deploy: 6433b88

feed.xml

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postdocs.html

@@ -112,7 +112,7 @@ <h1 id="current-us-cms-post-doctoral-researchers">Current U.S. CMS Post Doctoral
            <div class="card-text">
               <b><a href="/postdocs/kmohrman.html">Kelci Mohrman</a></b><br />
               <small>University of Florida</small><br /><br />
-              <small><b>Benchmarking current capabilities and exploring the acceleration of columnar processing via heterogeneous architectures (2024-2025). Deploying GPU algorithms through SONIC (2023-2024).</b></small><br /><br />
+              <small><b></b></small><br /><br />
            </div>
            <div class="card-text mt-auto"><i>
              Sep 2023 - Aug 2025<br />

postdocs/kmohrman.html

@@ -88,13 +88,13 @@
         </center>
 
         <br>
-        <h3>Project: Benchmarking current capabilities and exploring the acceleration of columnar processing via heterogeneous architectures (2024-2025). Deploying GPU algorithms through SONIC (2023-2024).</h3>
+        <h3>Project: </h3>
 
-        2024-2025: This project aims to benchmark the performance of the step of late-stage data analysis (in which nanoAOD formatted data is transformed into histograms) for realistic CMS analyses in order to understand current capabilities, scaling, and bottlenecks for columnar analysis workflows; acceleration of the columnar processing via GPU offloading will also be explored.  The results of these studies will help to illuminate the challenges and opportunities that lie ahead as CMS pushes towards rapid and efficient turnarounds of HL-LHC physics analyses.  An ongoing CMS multi-boson analysis will be used as the example application for the proposed explorations.  The analysis is fairly representative of a mature CMS analysis studying Run 2 and early Run 3 data, and is implemented in the coffea framework.  We will aim to benchmark the performance that is able to be achieved under various configurations in order to understand where the bottlenecks lie and how the analysis scales towards skimming and processing larger data volumes.  We will also aim to demonstrate the feasibility of running a portion of the analysis on GPUs and to enumerate the developments that would remain in order to run the analysis fully on GPUs. 2023-2024: The goal of the project is to demonstrate at a sufficiently large scale the reconstruction algorithm workflow within CMSSW to be processed, where the client jobs are running on one site, while the Line Segment Tracking (LST) algorithm will be executed on GPUs on computing nodes at another site connected through SONIC (Services for Optimized Network Inference on Co-processors) framework. LST is a tracking algorithm that takes advantage of double-layer design of the HL-LHC outer tracker in order to perform hit correlations in a parallel way with GPUs. SONIC is a framework that provides GPUs as a service to clients running at different sites.  Combining the LST algorithm with the SONIC framework is the goal of the project, in which we aim to to demonstrate the execution of the LST algorithm on GPUs at an external site (apart from the site where the client jobs are run) via the SONIC framework.
+        <br> <b>2024-2025: Benchmarking current capabilities and exploring the acceleration of columnar processing via heterogeneous architectures </b> <br>  This project aims to benchmark the performance of the step of late-stage data analysis (in which nanoAOD formatted data is transformed into histograms) for realistic CMS analyses in order to understand current capabilities, scaling, and bottlenecks for columnar analysis workflows; acceleration of the columnar processing via GPU offloading will also be explored.  The results of these studies will help to illuminate the challenges and opportunities that lie ahead as CMS pushes towards rapid and efficient turnarounds of HL-LHC physics analyses.  An ongoing CMS multi-boson analysis will be used as the example application for the proposed explorations.  The analysis is fairly representative of a mature CMS analysis studying Run 2 and early Run 3 data, and is implemented in the coffea framework.  We will aim to benchmark the performance that is able to be achieved under various configurations in order to understand where the bottlenecks lie and how the analysis scales towards skimming and processing larger data volumes.  We will also aim to demonstrate the feasibility of running a portion of the analysis on GPUs and to enumerate the developments that would remain in order to run the analysis fully on GPUs. <br>  <a href=http://uaf-10.t2.ucsd.edu/~kmohrman/for_uscms/uscms_r_and_d_proposal_2024_coffea/Kelci-Mohrman-2024.pdf>2024 Project proposal</a> <br> <br>  <b>2023-2024: Deploying GPU algorithms through SONIC </b> <br>  The goal of the project was to implement a version of the Line Segment Tracking (LST) algorithm with the SONIC framework in order to enable flexible and efficient GPU usage.  Because reconstruction tasks constitutes the largest fraction of CMS data processing, it is important to understand the resource requirements and to explore options for improving the efficiency of these steps.  To this end, CMS is exploring reconstruction algorithms that are designed to make use of GPU resources.  These include LST, which is a tracking algorithm that takes advantage of double-layer design of the HL-LHC outer tracker in order to perform hit correlations in a parallel way with GPUs. With more algorithms requiring GPU resources, it is important to understand the resource requirements and strategies for ensuring efficient deployment and usage.  The SONIC framework provides the ability to make use of GPUs "as a service", enabling GPUs to be factored out of CPU machines.  With this approach, the GPU-based servers may be remote from the CPU-based servers, potentially allowing for more flexibility in the usage of GPU resources. <br>  <a href=http://uaf-10.t2.ucsd.edu/~kmohrman/for_uscms/uscms_r_and_d_proposal_2023_soniclst/Kelci-Mohrman.pdf>2023 Project proposal</a> <br> 
 
         <br>
         <br>
-        More information: <a href = "/assets/pdfs/Kelci-Mohrman-2024.pdf">My project proposal</a><br>
+        More information: <a href = "">My project proposal</a><br>
 
         <br>
         <b>Mentors: </b> <br>