the religious spectrum. At the same time, the work demonstrates that medical practitioners were increasingly drawn into contemporary religious and political debates in a way that led to a fundamental politicization of the ‘profession’. By the end of the seventeenth century, it was now commonplace to see doctors, apothecaries and surgeons fully engaged in everyday political and civic life. At the same time, religious and political orientation often became an important factor in the career development of medics, especially in towns and cities, where substantial benefits might accrue to those who found themselves in favour with the ruling elites, be they Whig or Tory. The body politic, a Renaissance commonplace, was now peopled by medical practitioners who often claimed a special authority when it came to diagnosing the ills of late seventeenth-century society.
In 1666 an Irish gentleman called Valentine Greatrakes achieved brief but widespread fame as a miracle healer. Dubbed the ‘Stroker’, he is widely believed to have touched and cured thousands of men, ...women and children suffering from a large range of acute diseases and chronic conditions. His actions attracted the attention of the King, Charles II, as well as other eminent figures at court and in the various institutions of government and learning, including the newly founded Royal Society. However, there was little consensus as to the nature and origin of his gift and, following a brief period of intense lobbying on his behalf, he retired to Ireland and relative obscurity. Greatrakes’ life and career as miracle healer offers tantalising new insights into the broader issues and conflicts affecting men and women in the years immediately after the Restoration. Through his mission to heal or ‘exorcise’ the sick and crippled, Greatrakes’ exemplary life and character provided a template for a wider process of healing in the body politic after 1660, one that hoped to bridge the deep-seated religious and political divisons of his day.
Software Training Outreach In HEP Malik, Sudhir; Cordero, Danelix; Elmer, Peter ...
EPJ Web of Conferences,
2024, Letnik:
295
Journal Article, Conference Proceeding
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The NSF-funded IRIS-HEP "Training, Education & Outreach" program and QuarkNet are partnering to enable and expand software training for the high school teachers with a goal to tap, grow and diversify ...the talent pipeline from K-12 students for future cyberinfrastructure. The Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) is a software institute that aims to develop the state-of-the-art software cyberinfrastructure for the High Luminosity Large Hadron Collider (HL-LHC) at CERN and other planned HEP experiments of the 2020’s. QuarkNet provides professional development to K-12 physics teachers in particle physics content and teaching methods. The two projects have recently built a collaborative relationship where a well-established community of QuarkNet K-12 teachers has access to a wide training on software tools via its Data and Coding Camps supported by IRISHEP. The paper highlights the synergistic efforts and future plans.
Train to Sustain Malik, Sudhir; Lieret, Kilian; Elmer, Peter ...
EPJ Web of Conferences,
2024, Letnik:
295
Journal Article, Conference Proceeding
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The HSF/IRIS-HEP Software Training group provides software training skills to new researchers in High Energy Physics (HEP) and related communities. These skills are essential to produce high-quality ...and sustainable software needed to do the research. Given the thousands of users in the community, sustainability, though challenging, is the centerpiece of its approach. The training modules are open source and collaborative. Different tools and platforms, like GitHub, enable technical continuity, collaboration and nurture the sense to develop software that is reproducible and reusable. This contribution describes these efforts and its broader impacts.
The Large Hadron Collider (LHC) will be upgraded to Highluminosity LHC, increasing the number of simultaneous proton-proton collisions (pileup, PU) by several-folds. The harsher PU conditions lead to ...exponentially increasing combinatorics in charged particle tracking, placing a large demand on the computing resources. The projection on required computing resources exceeds the computing budget with the current algorithms running on single-thread CPUs. Motivated by the rise of heterogeneous computing in high-performance computing centers, we present Line Segment Tracking (LST), a highly parallelizeable algorithm that can run efficiently on GPUs and is being integrated to the CMS experiment central software. The usage of Alpaka framework for the algorithm implementation allows better portability of the code to run on different types of commercial parallel processors allowing flexibility on which processors to purchase for the experiment in the future. To verify a similar computational performance with a native solution, the Alpaka implementation is compared with a CUDA one on a NVIDIA Tesla V100 GPU. The algorithm creates short track segments in parallel, and progressively form higher level objects by linking segments that are consistent with genuine physics track hypothesis. The computing and physics performance are on par with the latest, multi-CPU versions of existing CMS tracking algorithms.
mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both threadand data-level parallelism. In the past few years the project transitioned from the R&D ...phase to deployment in the Run-3 offline workflow of the CMS experiment. The CMS tracking performs a series of iterations, targeting reconstruction of tracks of increasing difficulty after removing hits associated to tracks found in previous iterations. mkFit has been adopted for several of the tracking iterations, which contribute to the majority of reconstructed tracks. When tested in the standard conditions for production jobs, speedups in track pattern recognition are on average of the order of 3.5x for the iterations where it is used (3-7x depending on the iteration). Multiple factors contribute to the observed speedups, including vectorization and a lightweight geometry description, as well as improved memory management and single precision. Efficient vectorization is achieved with both the icc and the gcc (default in CMSSW) compilers and relies on a dedicated library for small matrix operations, Matriplex, which has recently been released in a public repository. While the mkFit geometry description already featured levels of abstraction from the actual Phase-1 CMS tracker, several components of the implementations were still tied to that specific geometry. We have further generalized the geometry description and the configuration of the run-time parameters, in order to enable support for the Phase-2 upgraded tracker geometry for the HL-LHC and potentially other detector configurations. The implementation strategy and high-level code changes required for the HL-LHC geometry are presented. Speedups in track building from mkFit imply that track fitting becomes a comparably time consuming step of the tracking chain. Prospects for an mkFit implementation of the track fit are also discussed.
Awkward Arrays in Python, C++, and Numba Pivarski, Jim; Elmer, Peter; Lange, David
EPJ Web of Conferences,
2020, Letnik:
245
Journal Article, Conference Proceeding
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The Awkward Array library has been an important tool for physics analysis in Python since September 2018. However, some interface and implementation issues have been raised in Awkward Array’s first ...year that argue for a reimplementation in C++ and Numba. We describe those issues, the new architecture, and present some examples of how the new interface will look to users. Of particular importance is the separation of kernel functions from data structure management, which allows a C++ implementation and a Numba implementation to share kernel functions, and the algorithm that transforms recordoriented data into columnar Awkward Arrays.
The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions ...expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph-nodes represent hits, while edges represent possible track segments-and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.
File formats for generic data structures, such as ROOT, Avro, and Parquet, pose a problem for deserialization: it must be fast, but its code depends on the type of the data structure, not known at ...compile-time. Just-in-time compilation can satisfy both constraints, but we propose a more portable solution: specialized virtual machines. AwkwardForth is a Forth-driven virtual machine for deserializing data into Awkward Arrays. As a language, it is not intended for humans to write, but it loosens the coupling between Uproot and Awkward Array. AwkwardForth programs for deserializing record-oriented formats (ROOT and Avro) are about as fast as C++ ROOT and 10–80× faster than fastavro. Columnar formats (simple TTrees, RNTuple, and Parquet) only require specialization to interpret metadata and are therefore faster with precompiled code.
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still ...meeting data processing time constraints. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches the equation space to discover algebraic relations approximating a dataset. We use PySR (a software to uncover these expressions based on an evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimize the top metric by pinning the network size because the vast hyperparameter space prevents an extensive search for neural architecture. Conversely, SR selects a set of models on the Pareto front, which allows for optimizing the performance-resource trade-off directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our method on a physics benchmark: the multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider. We show that our approach can approximate a 3-layer neural network using an inference model that achieves up to a 13-fold decrease in execution time, down to 5 ns, while still preserving more than 90% approximation accuracy.
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