An evaluation of current SIMD programming models for C++

SIMD extensions were added to microprocessors in the mid '90s to speed-up data-parallel code by vectorization. Unfortunately, the SIMD programming model has barely evolved and the most efficient utilization is still obtained with elaborate intrinsics coding. As a consequence, several approaches to write efficient and portable SIMD code have been proposed. In this work, we evaluate current programming models for the C++ language, which claim to simplify SIMD programming while maintaining high performance. The proposals were assessed by implementing two kernels: one standard floating-point benchmark and one real-world integer-based application, both highly data parallel. Results show that the proposed solutions perform well for the floating point kernel, achieving close to the maximum possible speed-up. For the real-world application, the programming models exhibit significant performance gaps due to data type issues, missing template support and other problems discussed in this paper

Getting Surplus Countries to Adjust

It has been 80 years since John Maynard Keynes first proposed a plan that would have disciplined persistent surplus countries. But the Keynes Plan, like the subsequent Volcker Plan in 1972-74, was defeated by the major surplus country of the day (the United States and Germany, respectively), and today China (not to mention Japan or Germany) exhibits no enthusiasm for new revisions of these ideas. Williamson evaluates the two earlier attempts and several new proposals now on the table. Morris Goldstein proposes using the International Monetary Fund (IMF) to discipline surplus countries. Countries showing large and persistent current account surpluses would receive a Fund mission with the purpose of judging whether the country had a misaligned exchange rate. Penalties would depend on the size and persistence of any misalignment the Fund diagnosed. Aaditya Mattoo and Arvind Subramanian propose that countries could bring a case for unfair trade through currency undervaluation to the World Trade Organization (WTO) dispute settlement system. The WTO would seek to establish the facts of the matter from the IMF. C. Fred Bergsten proposes that either a reserve currency country or the IMF itself should be able to engage in counter-intervention to push up the value of a currency that is being deliberately held down to an undervalued rate through intervention. US Secretary of the Treasury Timothy Geithner, echoing ideas of the Korean G-20 summit hosts and endorsed by Yi Gang, a vice governor of the People's Bank of China, has proposed that members of the G-20 should commit themselves to limit their current account imbalances to a maximum of 4 percent of GDP. Daniel Gros and Gary Hufbauer have advanced other ways of disciplining surplus countries, by limiting or taxing the assets that surplus countries can hold. Williamson suggests incorporating ideas from the various proposals into a feasible mechanism for disciplining surplus countries. He finds the Mattoo-Subramanian proposal most attractive for typical countries: They focus attention on the exchange rate rather than reserve holdings, seek to use the IMF in an area where it undoubtedly has expertise, but also exploit the greatest success in international cooperation in recent years, namely the dispute settlement mechanism of the WTO. To deal with the problem when a member of a monetary union--which by definition does not have an independent exchange rate--has an excessive surplus, Williamson suggests running the Goldstein proposal in parallel--perhaps operated by the European Central Bank rather than the IMF, and with the focus on the level of demand rather than the exchange rate.

Accelerating MCMC via Parallel Predictive Prefetching

We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast, iterative approximations to the target density to speculatively evaluate many potential future steps of the chain in parallel. The approach can accelerate computation of the target distribution of a Bayesian inference problem, without compromising exactness, by exploiting subsets of data. It takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, it achieves speedup over serial evaluation that is close to linear in the number of available cores

Orthogonal parallel MCMC methods for sampling and optimization

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called {\it orthogonal MCMC} (O-MCMC), where a set of "vertical" parallel MCMC chains share information using some "horizontal" MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel multiple try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel simulated annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters

Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

Parallel Metropolis chains with cooperative adaptation

Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC scheme where parallel MCMC chains interact, adapting cooperatively the parameters of their proposal functions. Furthermore, the novel algorithm distributes the computational effort adaptively, rewarding the chains which are providing better performance and, possibly even stopping other ones. These extinct chains can be reactivated if the algorithm considers necessary. Numerical simulations shows the benefits of the novel scheme