Towards a distributed heterogeneous task scheduler for the ATLAS offline software framework*

With the increased data volumes expected to be delivered by the HLLHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud computing platforms. To run its data processing applications on these resources, the ATLAS software framework must be capable of efficiently executing data processing tasks in heterogeneous distributed computing environments. Today, using the Gaudi Avalanche Scheduler, whose implementation is based on Intel TBB, we can efficiently schedule Athena algorithms to multiple threads within a single compute node. We aim to develop a new framework scheduler capable of supporting distributed heterogeneous environments, based on technologies like HPX or Ray. After the initial evaluation phase of these technologies, we began the development of a prototype distributed task scheduler for the Athena framework. This contribution describes this prototype scheduler and the preliminary results of performance studies within ATLAS data processing applications

Higgs self-coupling measurements using deep learning in the bb¯¯bb¯¯ final state

Measuring the Higgs trilinear self-coupling λhhh is experimentally demanding but fundamental for understanding the shape of the Higgs potential. We present a comprehensive analysis strategy for the HL-LHC using di-Higgs events in the four b-quark channel (hh → 4b), extending current methods in several directions. We perform deep learning to suppress the formidable multijet background with dedicated optimisation for BSM λhhh scenarios. We compare the λhhh constraining power of events using different multiplicities of large radius jets with a two-prong structure that reconstruct boosted h → bb decays. We show that current uncertainties in the SM top Yukawa coupling yt can modify λhhh constraints by ∼ 20%. For SM yt, we find prospects of −0.8 < λhhh/λSMhhh < 6.6 at 68% CL under simplified assumptions for 3000 fb−1 of HL-LHC data. Our results provide a careful assessment of di-Higgs identification and machine learning techniques for all-hadronic measurements of the Higgs self-coupling and sharpens the requirements for future improvement

Searching for Beyond the Standard Model Resonances in the HH→bbˉbbˉHH \to b\bar{b}b\bar{b} Final State Using the ATLAS Detector

An ATLAS analysis searching for Beyond the Standard Model resonances, $X$, that are produced and decay through the $pp\to X \to HH \to b\bar{b}b\bar{b}$ process is presented. These events are studied in the phase space region where the four $b$-quarks produce four well-separated jets. A significantly stronger upper-limit is set on the $pp \to X \to HH$ cross-section for these resonances than was set by the Early Run 2 ATLAS analysis. Above 350 GeV these limits are also stronger than those of the combination of all ATLAS di-Higgs searches with 27.5 fb$^{-1}$ to 36.1 fb$^{-1}$ of data. No statistically significant excesses are found, with the largest excess having a global significance of $1.2^{+0.4}_{-0.3}\sigma$. In particular, with an improved pairing algorithm and more sophisticated background modelling, the 2.8$\sigma$ excess found at 280 GeV by the Early Run 2 analysis is not confirmed. The results of this analysis suggest the upcoming version, which uses an evolution of the same technique on the same data to look for Standard Model di-Higgs production, will be able to set stringent limits on that process

Towards a distributed heterogeneous task scheduler for the ATLAS offline software framework

With the increased data volumes expected to be delivered by the HL-LHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud computing platforms. To be able to run its data processing applications on these resources, the ATLAS software framework must be capable of efficiently executing data processing tasks in heterogeneous distributed computing environments. Today with the use of the Gaudi Avalanche Scheduler, whose implementation is based on Intel TBB, we can efficiently schedule Athena algorithms to multiple threads within a single compute node. Our goal is to develop a new framework scheduler capable of supporting distributed heterogeneous environments, based on technologies like HPX or Ray. After the initial evaluation phase of these technologies, we began the development of a prototype distributed task scheduler for the Athena framework. This contribution will describe this prototype scheduler and the preliminary results of performance studies within ATLAS data processing applications

Evaluating HPX as a Next-Gen Scheduler for ATLAS on HPCs

Description Experiments at the CERN High-Luminosity Large Hadron Collider (HL-LHC) will produce hundreds of Petabytes of data per year. Efficient processing of this dataset represents a significant human resource and technical challenge. Today, ATLAS data processing applications run in multi-threaded mode, using Intel TBB for thread management, which allows efficient utilization of all available CPU cores on the computing resources. However, modern HPC systems and high-end computing clusters are increasingly based on heterogeneous architectures, usually a combination of CPU and accelerators (e.g., GPU, FPGA). To run ATLAS software on these machines efficiently, we started developing a distributed, fine-grained, vertically integrated task scheduling software system. A first simplified implementation of such a system called Raythena was developed in late 2019. It is based on Ray - a high-performance distributed execution platform developed by Riselab at UC Berkeley. Raythena leverages the ATLAS event-service architecture for efficient utilization of CPU resources on HPC systems by dynamically assigning fine-grained workloads (individual events or event ranges) to ATLAS data-processing applications running simultaneously on multiple HPC compute nodes. The main purpose of the Raythena project was to gain the experience of developing real-life applications with the Ray platform. However, in order to achieve our main objective, we need to design a new system capable of utilizing heterogeneous computing resources in a distributed environment. To accomplish this, we have started to evaluate HPX as an alternative to TBB/Ray. HPX is a C++ library for concurrency and parallelism developed by the Stellar group, which exposes a uniform, standards-oriented API for programming parallel, distributed, and heterogeneous applications. This presentation will describe the preliminary results of the evaluation of HPX for implementation of the task scheduler for ATLAS data-processing applications aimed to enable cross-node scheduling in heterogeneous systems that offer a mixture of CPU and GPU architectures. We present the prototype applications implemented using HPX and the preliminary results of performance studies of these applications. Significance This presentation describes design ideas and first simple prototype implementations of the distributed and heterogeneous task scheduling system for the ATLAS experiment. Given the increased data volumes expected to be recorded in the era of HL LHC, it becomes critical for the experiments to efficiently utilize all available computing resources, including the new generation of supercomputers, most of which will be based on heterogeneous architectures

Towards a distributed heterogeneous task scheduler for the ATLAS offline software framework

With the increased data volumes expected to be delivered by the HL-LHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud computing platforms. To be able to run its data processing applications on these resources, the ATLAS software framework must be capable of efficiently executing data processing tasks in heterogeneous distributed computing environments. Today with the use of Gaudi Avalanche Scheduler, a central component of the multithreaded Athena framework whose implementation is based on Intel TBB, we can efficiently schedule Athena algorithms to multiple threads within a single compute node. Our goal is to develop a new framework scheduler capable of supporting distributed heterogeneous environments, based on technologies like HPX and Ray. After the initial evaluation phase of these technologies, we began the actual development of prototype distributed task schedulers and their integration with the Athena framework. This contribution will describe these prototype schedulers , as well as the preliminary results of performance studies of these prototypes within ATLAS data processing applications

scikit-hep/uproot4: 4.0.11

&lt;p&gt;Fixed #383.&lt;/p&gt

scikit-hep/uproot4: 4.1.1

@veprbl added support for reading ROOT TTables: PR #418. @jpivarski fixed the warning handling for old XRootD clients: PR #425. @jpivarski fixed several performance bugs and one bug-bug in writing histograms and TTrees: PRs #426 and #428. See the GitHub conversations for measurements. A quantitative rule of thumb derived from that work is that you want to call TTree.extend with no less than ~100 kB per array branch. This is roughly the same scale that would be preferred for reading TTree data (you want TBaskets to be about 100 kB or larger), but in writing, you get to control it

scikit-hep/uproot4: 4.1.4

&lt;p&gt;@jpivarski added TStreamerInfo for TTrees in files written by Uproot. &lt;strong&gt;If you're using an older version, you're producing files without TStreamerInfo!&lt;/strong&gt; (Which means they won't be readable in some older versions of ROOT, and possibly not new ones, either.) PR #472.&lt;/p&gt; &lt;p&gt;Also corrected the TLeaf fTitle for jagged arrays, which is needed to read jagged arrays back in ROOT's &lt;code&gt;TTree::Draw&lt;/code&gt; and &lt;code&gt;TTree::Scan&lt;/code&gt;, but not for iteration in PyROOT (which is what we had been using for testing). PR #458.&lt;/p&gt; &lt;p&gt;Added an implementation of non-split TClonesArray, which makes more files readable: PR #467, and added RNTuple to the &lt;code&gt;must_be_attached&lt;/code&gt; list, which enables RNTuple objects to read more data (like TTrees): PR #463. That's just a step in developing RNTuple-reading capabilities.&lt;/p&gt

scikit-hep/uproot4: 4.1.6

&lt;p&gt;@duncanmmacleod included the test suite in the source distribution: PR #477.&lt;/p&gt; &lt;p&gt;@btovar fixed exception handling in XRootD callback threads by passing them on to the main thread: PR #480.&lt;/p&gt; &lt;p&gt;@jpivarski restricted Uproot 4.x to Awkward 1.x: PR #478. Also fixed some bugs when writing TTrees to preexisting files ("update" mode): PR #488.&lt;/p&gt