210 research outputs found
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
HEP Community White Paper on Software trigger and event reconstruction
Realizing the physics programs of the planned and upgraded high-energy
physics (HEP) experiments over the next 10 years will require the HEP community
to address a number of challenges in the area of software and computing. For
this reason, the HEP software community has engaged in a planning process over
the past two years, with the objective of identifying and prioritizing the
research and development required to enable the next generation of HEP
detectors to fulfill their full physics potential. The aim is to produce a
Community White Paper which will describe the community strategy and a roadmap
for software and computing research and development in HEP for the 2020s. The
topics of event reconstruction and software triggers were considered by a joint
working group and are summarized together in this document.Comment: Editors Vladimir Vava Gligorov and David Lang
LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics
This work presents a novel reconfigurable architecture for Low Latency Graph
Neural Network (LL-GNN) designs for particle detectors, delivering
unprecedented low latency performance. Incorporating FPGA-based GNNs into
particle detectors presents a unique challenge since it requires
sub-microsecond latency to deploy the networks for online event selection with
a data rate of hundreds of terabytes per second in the Level-1 triggers at the
CERN Large Hadron Collider experiments. This paper proposes a novel
outer-product based matrix multiplication approach, which is enhanced by
exploiting the structured adjacency matrix and a column-major data layout.
Moreover, a fusion step is introduced to further reduce the end-to-end design
latency by eliminating unnecessary boundaries. Furthermore, a GNN-specific
algorithm-hardware co-design approach is presented which not only finds a
design with a much better latency but also finds a high accuracy design under
given latency constraints. To facilitate this, a customizable template for this
low latency GNN hardware architecture has been designed and open-sourced, which
enables the generation of low-latency FPGA designs with efficient resource
utilization using a high-level synthesis tool. Evaluation results show that our
FPGA implementation is up to 9.0 times faster and achieves up to 13.1 times
higher power efficiency than a GPU implementation. Compared to the previous
FPGA implementations, this work achieves 6.51 to 16.7 times lower latency.
Moreover, the latency of our FPGA design is sufficiently low to enable
deployment of GNNs in a sub-microsecond, real-time collider trigger system,
enabling it to benefit from improved accuracy. The proposed LL-GNN design
advances the next generation of trigger systems by enabling sophisticated
algorithms to process experimental data efficiently.Comment: This paper has been accepted by ACM Transactions on Embedded
Computing Systems (TECS
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe
High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)
Computing plays an essential role in all aspects of high energy physics. As
computational technology evolves rapidly in new directions, and data throughput
and volume continue to follow a steep trend-line, it is important for the HEP
community to develop an effective response to a series of expected challenges.
In order to help shape the desired response, the HEP Forum for Computational
Excellence (HEP-FCE) initiated a roadmap planning activity with two key
overlapping drivers -- 1) software effectiveness, and 2) infrastructure and
expertise advancement. The HEP-FCE formed three working groups, 1) Applications
Software, 2) Software Libraries and Tools, and 3) Systems (including systems
software), to provide an overview of the current status of HEP computing and to
present findings and opportunities for the desired HEP computational roadmap.
The final versions of the reports are combined in this document, and are
presented along with introductory material.Comment: 72 page
Pixel data real time processing as a next step for HL-LHC upgrades and beyond
The experiments at LHC are implementing novel and challenging detector
upgrades for the High Luminosity LHC, among which the tracking systems. This
paper reports on performance studies, illustrated by an electron trigger, using
a simplified pixel tracker. To achieve a real-time trigger (e.g. processing
HL-LHC collision events at 40 MHz), simple algorithms are developed for
reconstructing pixel-based tracks and track isolation, utilizing look-up tables
based on pixel detector information. Significant gains in electron trigger
performance are seen when pixel detector information is included. In
particular, a rate reduction up to a factor of 20 is obtained with a signal
selection efficiency of more than 95\% over the whole coverage of this
detector. Furthermore, it reconstructs p-p collision points in the beam axis
(z) direction, with a high precision of 20 m resolution in the very
central region (), and, up to 380 m in the forward region
(2.7 3.0). This study as well as the results can easily be adapted
to the muon case and to the different tracking systems at LHC and other
machines beyond the HL-LHC. The feasibility of such a real-time processing of
the pixel information is mainly constrained by the Level-1 trigger latency of
the experiment. How this might be overcome by the Front-End ASIC design, new
processors and embedded Artificial Intelligence algorithms is briefly tackled
as well.Comment: To be submitted to JHE
ROOT for the HL-LHC: data format
This document discusses the state, roadmap, and risks of the foundational
components of ROOT with respect to the experiments at the HL-LHC (Run 4 and
beyond). As foundational components, the document considers in particular the
ROOT input/output (I/O) subsystem. The current HEP I/O is based on the TFile
container file format and the TTree binary event data format. The work going
into the new RNTuple event data format aims at superseding TTree, to make
RNTuple the production ROOT event data I/O that meets the requirements of Run 4
and beyond
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