Parallelization of Stochastic Evolution for Cell Placement

VLSI physical design and the problems related to it such as placement, channel routing, etc, carry inherent complexities that are best dealt with iterative heuristics. However the major drawback of these iterative heuristics has been the large runtime involved in reaching acceptable solutions especially when optimizing for multiple objectives. Among the acceleration techniques proposed, parallelization is one promising method. Distributed memory multiprocessor systems and shared memory multiprocessor systems have gained considerable attention in recent years of research. This idea of parallel computing has attracted both the researchers and manufacturers who are targeting to reduce the time to market. Our objective is to exploit the benefits of parallel computing for a time consuming placement problem in VLSI. Finding the best solution for the placement of n modules is a hard problem. Thus the enumerative search techniques, specially those which employ the brute force, are unaccepted for the circuits in which n (number of modules) is large. Constructive and Iterative heuristics play the key role in this scenario and hence are frequently used. We will use Stochastic Evolution for finding the optimal solution to the above mentioned placement problem where the major task in our objective will be the parallelization of Stochastic Evolution using different parallelization techniques and the comparison between these different parallelized versions based on the results achieved. The parallelization will be carried out using MPI (Message Passing Interface) on a distributed memory multiprocessor system and conclusion will be based on the results achieved that are expected to show speedup nearly equal to linear speedup when run over increasing number of processors

Parallelization of Stochastic Evolution for Cell Placement

VLSI physical design and the problems related to it such as placement, channel routing, etc, carry inherent complexities that are best dealt with iterative heuristics. However the major drawback of these iterative heuristics has been the large runtime involved in reaching acceptable solutions especially when optimizing for multiple objectives. Among the acceleration techniques proposed, parallelization is one promising method. Distributed memory multiprocessor systems and shared memory multiprocessor systems have gained considerable attention in recent years of research. This idea of parallel computing has attracted both the researchers and manufacturers who are targeting to reduce the time to market. Our objective is to exploit the benefits of parallel computing for a time consuming placement problem in VLSI. Finding the best solution for the placement of n modules is a hard problem. Thus the enumerative search techniques, specially those which employ the brute force, are unaccepted for the circuits in which n (number of modules) is large. Constructive and Iterative heuristics play the key role in this scenario and hence are frequently used. We will use Stochastic Evolution for finding the optimal solution to the above mentioned placement problem where the major task in our objective will be the parallelization of Stochastic Evolution using different parallelization techniques and the comparison between these different parallelized versions based on the results achieved. The parallelization will be carried out using MPI (Message Passing Interface) on a distributed memory multiprocessor system and conclusion will be based on the results achieved that are expected to show speedup nearly equal to linear speedup when run over increasing number of processors

Parallelization of Stochastic Evolution for Cell Placement

VLSI physical design and the problems related to it such as placement, channel routing, etc, carry inherent complexities that are best dealt with iterative heuristics. However the major drawback of these iterative heuristics has been the large runtime involved in reaching acceptable solutions especially when optimizing for multiple objectives. Among the acceleration techniques proposed, parallelization is one promising method. Distributed memory multiprocessor systems and shared memory multiprocessor systems have gained considerable attention in recent years of research. This idea of parallel computing has attracted both the researchers and manufacturers who are targeting to reduce the time to market. Our objective is to exploit the benefits of parallel computing for a time consuming placement problem in VLSI. Finding the best solution for the placement of n modules is a hard problem. Thus the enumerative search techniques, specially those which employ the brute force, are unaccepted for the circuits in which n (number of modules) is large. Constructive and Iterative heuristics play the key role in this scenario and hence are frequently used. We will use Stochastic Evolution for finding the optimal solution to the above mentioned placement problem where the major task in our objective will be the parallelization of Stochastic Evolution using different parallelization techniques and the comparison between these different parallelized versions based on the results achieved. The parallelization will be carried out using MPI (Message Passing Interface) on a distributed memory multiprocessor system and conclusion will be based on the results achieved that are expected to show speedup nearly equal to linear speedup when run over increasing number of processors

(2-Chloro­benzo[h]quinolin-3-yl)methanol

In the title mol­ecule, C14H10ClNO, all non-H atoms are coplanar (r.m.s deviation = 0.0266 Å). In the crystal, symmetry-related mol­ecules are hydrogen bonded via inter­molecular O—H⋯O inter­actions, forming chains along the b axis

Natural disasters and economic losses: controlling external migration, energy and environmental resources, water demand, and financial development for global prosperity

The objective of the study is to examine the impact of natural disasters on external migration, price level, poverty incidence, health expenditures, energy and environmental resources, water demand, financial development, and economic growth in a panel of selected Asian countries for a period of 2005–2017. The results confirm that natural disasters in the form of storm and flood largely increase migration, price level, and poverty incidence, which negatively influenced country’s economic resources, including enlarge healthcare expenditures, high energy demand, and low economic growth. The study further presented the following results: i) natural resource depletion increases external migration, ii) FDI inflows increase price level, iii) increase healthcare spending and energy demand decreases poverty headcount, iv) poverty incidence and mortality rate negatively influenced healthcare expenditures, v) industrialization increases energy demand, and vi) agriculture value added, fertilizer, and cereal yields required more water supply to produce greater yield. The study emphasized the need to magnify the intensity of natural disasters and create natural disaster mitigation unit to access the human and infrastructure cost and attempt quick recovery for global prosperity

Parallel Stochastic Evolution Algorithms for Constrained Multiobjective Optimization

Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solution qualities and runtimes when compared to some other well established stochastic metaheuristics. However, unlike these metaheuristics, parallelization of StocE has not been explored before. In this paper, we discuss a comprehensive set of parallel strategies for StocE using a constrained multiobjective VLSI cell placement as an optimization problem. The effectiveness of the proposed strategy is demonstrated by comparing its results with results of parallel SA algorithms on the same optimization problem

Parallel Stochastic Evolution Algorithms for Constrained Multiobjective Optimization

Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solution qualities and runtimes when compared to some other well established stochastic metaheuristics. However, unlike these metaheuristics, parallelization of StocE has not been explored before. In this paper, we discuss a comprehensive set of parallel strategies for StocE using a constrained multiobjective VLSI cell placement as an optimization problem. The effectiveness of the proposed strategy is demonstrated by comparing its results with results of parallel SA algorithms on the same optimization problem

Effect of Organic Fertilizer on the Growth of Tea ((Camellia sinensis L.)

This study was conducted at National Tea and High value crops Research Institute, Shinkiari, Mansehra during 2014- 2015. The objective of the experiment was to evaluate the response of different doses of organic fertilizers to the growth of newly planted tea (Camellia sinensis L., variety, Turkish). Different doses of Hyosung applied as kg/acre were T0= Control, T1 = (400), T2 = (500), T3 = (600), T4 = (700) and T5 = (800). The data recorded during growth season during 2014 and 2015. Highest plant height (50 & 52 cm), Number of leaves per plant (39.67, 17.75) and Number of branches/plant (7.67, 6.29) were recorded in T4 respectively followed by T5 while maximum leaf area was recorded in T4 (48 cm2) during 2014 and T3 (47.87cm2) during 2015 respectively. Soil samples were collected at 0-20 cm and 21-40 cm depth and analyzed for physico-chemical characteristics. It is revealed that soil was sandy loam in texture with soil pH 6.25 and 2.90% organic matter and was supportive for the good growth of tea crop. A gradual increase in the organic matter content was observed with the increase of fertilizer dose. Soil pH was slightly decreased by the application of organic fertilizer which is a good sign to increase the growth of tea crop

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability