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Researcher profile

Frank Würthwein

Frank Würthwein contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Distributed, Parallel, and Cluster Computinghep-exNetworking and Internet Architecturephysics.ins-deteess.SYInformation RetrievalMachine LearningPerformancephysics.comp-phphysics.plasm-phSystems and Control

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Published work

10 published item(s)

preprint2026arXiv

Benchmarking the Open Science Data Federation services to develop XRootD best practices

Research has become dependent on processing power and storage, one crucial aspect being data sharing. The Open Science Data Federation (OSDF) project aims to create a scientific global data distribution network based on the Pelican Platform. OSDF relies on the XRootD and Pelican projects. Nevertheless, OSDF must understand the XRootD limits under various configuration options, including transfer rate limits, proper buffer configuration, and storage type effect. We have thus executed a set of benchmarks to create a set of recommendations to share with the XRootD and Pelican teams. This work describes the tests and results performed using National Research Platform (NRP) hosts. The tests cover various file sizes and parallel streams and use clients from various distances from the server host. We also used several standalone clients (wget, curl, pelican) and the native HTCondor file transfer mechanisms. Applying the methodology creates a possibility to track how XRootD and the Pelican layer perform in different scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Access Trends of In-network Cache for Scientific Data

Scientific collaborations are increasingly relying on large volumes of data for their work and many of them employ tiered systems to replicate the data to their worldwide user communities. Each user in the community often selects a different subset of data for their analysis tasks; however, members of a research group often are working on related research topics that require similar data objects. Thus, there is a significant amount of data sharing possible. In this work, we study the access traces of a federated storage cache known as the Southern California Petabyte Scale Cache. By studying the access patterns and potential for network traffic reduction by this caching system, we aim to explore the predictability of the cache uses and the potential for a more general in-network data caching. Our study shows that this distributed storage cache is able to reduce the network traffic volume by a factor of 2.35 during a part of the study period. We further show that machine learning models could predict cache utilization with an accuracy of 0.88. This demonstrates that such cache usage is predictable, which could be useful for managing complex networking resources such as in-network caching.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Auto-scaling HTCondor pools using Kubernetes compute resources

HTCondor has been very successful in managing globally distributed, pleasantly parallel scientific workloads, especially as part of the Open Science Grid. HTCondor system design makes it ideal for integrating compute resources provisioned from anywhere, but it has very limited native support for autonomously provisioning resources managed by other solutions. This work presents a solution that allows for autonomous, demand-driven provisioning of Kubernetes-managed resources. A high-level overview of the employed architectures is presented, paired with the description of the setups used in both on-prem and Cloud deployments in support of several Open Science Grid communities. The experience suggests that the described solution should be generally suitable for contributing Kubernetes-based resources to existing HTCondor pools.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Comparing single-node and multi-node performance of an important fusion HPC code benchmark

Fusion simulations have traditionally required the use of leadership scale High Performance Computing (HPC) resources in order to produce advances in physics. The impressive improvements in compute and memory capacity of many-GPU compute nodes are now allowing for some problems that once required a multi-node setup to be also solvable on a single node. When possible, the increased interconnect bandwidth can result in order of magnitude higher science throughput, especially for communication-heavy applications. In this paper we analyze the performance of the fusion simulation tool CGYRO, an Eulerian gyrokinetic turbulence solver designed and optimized for collisional, electromagnetic, multiscale simulation, which is widely used in the fusion research community. Due to the nature of the problem, the application has to work on a large multi-dimensional computational mesh as a whole, requiring frequent exchange of large amounts of data between the compute processes. In particular, we show that the average-scale nl03 benchmark CGYRO simulation can be run at an acceptable speed on a single Google Cloud instance with 16 A100 GPUs, outperforming 8 NERSC Perlmutter Phase1 nodes, 16 ORNL Summit nodes and 256 NERSC Cori nodes. Moving from a multi-node to a single-node GPU setup we get comparable simulation times using less than half the number of GPUs. Larger benchmark problems, however, still require a multi-node HPC setup due to GPU memory capacity needs, since at the time of writing no vendor offers nodes with a sufficient GPU memory setup. The upcoming external NVSWITCH does however promise to deliver an almost equivalent solution for up to 256 NVIDIA GPUs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Studying Scientific Data Lifecycle in On-demand Distributed Storage Caches

The XRootD system is used to transfer, store, and cache large datasets from high-energy physics (HEP). In this study we focus on its capability as distributed on-demand storage cache. Through exploring a large set of daily log files between 2020 and 2021, we seek to understand the data access patterns that might inform future cache design. Our study begins with a set of summary statistics regarding file read operations, file lifetimes, and file transfers. We observe that the number of read operations on each file remains nearly constant, while the average size of a read operation grows over time. Furthermore, files tend to have a consistent length of time during which they remain open and are in use. Based on this comprehensive study of the cache access statistics, we developed a cache simulator to explore the behavior of caches of different sizes. Within a certain size range, we find that increasing the XRootD cache size improves the cache hit rate, yielding faster overall file access. In particular, we find that increase the cache size from 40TB to 56TB could increase the hit rate from 0.62 to 0.89, which is a significant increase in cache effectiveness for modest cost.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The anachronism of whole-GPU accounting

NVIDIA has been making steady progress in increasing the compute performance of its GPUs, resulting in order of magnitude compute throughput improvements over the years. With several models of GPUs coexisting in many deployments, the traditional accounting method of treating all GPUs as being equal is not reflecting compute output anymore. Moreover, for applications that require significant CPU-based compute to complement the GPU-based compute, it is becoming harder and harder to make full use of the newer GPUs, requiring sharing of those GPUs between multiple applications in order to maximize the achievable science output. This further reduces the value of whole-GPU accounting, especially when the sharing is done at the infrastructure level. We thus argue that GPU accounting for throughput-oriented infrastructures should be expressed in GPU core hours, much like it is normally done for the CPUs. While GPU core compute throughput does change between GPU generations, the variability is similar to what we expect to see among CPU cores. To validate our position, we present an extensive set of run time measurements of two IceCube photon propagation workflows on 14 GPU models, using both on-prem and Cloud resources. The measurements also outline the influence of GPU sharing at both HTCondor and Kubernetes infrastructure level.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Parallelizing the Unpacking and Clustering of Detector Data for Reconstruction of Charged Particle Tracks on Multi-core CPUs and Many-core GPUs

We present results from parallelizing the unpacking and clustering steps of the raw data from the silicon strip modules for reconstruction of charged particle tracks. Throughput is further improved by concurrently processing multiple events using nested OpenMP parallelism on CPU or CUDA streams on GPU. The new implementation along with earlier work in developing a parallelized and vectorized implementation of the combinatoric Kalman filter algorithm has enabled efficient global reconstruction of the entire event on modern computer architectures. We demonstrate the performance of the new implementation on Intel Xeon and NVIDIA GPU architectures.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Creating a content delivery network for general science on the internet backbone using XCaches

A general problem faced by computing on the grid for opportunistic users is that delivering cycles is simpler than delivering data to those cycles. In this project we show how we integrated XRootD caches placed on the internet backbone to implement a content delivery network for general science workflows. We will show that for some workflows on different science domains like high energy physics, gravitational waves, and others the combination of data reuse from the workflows together with the use of caches increases CPU efficiency while decreasing network bandwidth use.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Reconstruction of Charged Particle Tracks in Realistic Detector Geometry Using a Vectorized and Parallelized Kalman Filter Algorithm

One of the most computationally challenging problems expected for the High-Luminosity Large Hadron Collider (HL-LHC) is finding and fitting particle tracks during event reconstruction. Algorithms used at the LHC today rely on Kalman filtering, which builds physical trajectories incrementally while incorporating material effects and error estimation. Recognizing the need for faster computational throughput, we have adapted Kalman-filter-based methods for highly parallel, many-core SIMD and SIMT architectures that are now prevalent in high-performance hardware. Previously we observed significant parallel speedups, with physics performance comparable to CMS standard tracking, on Intel Xeon, Intel Xeon Phi, and (to a limited extent) NVIDIA GPUs. While early tests were based on artificial events occurring inside an idealized barrel detector, we showed subsequently that our mkFit software builds tracks successfully from complex simulated events (including detector pileup) occurring inside a geometrically accurate representation of the CMS-2017 tracker. Here, we report on advances in both the computational and physics performance of mkFit, as well as progress toward integration with CMS production software. Recently we have improved the overall efficiency of the algorithm by preserving short track candidates at a relatively early stage rather than attempting to extend them over many layers. Moreover, mkFit formerly produced an excess of duplicate tracks; these are now explicitly removed in an additional processing step. We demonstrate that with these enhancements, mkFit becomes a suitable choice for the first iteration of CMS tracking, and eventually for later iterations as well. We plan to test this capability in the CMS High Level Trigger during Run 3 of the LHC, with an ultimate goal of using it in both the CMS HLT and offline reconstruction for the HL-LHC CMS tracker.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Architectures with the CMS Detector

In the High-Luminosity Large Hadron Collider (HL-LHC), one of the most challenging computational problems is expected to be finding and fitting charged-particle tracks during event reconstruction. The methods currently in use at the LHC are based on the Kalman filter. Such methods have shown to be robust and to provide good physics performance, both in the trigger and offline. In order to improve computational performance, we explored Kalman-filter-based methods for track finding and fitting, adapted for many-core SIMD and SIMT architectures. Our adapted Kalman-filter-based software has obtained significant parallel speedups using such processors, e.g., Intel Xeon Phi, Intel Xeon SP (Scalable Processors) and (to a limited degree) NVIDIA GPUs. Recently, an effort has started towards the integration of our software into the CMS software framework, in view of its exploitation for the Run III of the LHC. Prior reports have shown that our software allows in fact for some significant improvements over the existing framework in terms of computational performance with comparable physics performance, even when applied to realistic detector configurations and event complexity. Here, we demonstrate that in such conditions physics performance can be further improved with respect to our prior reports, while retaining the improvements in computational performance, by making use of the knowledge of the detector and its geometry.

0 citations0 reviewsNo code linkedOpen access