Engineering topics

Gartung, Patrick

Publications and source records attributed to Gartung, Patrick.

4 records

DOE OSTI2024

Darshan for HEP applications

Modern HEP workflows must manage increasingly large and complex data collections. HPC facilities may be employed to help meet these workflows’ growing data processing needs. However, a better understanding of the I/O patterns and underlying bottlenecks of these workflows is necessary to meet the performance expectations of HPC systems.Darshan is a lightweight I/O characterization tool that captures concise views of HPC application I/O behavior. It intercepts application I/O calls at runtime, records file access statistics for each process, and generates log files detailing application I/O access patterns.Typical HEP workflows include event generation, detector simulation, event reconstruction, and subsequent analysis stages. A study of the I/O behavior of the ATLAS simulation and filtering stage, and the CMS simulation workflow using Darshan is presented, including insights into the I/O operations and data access size.

Wang, Rui↗

DOE OSTI2023

Generalizing mkFit and its Application to HL-LHC

mkFit is an implementation of the Kalman filter-based track reconstruction algorithm that exploits both thread- and 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.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

CMSSW Scaling Limits on Many-Core Machines

Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over the course of the past decade, the CMS data processing framework, CMSSW, has been transformed from a single-threaded framework into a highly concurrent one. The first multithreaded version was brought into production by the start of the LHC Run 2 in 2015. Since then, the framework's threading efficiency has gradually been improved by adding more levels of concurrency and reducing the amount of serial code paths. The latest addition was support for concurrent Runs. In this work we review the concurrency model of the CMSSW, and measure its scalability with real CMS applications, such as simulation and reconstruction, on mode rn many-core machines. We show metrics such as event processing throughput and application memory usage with and without the contribution of I/O, as I/O has been the major scaling limitation for the CMS applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Vectorization of CMSSW offline software

The CMS experiment has been utilizing vectorization, or SIMD, in parts of its data processing applications for over a decade. On x86 platforms the vectorization level is still SSE3. In the past attempts to use wider vector instruction sets such as AVX or AVX-512 have, in practice, not resulted in improvements in the overall event processing throughput, because the CPUs scale down their frequency when processing AVX instructions. In addition, a notable part of the global pool of CMS’ resources has been old systems either not supporting AVX, or where the CPU frequency downscaling impacts all cores of the CPU. CMS has nevertheless continued to vectorize more of its application code, and in this work we review profiling methods we have found effective to find out pieces of code that would benefit from vectorization, and techniques to transform those codes such that the GCC compiler is able to auto-vectorize those codes. The build system used for CMSSW, Scram, has also been enha nced to be able to build code for multiple CPU microarchitectures such that the shared libraries of desired microarchitecture level can be loaded based on the CPU of the system. This multi-microarchitecture setup is invisible to the workflow management system, which makes its deployment straightforward. We describe in detail how this multi-microarchitecture build is set up, and measure the impact of using wider vector units than SSE3 on the event processing throughput of CMS applications such as simulation and reconstruction on recent x86 CPUs.

Gartung, Patrick↗