70,711 research outputs found
Massively Parallel Computing and the Search for Jets and Black Holes at the LHC
Massively parallel computing at the LHC could be the next leap necessary to
reach an era of new discoveries at the LHC after the Higgs discovery.
Scientific computing is a critical component of the LHC experiment, including
operation, trigger, LHC computing GRID, simulation, and analysis. One way to
improve the physics reach of the LHC is to take advantage of the flexibility of
the trigger system by integrating coprocessors based on Graphics Processing
Units (GPUs) or the Many Integrated Core (MIC) architecture into its server
farm. This cutting edge technology provides not only the means to accelerate
existing algorithms, but also the opportunity to develop new algorithms that
select events in the trigger that previously would have evaded detection. In
this article we describe new algorithms that would allow to select in the
trigger new topological signatures that include non-prompt jet and black
hole--like objects in the silicon tracker.Comment: 15 pages, 11 figures, submitted to NIM
Active data structures on GPGPUs
Active data structures support operations that may affect a large number of elements of an aggregate data structure. They are well suited for extremely fine grain parallel systems, including circuit parallelism. General purpose GPUs were designed to support regular graphics algorithms, but their intermediate level of granularity makes them potentially viable also for active data structures. We consider the characteristics of active data structures and discuss the feasibility of implementing them on GPGPUs. We describe the GPU implementations of two such data structures (ESF arrays and index intervals), assess their performance, and discuss the potential of active data structures as an unconventional programming model that can exploit the capabilities of emerging fine grain architectures such as GPUs
Multi-Architecture Monte-Carlo (MC) Simulation of Soft Coarse-Grained Polymeric Materials: SOft coarse grained Monte-carlo Acceleration (SOMA)
Multi-component polymer systems are important for the development of new
materials because of their ability to phase-separate or self-assemble into
nano-structures. The Single-Chain-in-Mean-Field (SCMF) algorithm in conjunction
with a soft, coarse-grained polymer model is an established technique to
investigate these soft-matter systems. Here we present an im- plementation of
this method: SOft coarse grained Monte-carlo Accelera- tion (SOMA). It is
suitable to simulate large system sizes with up to billions of particles, yet
versatile enough to study properties of different kinds of molecular
architectures and interactions. We achieve efficiency of the simulations
commissioning accelerators like GPUs on both workstations as well as
supercomputers. The implementa- tion remains flexible and maintainable because
of the implementation of the scientific programming language enhanced by
OpenACC pragmas for the accelerators. We present implementation details and
features of the program package, investigate the scalability of our
implementation SOMA, and discuss two applications, which cover system sizes
that are difficult to reach with other, common particle-based simulation
methods
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
Practical Implementation of Lattice QCD Simulation on Intel Xeon Phi Knights Landing
We investigate implementation of lattice Quantum Chromodynamics (QCD) code on
the Intel Xeon Phi Knights Landing (KNL). The most time consuming part of the
numerical simulations of lattice QCD is a solver of linear equation for a large
sparse matrix that represents the strong interaction among quarks. To establish
widely applicable prescriptions, we examine rather general methods for the SIMD
architecture of KNL, such as using intrinsics and manual prefetching, to the
matrix multiplication and iterative solver algorithms. Based on the performance
measured on the Oakforest-PACS system, we discuss the performance tuning on KNL
as well as the code design for facilitating such tuning on SIMD architecture
and massively parallel machines.Comment: 8 pages, 12 figures. Talk given at LHAM'17 "5th International
Workshop on Legacy HPC Application Migration" in CANDAR'17 "The Fifth
International Symposium on Computing and Networking" and to appear in the
proceeding
Arithmetic on a Distributed-Memory Quantum Multicomputer
We evaluate the performance of quantum arithmetic algorithms run on a
distributed quantum computer (a quantum multicomputer). We vary the node
capacity and I/O capabilities, and the network topology. The tradeoff of
choosing between gates executed remotely, through ``teleported gates'' on
entangled pairs of qubits (telegate), versus exchanging the relevant qubits via
quantum teleportation, then executing the algorithm using local gates
(teledata), is examined. We show that the teledata approach performs better,
and that carry-ripple adders perform well when the teleportation block is
decomposed so that the key quantum operations can be parallelized. A node size
of only a few logical qubits performs adequately provided that the nodes have
two transceiver qubits. A linear network topology performs acceptably for a
broad range of system sizes and performance parameters. We therefore recommend
pursuing small, high-I/O bandwidth nodes and a simple network. Such a machine
will run Shor's algorithm for factoring large numbers efficiently.Comment: 24 pages, 10 figures, ACM transactions format. Extended version of
Int. Symp. on Comp. Architecture (ISCA) paper; v2, correct one circuit error,
numerous small changes for clarity, add reference
Triggering BTeV
BTeV is a collider experiment at Fermilab designed for precision studies of
CP violation and mixing. Unlike most collider experiments, the BTeV detector
has a forward geometry that is optimized for the measurement of B and charm
decays in a high-rate environment. While the rate of B production gives BTeV an
advantage of almost four orders of magnitude over e+e- B factories, the BTeV
Level 1 trigger must be able to accept data at a rate of 100 Gigabytes per
second, reconstruct tracks and vertices, trigger on B events with high
efficiency, and reject minimum bias events by a factor of 100:1. An overview of
the Level 1 trigger will be presented.Comment: 6 pages, 3 figures. Contribution to the Proceedings, APS-Division of
Particles and Fields Conference, DPF99, UCLA, Los Angeles, CA, Jan. 5-9, 199
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