When parallel speedups hit the memory wall

After Amdahl's trailblazing work, many other authors proposed analytical speedup models but none have considered the limiting effect of the memory wall. These models exploited aspects such as problem-size variation, memory size, communication overhead, and synchronization overhead, but data-access delays are assumed to be constant. Nevertheless, such delays can vary, for example, according to the number of cores used and the ratio between processor and memory frequencies. Given the large number of possible configurations of operating frequency and number of cores that current architectures can offer, suitable speedup models to describe such variations among these configurations are quite desirable for off-line or on-line scheduling decisions. This work proposes new parallel speedup models that account for variations of the average data-access delay to describe the limiting effect of the memory wall on parallel speedups. Analytical results indicate that the proposed modeling can capture the desired behavior while experimental hardware results validate the former. Additionally, we show that when accounting for parameters that reflect the intrinsic characteristics of the applications, such as degree of parallelism and susceptibility to the memory wall, our proposal has significant advantages over machine-learning-based modeling. Moreover, besides being black-box modeling, our experiments show that conventional machine-learning modeling needs about one order of magnitude more measurements to reach the same level of accuracy achieved in our modeling.Comment: 24 page

Less is more: simplified Nelder-Mead method for large unconstrained optimization

Nelder-Mead method (NM) for solving continuous non-linear optimization problem is probably the most cited and the most used method in the optimization literature and in practical applications, too. It belongs to the direct search methods, those which do not use the first and the second order derivatives. The popularity of NM is based on its simplicity. In this paper we propose even more simple algorithm for larger instances that follows NM idea. We call it Simplified NM (SNM): instead of generating all n + 1 simplex points in Rn, we perform search using just q + 1 vertices, where q is usually much smaller than n. Though the results cannot be better than after performing calculations in n+1 points as in NM, significant speed-up allows to run many times SNM from different starting solutions, usually getting better results than those obtained by NM within the same cpu time. Computational analysis is performed on 10 classical convex and non-convex instances, where the number of variables n can be arbitrarily large. The obtained results show that SNM is more effective than the original NM, confirming that LIMA yields good results when solving a continuous optimization problem

Variabilidade espacial da produtividade e do estado nutricional do cafeeiro Canephora

Utilizing precision farming techniques along with the Diagnosis and Recommendation Integrated System (DRIS) allows crop management to be improved, thereby making it possible to better control plant nutrition and to assist in reducing fertilizer expenditures. This study aimed to evaluate the spatial variability of the nutritional status of conilon coffee (Coffea canephora), using the Nutritional Balance index (NBI). 140 points were georeferenced within a coffee crop, each sampling point contained five plants. Leaf samples were analyzed in order to determine levels of N, P, K, Ca, Mg, S, Fe, B, Zn, Mn and Cu. The crop showed itself to have a nutritional imbalance, as shown by the deficiency and excess variation of some nutrients in the crop. The nutritional balance index (NBI) was not correlated with productivity (Prod), indicating that, when the crop has a high nutritional imbalance IBN is not a good tool for establishing nutritional standards for conilon coffee.O uso das técnicas da agricultura de precisão aliada ao Sistema Integrado de Diagnose e Recomendação (DRIS) permite o aperfeiçoamento do manejo da lavoura, possibilitando melhor controle nutricional da planta e contribuindo para reduzir gastos com fertilizante. Objetivou-se, neste trabalho, avaliar a variabilidade espacial do estado nutricional do cafeeiro conilon (Coffea canephora), utilizando o Índice de Balanço Nutricional (IBN). Em uma lavoura de café foram amostrados 140 pontos georreferenciados, sendo cada ponto amostral constituído de cinco plantas. As amostras foliares foram analisadas para determinação dos teores de N, P, K, Ca, Mg, S, Fe, B, Zn, Mn e Cu. A lavoura apresenta desequilíbrio nutricional mostrado pela variação da deficiência e excesso de alguns nutrientes na lavoura. O índice de balanceamento nutricional (IBN) não apresentou correlação com a produtividade (Prod), indicando que, quando a lavoura apresenta elevado desequilíbrio nutricional o IBN não é uma boa ferramenta para o estabelecimento de um padrão nutricional para o café conilon

Variabilidade espacial e temporal da produtividade do cafeeiro canephora

GIS techniques have been used in many agricultural crops to study and assess the causes of spatial and temporal variability in production. The spatial and temporal variability of the canephora (conilon) coffee productivity was analyzed in the present work in three consecutive agricultural years (harvests) using geoprocessing techniques. A sampling grid with 109 georeferenced points was built with five plants per point. Significant differences in productivity were observed, with the lowest productivity recorded for the harvest 3 in 93.5% of the area. The productivity index (YI) varied from -18.0% in harvest 2 to harvest 1 and from -57.0% in harvest 2 to harvest 3, showing increasing decrease between different harvests.Em várias culturas, tem-se utilizado técnicas de geoprocessamento com intuito de estudar e interpretar as causas da variabilidade espacial e temporal da sua produção. Este trabalho foi desenvolvido objetivando-se analisar a variabilidade espacial e temporal da produtividade do cafeeiro canephora (conilon) em três safras consecutivas, utilizando técnicas de geoprocessamento. Uma malha amostral foi construída com 109 pontos georreferenciados, considerando cinco plantas por ponto. As produtividades apresentaram diferenças significativas, com menor produtividade na safra 3, em 93,5% da área. O índice de produtividade (IP) ficou da safra 2 para a 1 em -18,0% e da safra 3 para a 2 em -57,0%, mostrando redução crescente entre as diferentes safras