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We present the International Lattice Data Grid (ILDG), a loosely federated grid of grids for sharing data from Lattice Quantum Chromodynamics (LQCD) simulations. The ILDG comprises of metadata, file format and web-service standards, which can be used to wrap regional data-grid interfaces, allowing seamless access to catalogues and data in a diverse set of collaborating regional grids. We discuss the technological underpinnings of the ILDG, primarily the metadata and the middleware, and offer a critique of its various aspects with the hindsight of the design work and the first full year of production.Comment: 13 page

We have measured the dynamical critical exponent z for the Swendsen-Wang and the Wolff cluster update algorithms, as well as a number of variants of these algorithms, for the q = 2 and q = 3 Potts models in two dimensions. We find that although the autocorrelation times differ considerably between algorithms, the critical exponents are the same. For q = 2, we find that although the data are better fitted by a logarithmic increase in the autocorrelation time with lattice size, they are also consistent with a power law with exponent z ≈ 0.25, especially if there are non-negligible corrections to scaling.

We have measured the autocorrelations for the Swendsen-Wang and the Wolff cluster update algorithms for the Ising model in two, three, and four dimensions. The data for the Wolff algorithm suggest that the autocorrelations are linearly related to the specific heat, in which case the dynamic critical exponent is Zim^^cr/v. For the Swendsen-Wang algorithm, scaling the autocorrelations by the average maximum cluster size gives either a constant or a logarithm, which implies that Z §0!E =/J/v for the Ising model. PACS numbers: 64.60.Ht, 05.50.+q, 11.15.Ha The Monte Carlo cluster update algorithms of Swendsen and Wang (SW) [1] and Wolff [2] can dramatically reduce critical slowing down in computer simulations of spin models, and thus greatly increase the computational efficiency of the simulations (for reviews of cluster algorithms, see Refs. [3,4]). There is little theoretical understanding of the dynamics of these algorithms. In particular, little is known as to why they seem to eliminate critical slowing down completely in some cases and not others. There is no known theory which can predict the value of the dynamic critical exponent z for any spin model, although a rigorous bound on z for the SW algorithm for Potts models has been derived [5]. Another problem which is not well understood is why the SW and Wolff algorithms give similar values of z for the 2D Potts model [6], but have very different behaviors for other models, such as the Ising model in more than two dimensions [7,8].The measurement of dynamic critical exponents is notoriously difficult, and both very good statistics and very large lattices are required in order to obtain accurate results. This is certainly the case for the Ising model, where a number of different measurements have given conflicting results. For the two-dimensional Ising model, initial results suggested z ~ y for both the SW and Wolff algorithms [1,8]. Further work [7] gave z~ y, and it was later shown that the data were consistent with a logarithmic divergence, suggesting that z =0 [9]. Recent results show that it is very difficult to distinguish between a logarithm and a small power [6], Measurements on the three-dimensional model have proven to be just as difficult, with values of z for the SW algorithm ranging from 0.339(4) to 0.75(1) [1,7,10]. For the Wolff algorithm, Tamayo, Brower, and Klein [8] obtained 0.44(10), while Wolff found a value of 0.28(2) for the energy autocorrelations [7]. We have examined Wolff's data and found that they are also fitted well by a logarithm, so that z =0 is also a possibility.In four dimensions only one result is known, which is z = -0.05(15) for the Wolff algorithm [8]. Simulations have also been done on the mean-field Ising model, which 10

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