Non-perturbatively gauge-fixed compact U(1)U(1) lattice gauge theory

An extensive study of the compact $U(1)$ lattice gauge theory with a higher derivative gauge-fixing term and a suitable counter-term has been undertaken to determine the nature of the possible continuum limits for a wide range of the parameters, especially at strong gauge couplings ($g>1$), adding to our previous study at a single gauge coupling $g=1.3$ \cite{DeSarkar2016}. Our major conclusion is that a continuum limit of free massless photons (with the redundant pure gauge degrees of freedom decoupled) is achieved at any gauge coupling, not necessarily small, provided the coefficient $\tilde{\kappa}$ of the gauge-fixing term is sufficiently large. In fact, the region of continuous phase transition leading to the above physics in the strong gauge coupling region is found to be analytically connected to the point $g=0$ and $\tilde{\kappa} \rightarrow \infty$ where the classical action has a global unique minimum, around which weak coupling perturbation theory in bare parameters is defined, controlling the physics of the whole region. A second major conclusion is that, local algorithms like Multihit Metropolis fail to produce faithful field configurations with large values of the coefficient $\tilde{\kappa}$ of the higher derivative gauge-fixing term and at large lattice volumes. A global algorithm like Hybrid Monte Carlo, although at times slow to move out of metastabilities, generally is able to produce faithful configurations and has been used extensively in the current study.Comment: 25 pages, 18 figures, version accepted for publication in JHE

Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents

We present a novel method for guiding a large-scale swarm of autonomous agents into a desired formation shape in a distributed and scalable manner. Our Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) algorithm adopts an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled. Each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain. These time-varying Markov matrices are constructed by each agent in real-time using the feedback from the current swarm distribution, which is estimated in a distributed manner. The PSG-IMC algorithm minimizes the expected cost of the transitions per time instant, required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. We demonstrate the effectiveness of this proposed swarm guidance algorithm by using results of numerical simulations and hardware experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic

Evaluating Cache Coherent Shared Virtual Memory for Heterogeneous Multicore Chips

The trend in industry is towards heterogeneous multicore processors (HMCs), including chips with CPUs and massively-threaded throughput-oriented processors (MTTOPs) such as GPUs. Although current homogeneous chips tightly couple the cores with cache-coherent shared virtual memory (CCSVM), this is not the communication paradigm used by any current HMC. In this paper, we present a CCSVM design for a CPU/MTTOP chip, as well as an extension of the pthreads programming model, called xthreads, for programming this HMC. Our goal is to evaluate the potential performance benefits of tightly coupling heterogeneous cores with CCSVM

HIPE: HMC Instruction Predication Extension Applied on Database Processing

The recent Hybrid Memory Cube (HMC) is a smart memory which includes functional units inside one logic layer of the 3D stacked memory design. In order to execute instructions inside the Hybrid Memory Cube (HMC), the processor needs to send instructions to be executed near data, keeping most of the pipeline complexity inside the processor. Thus, control-flow and data-flow dependencies are all managed inside the processor, in such way that only update instructions are supported by the HMC. In order to solve data-flow dependencies inside the memory, previous work proposed HMC Instruction Vector Extensions (HIVE), which embeds a high number of functional units with a interlock register bank. In this work we propose HMC Instruction Prediction Extensions (HIPE), that supports predicated execution inside the memory, in order to transform control-flow dependencies into data-flow dependencies. Our mechanism focus on removing the high latency iteration between the processor and the smart memory during the execution of branches that depends on data processed inside the memory. In this paper we evaluate a balanced design of HIVE comparing to x86 and HMC executions. After we show the HIPE mechanism results when executing a database workload, which is a strong candidate to use smart memories. We show interesting trade-offs of performance when comparing our mechanism to previous work

HPC Accelerators with 3D Memory

Artículo invitado, publicado en las actas del congreso por IEEE Society Press. Páginas 320 a 328. ISBN: 978-1-5090-3593-9.DOI 10.1109/CSE-EUC-DCABES-2016.203After a decade evolving in the High Performance Computing arena, GPU-equipped supercomputers have con- quered the top500 and green500 lists, providing us unprecedented levels of computational power and memory bandwidth. This year, major vendors have introduced new accelerators based on 3D memory, like Xeon Phi Knights Landing by Intel and Pascal architecture by Nvidia. This paper reviews hardware features of those new HPC accelerators and unveils potential performance for scientific applications, with an emphasis on Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM) used by commercial products according to roadmaps already announced.Universidad de Málaga. Campus de Excelencia Internacional Andalucia Tec

Non-hermitian Exact Local Bosonic Algorithm for Dynamical Quarks

We present an exact version of the local bosonic algorithm for the simulation of dynamical quarks in lattice QCD. This version is based on a non-hermitian polynomial approximation of the inverse of the quark matrix. A Metropolis test corrects the systematic errors. Two variants of this test are presented. For both of them, a formal proof is given that this Monte Carlo algorithm converges to the right distribution. Simulation data are presented for different lattice parameters. The dynamics of the algorithm and its scaling in lattice volume and quark mass are investigated.Comment: 32 pages, LaTex, 8 ps figure