scholarly journals Evaluation of HPC Acceleration and Interconnect Technologies for High-Throughput Data Acquisition

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7759
Author(s):  
Alessandro Cilardo
Keyword(s):  
Data Acquisition ◽  
High Performance ◽  
High Energy Physics ◽  
Direct Memory Access ◽  
High Energy ◽  
Data Movement ◽  
Horizon 2020 ◽  
Direct Exposure ◽  
Efficient Data ◽  
Network Interfaces

Efficient data movement in multi-node systems is a crucial issue at the crossroads of scientific computing, big data, and high-performance computing, impacting demanding data acquisition applications from high-energy physics to astronomy, where dedicated accelerators such as FPGA devices play a key role coupled with high-performance interconnect technologies. Building on the outcome of the RECIPE Horizon 2020 research project, this work evaluates the use of high-bandwidth interconnect standards, namely InfiniBand EDR and HDR, along with remote direct memory access functions for direct exposure of FPGA accelerator memory across a multi-node system. The prototype we present aims at avoiding dedicated network interfaces built in the FPGA accelerator itself, leaving most of the resources for user acceleration and supporting state-of-the-art interconnect technologies. We present the detail of the proposed system and a quantitative evaluation in terms of end-to-end bandwidth as concretely measured with a real-world FPGA-based multi-node HPC workload.

A Generic Streaming Software Platform Design for High-energy Physics Data Acquisition Systems

2021 ◽  
pp. 1-1
Author(s):  
Tianxing Wang ◽  
Junfeng Yang ◽  
Hongchao Wang ◽  
Hongwei Yu ◽  
Zhengyang Sun ◽  
...  
Keyword(s):  
Data Acquisition ◽  
High Energy Physics ◽  
High Energy ◽  
Software Platform ◽  
Platform Design ◽  
Data Acquisition Systems ◽  
Energy Physics

Quantum-Dot Transistor Based Multi-Bit Multiplier Unit for In-Memory Computing

2020 ◽  
Vol 29 (01n04) ◽  
pp. 2040007
Author(s):  
Yang Zhao ◽  
Fengyu Qian ◽  
Faquir Jain ◽  
Lei Wang
Keyword(s):  
Quantum Dot ◽  
Data Processing ◽  
High Performance ◽  
Memory Cell ◽  
Process Variations ◽  
High Energy ◽  
Data Movement ◽  
Neuron Networks ◽  
Fast Processing ◽  
Dot Product

In-memory computing is an emerging technique to fulfill the fast growing demand for high-performance data processing. This technique provides fast processing and high throughput by accessing data stored in the memory array rather than dealing with complicated operation and data movement on hard drive. For data processing, the most important computation is dot product, which is also the core computation for applications such as deep learning neuron networks, machine learning, etc. As multiplication is the key function in dot product, it is critical to improve its performance and achieve faster memory processing. In this paper, we present a design with the ability to perform in-memory multi-bit multiplications. The proposed design is implemented by using quantum-dot transistors, which enable multi-bit computations in the memory cell. Experimental results demonstrate that the proposed design provides reliable in-memory multi-bit multiplications with high density and high energy efficiency. Statistical analysis is performed using Monte Carlo simulations to investigate the process variations and error effects.

NAND And NOR Logic-In-Memory Comprising Silicon Nanowire Feedback Field-Effect Transistors

2021 ◽  
Author(s):  
Yejin Yang ◽  
Juhee Jeon ◽  
Jaemin Son ◽  
Kyoungah Cho ◽  
Sangsig Kim
Keyword(s):  
Processing Speed ◽  
Field Effect ◽  
High Performance ◽  
Field Effect Transistors ◽  
Silicon Nanowire ◽  
Logic Gate ◽  
High Energy ◽  
Zero Bias ◽  
Computing Systems ◽  
Data Movement

Abstract The processing of large amounts of data requires a high energy efficiency and fast processing time for high-performance computing systems. However, conventional von Neumann computing systems have performance limitations because of bottlenecks in data movement between separated processing and memory hierarchy, which causes latency and high power consumption. To overcome this hindrance, logic-in-memory (LIM) has been proposed that performs both data processing and memory operations. Here, we present a NAND and NOR LIM composed of silicon nanowidre feedback field-effect transistors, whose configuration resembles that of CMOS logic gate circuits. The LIM can perform memory operations to retain its output logic under zero-bias conditions as well as logic operations with a high processing speed of nanoseconds. The newly proposed dynamic voltage-transfer characteristics verify the operating principle of the LIM. This study demonstrates that the NAND and NOR LIM has promising potential to resolve power and processing speed issues.

scholarly journals Dynamic Integration and Management of Opportunistic Resources for HEP

2019 ◽  
Vol 214 ◽  
pp. 08009 ◽  
Author(s):  
Matthias J. Schnepf ◽  
R. Florian von Cube ◽  
Max Fischer ◽  
Manuel Giffels ◽  
Christoph Heidecker ◽  
...  
Keyword(s):  
High Performance ◽  
High Energy Physics ◽  
Job Scheduling ◽  
High Energy ◽  
Software Environment ◽  
Resource Manager ◽  
Network Bandwidth ◽  
The Status ◽  
Single Entry ◽  
Institute Of Technology

Demand for computing resources in high energy physics (HEP) shows a highly dynamic behavior, while the provided resources by the Worldwide LHC Computing Grid (WLCG) remains static. It has become evident that opportunistic resources such as High Performance Computing (HPC) centers and commercial clouds are well suited to cover peak loads. However, the utilization of these resources gives rise to new levels of complexity, e.g. resources need to be managed highly dynamically and HEP applications require a very specific software environment usually not provided at opportunistic resources. Furthermore, aspects to consider are limitations in network bandwidth causing I/O-intensive workflows to run inefficiently. The key component to dynamically run HEP applications on opportunistic resources is the utilization of modern container and virtualization technologies. Based on these technologies, the Karlsruhe Institute of Technology (KIT) has developed ROCED, a resource manager to dynamically integrate and manage a variety of opportunistic resources. In combination with ROCED, HTCondor batch system acts as a powerful single entry point to all available computing resources, leading to a seamless and transparent integration of opportunistic resources into HEP computing. KIT is currently improving the resource management and job scheduling by focusing on I/O requirements of individual workflows, available network bandwidth as well as scalability. For these reasons, we are currently developing a new resource manager, called TARDIS. In this paper, we give an overview of the utilized technologies, the dynamic management, and integration of resources as well as the status of the I/O-based resource and job scheduling.

scholarly journals Beyond HEP: Photon and accelerator science computing infrastructure at DESY

2020 ◽  
Vol 245 ◽  
pp. 07036
Author(s):  
Christoph Beyer ◽  
Stefan Bujack ◽  
Stefan Dietrich ◽  
Thomas Finnern ◽  
Martin Flemming ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
High Energy Physics ◽  
High Energy ◽  
Resource Provisioning ◽  
Small Scale ◽  
Online Processing ◽  
Offline Processing ◽  
National Analysis ◽  
Energy Physics

DESY is one of the largest accelerator laboratories in Europe. It develops and operates state of the art accelerators for fundamental science in the areas of high energy physics, photon science and accelerator development. While for decades high energy physics (HEP) has been the most prominent user of the DESY compute, storage and network infrastructure, various scientific areas as science with photons and accelerator development have caught up and are now dominating the demands on the DESY infrastructure resources, with significant consequences for the IT resource provisioning. In this contribution, we will present an overview of the computational, storage and network resources covering the various physics communities on site. Ranging from high-throughput computing (HTC) batch-like offline processing in the Grid and the interactive user analyses resources in the National Analysis Factory (NAF) for the HEP community, to the computing needs of accelerator development or of photon sciences such as PETRA III or the European XFEL. Since DESY is involved in these experiments and their data taking, their requirements include fast low-latency online processing for data taking and calibration as well as offline processing, thus high-performance computing (HPC) workloads, that are run on the dedicated Maxwell HPC cluster. As all communities face significant challenges due to changing environments and increasing data rates in the following years, we will discuss how this will reflect in necessary changes to the computing and storage infrastructures. We will present DESY compute cloud and container orchestration plans as a basis for infrastructure and platform services. We will show examples of Jupyter notebooks for small scale interactive analysis, as well as its integration into large scale resources such as batch systems or Spark clusters. To overcome the fragmentation of the various resources for all scientific communities at DESY, we explore how to integrate them into a seamless user experience in an Interdisciplinary Data Analysis Facility.

scholarly journals High Performance Numerical Computing for High Energy Physics: A New Challenge for Big Data Science

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Numerical Methods ◽  
High Performance ◽  
Data Science ◽  
Experimental Validation ◽  
High Energy Physics ◽  
High Energy ◽  
Performance Computing ◽  
Energy Physics

Modern physics is based on both theoretical analysis and experimental validation. Complex scenarios like subatomic dimensions, high energy, and lower absolute temperature are frontiers for many theoretical models. Simulation with stable numerical methods represents an excellent instrument for high accuracy analysis, experimental validation, and visualization. High performance computing support offers possibility to make simulations at large scale, in parallel, but the volume of data generated by these experiments creates a new challenge for Big Data Science. This paper presents existing computational methods for high energy physics (HEP) analyzed from two perspectives: numerical methods and high performance computing. The computational methods presented are Monte Carlo methods and simulations of HEP processes, Markovian Monte Carlo, unfolding methods in particle physics, kernel estimation in HEP, and Random Matrix Theory used in analysis of particles spectrum. All of these methods produce data-intensive applications, which introduce new challenges and requirements for ICT systems architecture, programming paradigms, and storage capabilities.

Electronic structure, phonons and optical propertiesof baryte type scintillators TlXO4 (X=Cl, Br)

2021 ◽  
Author(s):  
Supratik Mukherjee ◽  
Aiswarya T ◽  
Subrata Mondal ◽  
Ganapathy Vaitheeswaran
Keyword(s):  
Band Structure ◽  
High Performance ◽  
High Energy Physics ◽  
Electronic Band Structure ◽  
Blue Shift ◽  
High Energy ◽  
Electronic Band ◽  
Nuclear Security ◽  
A Charge ◽  
Physics Experiments

Abstract This article thoroughly addresses the structural, mechanical, vibrational, electronic band structure and the optical properties of the unexplored thallous perchlorate and perbromate from ab-initio calculations. The zone centered vibrational phonon frequencies shows, there is a blue shift in the mid and high frequency range from Cl → Br due to change in mass and force constant with respect to oxygen atom. From the band structure it is clear that the top of the valence band is due to thallium s states, whereas the bottom of the conduction band is due to halogen s and oxygen p states, showing similar magnitude of dispersion and exhibits a charge transfer character. These characteristics and the band gap obtained are consistent with that of a favourable scintillators. Our findings deliver directions for the design of efficient TlXO4 based scintillators with high performance which are desirable for distinct applications such as medical imaging, high energy physics experiments, nuclear security.

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