Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

A preprint version of the article is available at: arXiv:2402.15366v2 [physics.ins-det], https://arxiv.org/abs/2402.15366 . Comments: Replaced with the published version. Added the journal reference and the DOI. All the figures and tables can be found at https://cms-results.web.cern.ch/cms-results/public-results/publications/MLG-23-001 (CMS Public Pages). Report numbers: CMS-MLG-23-001, CERN-EP-2023-303.Data Availability: No datasets were generated or analyzed during the current study.Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.SCOAP3. Open access funding provided by CERN (European Organization for Nuclear Research

Magnetic resonance diffusion tensor imaging for the pedunculopontine nucleus: proof of concept and histological correlation

The pedunculopontine nucleus (PPN) has been proposed as target for deep brain stimulation (DBS) in patients with postural instability and gait disorders due to its involvement in muscle tonus adjustments and control of locomotion. However, it is a deep-seated brainstem nucleus without clear imaging or electrophysiological markers. Some studies suggested that diffusion tensor imaging (DTI) may help guiding electrode placement in the PPN by showing the surrounding fiber bundles, but none have provided a direct histological correlation. We investigated DTI fractional anisotropy (FA) maps from in vivo and in situ post-mortem magnetic resonance images (MRI) compared to histological evaluations for improving PPN targeting in humans. A post-mortem brain was scanned in a clinical 3T MR system in situ. Thereafter, the brain was processed with a special method ideally suited for cytoarchitectonic analyses. Also, nine volunteers had in vivo brain scanning using the same MRI protocol. Images from volunteers were compared to those obtained in the post-mortem study. FA values of the volunteers were obtained from PPN, inferior colliculus, cerebellar crossing fibers and medial lemniscus using histological data and atlas information. FA values in the PPN were significantly lower than in the surrounding white matter region and higher than in areas with predominantly gray matter. In Nissl-stained histologic sections, the PPN extended for more than 10 mm in the rostro-caudal axis being closely attached to the lateral parabrachial nucleus. Our DTI analyses and the spatial correlation with histological findings proposed a location for PPN that matched the position assigned to this nucleus in the literature. Coregistration of neuroimaging and cytoarchitectonic features can add value to help establishing functional architectonics of the PPN and facilitate neurosurgical targeting of this extended nucleus