Sharif Digital Repository

Density peaks clustering (DPC) is as an efficient clustering algorithm due for using a non-iterative process. However, DPC and most of its improvements suffer from the following shortcomings: (1) highly sensitive to its cutoff distance parameter, (2) ignoring the local structure of data in computing local densities, (3) using a crisp kernel to calculate local densities, and (4) suffering from the cause of chain reaction. To address these issues, in this paper a new method called DPC-DBFN is proposed. The proposed method uses a fuzzy kernel for improving separability of clusters and reducing the impact of outliers. DPC-DBFN uses a density-based kNN graph for labeling backbones. This strategy... 

Memory-intensive workloads operate on massive amounts of data that cannot be captured by last-level caches (LLCs) of modern processors. Consequently, processors encounter frequent off-chip misses, and hence, lose significant performance potential. One of the components of a modern processor that has a prominent influence on the off-chip miss traffic is LLC's replacement policy. Existing processors employ a variation of least recently used (LRU) policy to determine the victim for replacement. Unfortunately, there is a large gap between what LRU offers and that of Belady's MIN, which is the optimal replacement policy. Belady's MIN requires selecting a victim with the longest reuse distance,... 

Memory-intensive workloads operate on massive amounts of data that cannot be captured by last-level caches (LLCs) of modern processors. Consequently, processors encounter frequent off-chip misses, and hence, lose significant performance potential. One of the components of a modern processor that has a prominent influence on the off-chip miss traffic is LLC's replacement policy. Existing processors employ a variation of least recently used (LRU) policy to determine the victim for replacement. Unfortunately, there is a large gap between what LRU offers and that of Belady's MIN, which is the optimal replacement policy. Belady's MIN requires selecting a victim with the longest reuse distance,... 

Evolutionary Algorithms are vastly used in development of rule based classifier systems in data mining where the rule base is usually a set of If-Then rules and an evolutionary trait develops and optimizes these rules. Genetic Algorithm is usually a favorite solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. Also, designing a good genetic algorithm for rule base evolution requires the design of a recombination operator that merges two rule bases without disrupting the functionalities of each of them. To overcome the speed problem and the need to design recombination operator, this paper presents a novel algorithm... 

This work presents the results of numerical simulation of liquid flow on a static bubble. Velocity and surfactant concentration profiles of the liquid around a bubble exposed in flow field were estimated via unsteady CFD solution. Governing equations are the Navier-Stokes and conservation of mass in the liquid bulk and at the interface. Resulted profiles of surfactant concentration and velocity in the liquid bulk are used to determine the dynamic surface tension data via two adsorption isotherms of Langmuir and Frumkin. Data of dynamic surface tension were validated via experimental data of “bubble in flow field” protocol. Surface concentrations of surfactants were also numerically... 

Genetic Algorithms are vastly used in development of rule based classifier systems in data mining. In such tasks, the rule base is usually a set of If-Then rules and the rules are developed using an evolutionary trait. GA is usually a good solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. This paper presents a novel algorithm for rule base generation based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. The new algorithm is compared with genetic algorithm on some globally used benchmarks and it is... 

Data prefetching, i.e., the act of predicting an application's future memory accesses and fetching those that are not in the on-chip caches, is a well-known and widely used approach to hide the long latency of memory accesses. The fruitfulness of data prefetching is evident to both industry and academy: Nowadays, almost every high-performance processor incorporates a few data prefetchers for capturing various access patterns of applications; besides, there is a myriad of proposals for data prefetching in the research literature, where each proposal enhances the efficiency of prefetching in a specific way. In this survey, we evaluate the effectiveness of data prefetching in the context of... 

Applications extensively use data objects with a regular and fixed layout, which leads to the recurrence of access patterns over memory regions. Spatial data prefetching techniques exploit this phenomenon to prefetch future memory references and hide the long latency of DRAM accesses. While state-of-the-art spatial data prefetchers are effective at reducing the number of data misses, we observe that there is still significant room for improvement. To select an access pattern for prefetching, existing spatial prefetchers associate observed access patterns to either a short event with a high probability of recurrence or a long event with a low probability of recurrence. Consequently, the... 

L1 instruction(L1-l) cache misses are a source of performance bottleneck. While many instruction prefetchers have been proposed, most of them leave a considerable potential uncovered. In 2011, Proactive Instruction Fetch (PIF) showed that a hardware prefetcher could effectively eliminate all instruction-cache misses. However, its enormous storage cost makes it impractical. Consequently, reducing the storage cost was the main research focus in instruction prefetching in the past decade. Several instruction prefetchers, including RDIP and Shotgun, were proposed to offer PIF-level performance with significantly lower storage overhead. However, our findings show that there is a considerable... 

We introduced several styles of data prefetching in the past three chapters. The introduced data prefetchers were known for a long time, sometimes for decades. In this chapter, we introduce several state-of-the-art data prefetchers, which have been introduced in the past few years. In particular, we introduce DOMINO, BINGO, MLOP, and RUNAHEAD METADATA. © 2022 Elsevier Inc  

We introduced several data prefetchers and qualitatively discussed their strengths and weaknesses. Without quantitative evaluation, the true strengths and weaknesses of a data prefetcher are still vague. To shed light on the strengths and weaknesses of the introduced data prefetchers and to enable the readers to better understand these prefetchers, in this chapter, we quantitatively compare and contrast them. © 2022 Elsevier Inc  

Recent research revisits pairwise-correlating data prefetching due to its extremely low overhead. Pairwise-correlating data prefetching, however, cannot accurately detect where data streams end. As a result, pairwise-correlating data prefetchers either expose low accuracy or they lose timeliness when they are performing multi-degree prefetching. In this letter, we propose a novel technique to detect where data streams end and hence, control the multi-degree prefetching in the context of pairwise-correlated prefetchers. The key idea is to have a separate metadata table that operates one step ahead of the main metadata table. This way, the runahead metadata table harnesses the degree of... 

This paper presents an efficient way of implementing Taylor expansion Diagrams (TED) that is called Binary Taylor Diagrams (BTD). BTD is based on Taylor series like TED, but uses a binary data structure. So BTD functions are simpler than those of TED. © 2005 IEEE  

In the present work, adsorption of hydrogen molecules over a metal organic framework (MOF-5) has been investigated by using first principles density functional theory (DFT). Different strategies have been applied for improving hydrogen storage, i.e. metal doping, boron substitution and functionalization. The metal atoms used for enhancing hydrogen adsorption include Li, Ca and Sc. It is found that the binding energy between these metal atoms and MOF is not enough to prevent clustering. Therefore a number of carbon atoms are substituted by boron atoms and it is indicated that boron substitution enhances the binding energies, significantly. Also the results reveal that boron substituted MOF... 

In the present work the interaction of hydrogen molecules with perfect, defective and scandium doped polycyclic aromatic hydrocarbon (PAH) has been evaluated. At first the potential barrier for the penetration of hydrogen molecules through PAH structures has been investigated and then the adsorption of hydrogen molecules over PAH structures has been studied. To model the graphene surface for barrier calculations, it is shown that coronene can successfully estimate the graphene monolayer. The barrier height is calculated for perfect and two different defective PAH structures including Stone-Wales (SW) and 585. It is found that PAH even with small defects is impermeable to hydrogen molecules.... 

In the present work, the interaction of hydrogen molecules with defective graphene structures doped by transition metal (TM) atoms is investigated by using first principles density functional theory (DFT). Defective graphene structures include Stone-Wales (SW), 585 and 555-777 and transition metals include early TMs, i.e. scandium (Sc), titanium (Ti) and vanadium (V). It is found that in comparison with the pristine graphene, presence of defects significantly enhances the metal binding. Among three defects, 585 divacancy leads to the strongest binding between graphene and metal. Hydrogen adsorption is then evaluated by sequential addition of hydrogen molecules to the system. The results... 

Hydrogen appears as one of the most promising energy carriers. Hydrogen has the advantage of being the cleanest fuel and as a result it can efficiently help limiting the greenhouse effect. However one of the major barriers for the large scale use of hydrogen, especially in the transportation sector, is hydrogen storage issue. There are four different technologies for storing hydrogen which are as follows: (1) Cryogenic liquid hydrogen (LH); (2) compressed gas storage; (3) metal hydride storage technology; (4) Physisorption methods. Physisorption of hydrogen in novel carbon nanostructures such as carbon nanotubes and graphene seems to be a safe and promising method for storage of hydrogen....