Trade and synchronization in a multi-country economy

Substantial evidence suggests that countries with stronger trade linkages have more synchro- nized business cycles. The standard international business cycle framework cannot replicate this finding, uncovering the trade-comovement puzzle. We show that under certain macro-level conditions but irrespective of the micro-level assumptions concerning trade the puzzle arises because trade fails to substantially increase the correlation between each country's import penetration ratio and the trade partner's technology shock. Within a large class of trade models, there are three channels through which bilateral trade may increase business cycle synchronization. Specifically, increased bilateral trade may (i) raise the correlation between each country's tech- nology shocks, (ii) raise the correlation between each country's share of expenditure on domestic goods, and (iii) raise the response of the domestic import penetration ratio to foreign technology shocks. Empirical evidence strongly supports the first and second channels. We show that the trade-comovement puzzle can be resolved if productivity shocks are more correlated between country-pairs that trade more

Problems in characterizing barrier performance

The barrier is a synchronization construct which is useful in separating a parallel program into parallel sections which are executed in sequence. The completion of a barrier requires cooperation among all executing processes. This requirement not only introduces the wait for the slowest process delay which is inherent in the definition of the synchronization, but also has implications for the efficient implementation and measurement of barrier performance in different systems. Types of barrier implementation and their relationship to different multiprocessor environments are described. Then the problem of measuring the performance of barrier implementations on specific machine architecture is discussed. The fact that the barrier synchronization requires the cooperation of all processes makes the problem of performance measurement similarly global. Making non-intrusive measurements of sufficient accuracy can be tricky on systems offering only rudimentary measurement tools

Reentrant transition in coupled noisy oscillators

We report on a novel type of instability observed in a noisy oscillator unidirectionally coupled to a pacemaker. Using a phase oscillator model, we find that, as the coupling strength is increased, the noisy oscillator lags behind the pacemaker more frequently and the phase slip rate increases, which may not be observed in averaged phase models such as the Kuramoto model. Investigation of the corresponding Fokker-Planck equation enables us to obtain the reentrant transition line between the synchronized state and the phase slip state. We verify our theory using the Brusselator model, suggesting that this reentrant transition can be found in a wide range of limit cycle oscillators.Comment: 16 pages, 7 figure

Industry Structure Similarities, Trade Agreements, and Business Cycle Synchronization

This paper analyzes the effects of industry structure similarities, free trade agreements, and geographic borders on regional business cycle correlation, using fifty US states, 10 Canadian provinces, and 1 Canadian territory as a case study. Using two cross-sectional OLS regressions and one panel data OLS regression, this study finds that pair-wise gross territorial product growth correlation decreased significantly after NAFTA ratification for state-state, province-province, and state-province territorial pairs, contrary to previous literature’s results. NAFTA effectively decoupled intra-national business cycles in the US and Canada while also desynchronizing cross-border pair-wise GSP growth correlation, but cross-border pair-wise GSP growth correlation was much less desynchronized post-NAFTA relative to intra-national pairs. These results indicate that NAFTA and the US-Canada border may produce two opposing forces that dampen each other’s desynchronizing effects

BarrierPoint: sampled simulation of multi-threaded applications

Sampling is a well-known technique to speed up architectural simulation of long-running workloads while maintaining accurate performance predictions. A number of sampling techniques have recently been developed that extend well- known single-threaded techniques to allow sampled simulation of multi-threaded applications. Unfortunately, prior work is limited to non-synchronizing applications (e.g., server throughput workloads); requires the functional simulation of the entire application using a detailed cache hierarchy which limits the overall simulation speedup potential; leads to different units of work across different processor architectures which complicates performance analysis; or, requires massive machine resources to achieve reasonable simulation speedups. In this work, we propose BarrierPoint, a sampling methodology to accelerate simulation by leveraging globally synchronizing barriers in multi-threaded applications. BarrierPoint collects microarchitecture-independent code and data signatures to determine the most representative inter-barrier regions, called barrierpoints. BarrierPoint estimates total application execution time (and other performance metrics of interest) through detailed simulation of these barrierpoints only, leading to substantial simulation speedups. Barrierpoints can be simulated in parallel, use fewer simulation resources, and define fixed units of work to be used in performance comparisons across processor architectures. Our evaluation of BarrierPoint using NPB and Parsec benchmarks reports average simulation speedups of 24.7x (and up to 866.6x) with an average simulation error of 0.9% and 2.9% at most. On average, BarrierPoint reduces the number of simulation machine resources needed by 78x

Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation

Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the "changes" between iterations. By DAIC, we can process only the "changes" to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60x speedup over Hadoop and outperforms other state-of-the-art frameworks.Comment: ScienceCloud 2012, TKDE 201