Parallelization of Stochastic Evolution

The complexity involved in VLSI design and its sub-problems has always made them ideal application areas for non-eterministic iterative heuristics. However, the major drawback has been the large runtime involved in reaching acceptable solutions especially in the case of multi-objective optimization problems. Among the acceleration techniques proposed, parallelization of iterative heuristics is a promising one. The motivation for Parallel CAD include faster runtimes, handling of larger problem sizes, and exploration of larger search space. In this work, the development of parallel algorithms for Stochastic Evolution, applied on multi-objective VLSI cell-placement problem is presented. In VLSI circuit design, placement is the process of arranging circuit blocks on a layout. In standard cell design, placement consists of determining optimum positions of all blocks on the layout to satisfy the constraint and improve a number of objectives. The placement objectives in our work are to reduce power dissipation and wire-length while improving performance (timing). The parallelization is achieved on a cluster of workstations interconnected by a low-latency network, by using MPI communication libraries. Circuits from ISCAS-89 are used as benchmarks. Results for parallel Stochastic Evolution are compared with its sequential counterpart as well as with the results achieved by parallel versions of Simulated Annealing as a reference point for both, the quality of solution as well as the execution time. After parallelization, linear and super linear speed-ups were obtained, with no degradation in quality of the solution

Parallelization of Stochastic Evolution

The complexity involved in VLSI design and its sub-problems has always made them ideal application areas for non-eterministic iterative heuristics. However, the major drawback has been the large runtime involved in reaching acceptable solutions especially in the case of multi-objective optimization problems. Among the acceleration techniques proposed, parallelization of iterative heuristics is a promising one. The motivation for Parallel CAD include faster runtimes, handling of larger problem sizes, and exploration of larger search space. In this work, the development of parallel algorithms for Stochastic Evolution, applied on multi-objective VLSI cell-placement problem is presented. In VLSI circuit design, placement is the process of arranging circuit blocks on a layout. In standard cell design, placement consists of determining optimum positions of all blocks on the layout to satisfy the constraint and improve a number of objectives. The placement objectives in our work are to reduce power dissipation and wire-length while improving performance (timing). The parallelization is achieved on a cluster of workstations interconnected by a low-latency network, by using MPI communication libraries. Circuits from ISCAS-89 are used as benchmarks. Results for parallel Stochastic Evolution are compared with its sequential counterpart as well as with the results achieved by parallel versions of Simulated Annealing as a reference point for both, the quality of solution as well as the execution time. After parallelization, linear and super linear speed-ups were obtained, with no degradation in quality of the solution

Evaluating the Nature of Distractive Driving Factors towards Road Traffic Accident

For past two decades many researchers have been working on quantitative as well as qualitative study of distractive driving using different approaches. Road traffic accidents have been identified as the main source of human casualties and cause of damages to the economy and society, as millions of humans is killed every year in these accidents around the world. National-level studies in Pakistan reveal that a higher percentage of males in the age group from twenty to forty years lose their lives in road traffic accidents when compared with that of females. Due to these factors, it is alarming for a society, which is highly dependent on males such as Pakistan, as these losses put numerous families into the financial crisis that lead to poverty. This study envisaged identifying whether moods and emotions play any role in road traffic accidents of young drivers. The study reviews have shown various gaps in our understanding. For this purpose, qualitative interviews of young drivers who are university going and have met some road accidents in recent years in Pakistan had been conducted. Data from the interviews had been transcribed for analysis while maintaining the anonymity of the participants for confidentiality. Analysis of the transcribed data reveals various factors that contribute to road traffic accidents where major causes are distractions, different weather conditions, sleep deprivation, unsafe lane changes, night-time driving, and these factors are triggered by the behavior when youthful drivers engage in driving for sensation seeking and self-esteem. We conclude that it is just through the appropriation of a systems approach that coordinated countermeasures can be proposed and actualized to relieve driver mistakes caused by distraction

Evaluating Energy Security Performance in Pakistan and India through Aggregated Energy Security Performance Indicators (AESPI)

This article computes the energy security performance between two crucial South Asian neighboring countries in the region through aggregated energy security performance indicators (AESPI) using time series data of time spanning 1990-2018. The findings of this manuscript suggest that total primary energy supply, final energy consumption, household electricity consumption, share of non- carbon energy per total primary energy consumption, net energy import dependency Co2 emissions per capita and per GDP and residential energy consumption lead to make better energy security performance in Pakistan. However, total primary electricity consumption, total primary and final energy intensity, reserve production ratio of oil & gas and transformation losses adversely affect energy security performance in Pakistan. On another end, in India final energy consumption, total primary energy intensity, household electricity consumption, share of capacity of renewable energy per total electricity generation, share of renewable energy per final energy consumption, net energy import dependency and Co2 emissions per capita lead to improve energy security performance. Conversely, total primary energy supply, total primary electricity consumption, final energy intensity, transformation loss, reserve production ratio of oil & gas, share of non-carbon energy per total primary energy supply and Co2 emissions per GDP may cause to reduce energy security performance in India. In conclusion, the overall energy security performance is improved in both the countries by time, India (more improved than Pakistan) and Pakistan, as the findings of this manuscript suggest

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

Juvenile Ossifying Fibroma in a 14-year-old female: A Case Report

Juvenile Ossifying Fibroma is a rare benign bone neoplasm affecting age-group of 5-15 which tends to mimic malignant lesions. This is in opposition to its conventional variant which occurs mostly in middle aged cohort and is relatively docile. It's aetiology remains unknown and it is characterized by substitution of normal bone by mineralized fibrous tissue. The rapid progression, aggressive nature and high recurrence potential are features that makes JOF of vying attention. Additionally it's prognosis being greatly dependent on time and type of intervention employed is widely documented. In this study JOF with a history of only 7 months is reported in a 14 year old female. The affected bone was maxilla with dental and palatal involvement. Partial Maxillectomy and BIPP for reconstruction were applied as treatment of choice for this patient. Yearly follow up protocol for next 5 years was devised. No post op complications were observed