TaskPoint: sampled simulation of task-based programs

Sampled simulation is a mature technique for reducing simulation time of single-threaded programs, but it is not directly applicable to simulation of multi-threaded architectures. Recent multi-threaded sampling techniques assume that the workload assigned to each thread does not change across multiple executions of a program. This assumption does not hold for dynamically scheduled task-based programming models. Task-based programming models allow the programmer to specify program segments as tasks which are instantiated many times and scheduled dynamically to available threads. Due to system noise and variation in scheduling decisions, two consecutive executions on the same machine typically result in different instruction streams processed by each thread. In this paper, we propose TaskPoint, a sampled simulation technique for dynamically scheduled task-based programs. We leverage task instances as sampling units and simulate only a fraction of all task instances in detail. Between detailed simulation intervals we employ a novel fast-forward mechanism for dynamically scheduled programs. We evaluate the proposed technique on a set of 19 task-based parallel benchmarks and two different architectures. Compared to detailed simulation, TaskPoint accelerates architectural simulation with 64 simulated threads by an average factor of 19.1 at an average error of 1.8% and a maximum error of 15.0%.This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493, SEV-2011-00067), the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), the RoMoL ERC Advanced Grant (GA 321253), the European HiPEAC Network of Excellence and the Mont-Blanc project (EU-FP7-610402 and EU-H2020-671697). M. Moreto has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship JCI-2012-15047. M. Casas is supported by the Ministry of Economy and Knowledge of the Government of Catalonia and the Cofund programme of the Marie Curie Actions of the EUFP7 (contract 2013BP B 00243). T.Grass has been partially supported by the AGAUR of the Generalitat de Catalunya (grant 2013FI B 0058).Peer ReviewedPostprint (author's final draft

Placing the poor while keeping the rich in their place

A central objective of modern US housing policy is deconcentrating poverty through "housing mobility programs" that move poor families into middle class neighborhoods. Pursuing these policies too aggressively risks inducing middle class flight, but being too cautious squanders the opportunity to help more poor families. This paper presents a stylized dynamicoptimization model that captures this tension. With base-caseparameter values, cost considerations limit mobility programs before flight becomes excessive. However, for modest departures reflecting stronger flight tendencies and/or weaker destination neighborhoods, other outcomes emerge. In particular, we find state-dependence and multiple equilibria, including both de-populated and oversized outcomes. For certain sets of parameters there exists a Skiba point that separates initial conditions for which the optimal strategy leads to substantial flight and depopulation from those for which the optimal strategy retains or even expands the middle class population. These results suggest the value of estimating middle-class neighborhoods' "carrying capacity" for absorbing mobility program placements and further modeling of dynamic response.housing policy, multiple equilibria, negative externality, optimal control, segregation, separation, Skiba point

Extremal non-BPS black holes and entropy extremization

At the horizon, a static extremal black hole solution in N=2 supergravity in four dimensions is determined by a set of so-called attractor equations which, in the absence of higher-curvature interactions, can be derived as extremization conditions for the black hole potential or, equivalently, for the entropy function. We contrast both methods by explicitly solving the attractor equations for a one-modulus prepotential associated with the conifold. We find that near the conifold point, the non-supersymmetric solution has a substantially different behavior than the supersymmetric solution. We analyze the stability of the solutions and the extrema of the resulting entropy as a function of the modulus. For the non-BPS solution the region of attractivity and the maximum of the entropy do not coincide with the conifold point.Comment: 19 pages, 4 figures, AMS-LaTeX, reference adde

Task sampling: computer architecture simulation in the many-core era

Chip Multi-Processors (CMPs) are evolving towards ever increasing core counts. Task-based programming models are a promising candidate for exploiting the parallelism offered by these machines. Simulation, the prevailing design methodology in computer architecture, is prohibitively time consuming, when it comes to CMPs featuring 1000s of cores. Sampled simulation is a standard technique for reducing simulation time for single-threaded architectures. Recently, these techniques have been extended to allow for simulation of multi-threaded systems. However, they have not been assessed for dynamically scheduled multi-threaded programs. In this work we use the OmpSs programming model [4]. OmpSs, an extension of OpenMP, allows to declare code blocks as tasks and to specify data consumed and produced by each task. The runtime environment executes tasks, potentially out of program order, on available cores, similar to the out-oforder execution in a superscalar processor.Peer ReviewedPostprint (published version

Optimal management and spatial patterns in a distributed shallow lake model

We present a numerical framework to treat infinite time horizon spatially distributed optimal control problems via the associated canonical system derived by Pontryagin's maximum principle. The basic idea is to consider the canonical system in two steps. First we perform a bifurcation analysis of canonical steady states using the continuation and bifurcation package {\tt pde2path}, yielding a number of so called flat and patterned canonical steady states. In a second step we link pde2path to the two point boundary value problem solver TOM to study time dependent canonical paths to steady states having the so called saddle point property. As an example we consider a shallow lake model with diffusion

Lanchester Model for Three-Way Combat

Lanchester (1916) modeled combat situations between two opponents, where mutual attrition occurs continuously in time, by a pair of simple ordinary (linear) differential equations. The aim of the present paper is to extend the model to a conflict consisting of three parties. In particular, Lanchester's main result, i.e. his square law, is adapted to a triple fight. However, here a central factor besides the initial strengths of the forces determining the long run outcome is the allocation of each opponent's efforts between the other two parties. De- pending on initial strengths, (the) solution paths are calculated and visualized in appropriate phase portraits. We are able identify regions in the state space where, independent of the force allocation of the opponents, always the same combatant wins, regions, where a combatant can win if its force allocation is wisely chosen, and regions where a combatant cannot win itself but determine the winner by its forces allocation. As such, the present model can be seen as a forerunner of a dynamic game between three opponents.Naval Research ProgramThis research was supported by the Austrian Science Fund (FWF)P25979-N2

A non-autonomous optimal control model of renewable energy production under the aspect of fluctuating supply and learning by doing

Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.Austrian Science Fund (FWF

Optimal scientific production over the lifecycle

The paper develops an optimization model of the career of a scientist. Recognizing that research efforts and networking get more efficient if the scientist is more knowledgeable, history dependent solutions are developed. We give a theoretical underpinning of the four different research patterns detected in Way et al. (2017, Proceedings of the National Academy of Sciences). If the scientist does not bother about his reputation at the end of his career, we show that a sufficient education level is needed for the scientist to develop a typical research pattern where productivity increases in the beginning of his career, while it declines towards retirement. If the education level is not sufficient, a fading research pattern will result where productivity declines over time. On the other hand, when the scientist appreciates to have a good reputation at the end of his career, sufficient education will result in increasing productivity over the career lifetime, preventing a midlife slump