Abstract: Imaginary time evolution (ITE) is a well-established method for ground-state preparation, a fundamental problem in many fields such as materials science, chemistry, and optimization. Quantum ITE (QITE) approximates this evolution on quantum hardware but suffers from high circuit depth and numerous measurements. In this work we introduce adaptive-time compressed QITE (ACQ), a novel algorithm that reduces resource-cost by combining adaptive time steps with circuit compression. This approach leverages geometric insights by characterizing its relationship to geodesic trajectories with a measure that distinguishes trajectories in CPN. Recalling that ITE is a gradient flow on the complex projective plane CPN, such trajectory measures allow one to measure the deviation from geodesicity of said flow. For Hamiltonians with only two distinct eigenvalues (spectral cardiality), ITE and QITE exactly trace geodesics, this fact motivates an adaptive strategy for systems whose corresponding spectral cardinality is greater than 2, where QITE unitaries are reused until an energy increase signals departure from the ITE path. This is implemented via a line search for energy minimization. Circuit compression is achieved by approximating the sequence of QITE unitaries with a single element of a one-parameter group. Numerical simulations on the transverse field Ising model and the Heisenberg model demonstrate that ACQ achieves comparable fidelity to standard QITE while significantly reducing the number of QITE optimizations and maintaining fixed circuit depth during propagation. Gate-count estimates and an analysis of the fidelity scaling with truncation parameters are provided. A gate count and performance comparison with the state of the art method double bracket QITE is also performed.

Abstract: This paper presents the design of an optical link card developed in the frame of the R&D activities for the phase 2 upgrade of the TileCal experiment. This board, that is part of the evaluation of different technologies for the final choice in the next years, is designed as a mezzanine that can work independently or be plugged in the optical multiplexer board of the TileCal backend electronics. It includes two SNAP 12 optical connectors able to transmit and receive up to 75 Gb/s and one SFP optical connector for lower speeds and compatibility with existing hardware as the read out driver. All processing is done in a Stratix II GX field-programmable gate array (FPGA). Details are given on the hardware design, including signal and power integrity analysis, needed when working with these high data rates and on firmware development to obtain the best performance of the FPGA signal transceivers and for the use of the GBT protocol.

Abstract: Monte Carlo methods provide detailed and accurate results for radiation transport simulations. Unfortunately, the high computational cost of these methods limits its usage in real-time applications. Moreover, existing computer codes do not provide a methodology for adapting these kinds of simulations to specific problems without advanced knowledge of the corresponding code system, and this restricts their applicability. To help solve these current limitations, we present PenRed, a general-purpose, standalone, extensible and modular framework code based on PENELOPE for parallel Monte Carlo simulations of electron-photon transport through matter. It has been implemented in C++ programming language and takes advantage of modern object-oriented technologies. In addition, PenRed offers the capability to read and process DICOM images as well as to construct and simulate image-based voxelized geometries, so as to facilitate its usage in medical applications. Our framework has been successfully verified against the original PENELOPE Fortran code. Furthermore, the implemented parallelism has been tested showing a significant improvement in the simulation time without any loss in precision of results. Program summary Program title: PenRed: Parallel Engine for Radiation Energy Deposition. CPC Library link to program files: https://doi .org /10 .17632/rkw6tvtngy.1 Licensing provision: GNU Affero General Public License (AGPL). Programming language: C++ standard 2011. Nature of problem: Monte Carlo simulations usually require a huge amount of computation time to achieve low statistical uncertainties. In addition, many applications necessitate particular characteristics or the extraction of specific quantities from the simulation. However, most available Monte Carlo codes do not provide an efficient parallel and truly modular structure which allows users to easily customise their code to suit their needs without an in-depth knowledge of the code system. Solution method: PenRed is a fully parallel, modular and customizable framework for Monte Carlo simulations of the passage of radiation through matter. It is based on the PENELOPE [1] code system, from which inherits its unique physics models and tracking algorithms for charged particles. PenRed has been coded in C++ following an object-oriented programming paradigm restricted to the C++11 standard. Our engine implements parallelism via a double approach: on the one hand, by using standard C++ threads for shared memory, improving the access and usage of the memory, and, on the other hand, via the MPI standard for distributed memory infrastructures. Notice that both kinds of parallelism can be combined together in the same simulation. Moreover, both threads and MPI processes, can be balanced using the builtin load balance system (RUPER-LB [30]) to maximise the performance on heterogeneous infrastructures. In addition, PenRed provides a modular structure with methods designed to easily extend its functionality. Thus, users can create their own independent modules to adapt our engine to their needs without changing the original modules. Furthermore, user extensions will take advantage of the builtin parallelism without any extra effort or knowledge of parallel programming. Additional comments including restrictions and unusual features: PenRed has been compiled in linux systems withg++ of GCC versions 4.8.5, 7.3.1, 8.3.1 and 9; clang version 3.4.2 and intel C++ compiler (icc) version 19.0.5.281. Since it is a C++11-standard compliant code, PenRed should be able to compile with any compiler with C++11 support. In addition, if the code is compiled without MPI support, it does not require any non standard library. To enable MPI capabilities, the user needs to install whatever available MPI implementation, such as openMPI [24] or mpich [25], which can be found in the repositories of any linux distribution. Finally, to provide DICOM processing support, PenRed can be optionally compiled using the dicom toolkit (dcmtk) [32] library. Thus, PenRed has only two optional dependencies, an MPI implementation and the dcmtk library.

Abstract: The LHC Computing Grid (LCG) leads by CERN, has solved with the LCG File Catalog (LFC) the major problem of scaling the data management catalog. However, additional performance issues should be faced to deploy a painless catalog service. With this aim, we present a decentralized LFC server configuration, and its performance evaluation compared with the traditional LFC deployment. A performance analysis is shown, including not only the catalog server, but also analysing the client side overhead. We find that the LFC service has in the clients a relevant workload of the overall service. The experimental results show that the proposed LFC deployment for servers and clients, improves the performance of the service.

Abstract: In this paper we introduce the energy efficiency as a new metric for evaluating both hardware platforms based on Graphic Processor Units (GPU), and algorithm optimisations at High Energy Physics (HEP) experiments. We develop a method to compute the energy efficiency for the case of the first high level trigger (HLT1) of the LHCb experiment, relating the throughput with GPU specifications such as the number of cores, clock frequency, memory bandwidth and thermal design power. The model can be extended to other HEP experiments to make decisions and reach sustainable computing ecosystems.