Distributed Mutual exclusion algorithms - Distributed systems

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Distributed Mutual exclusion algorithms Some notes on Distributed Systems Written while studying the course Distributed Systems at IIIT Hyderabad, by professor Kishore Kothapalli. The book used is Distributed Computing: Principles, Algorithms, and Systems by Ajay D. Kshemkalyani and Mukesh Singhal.

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My notes on shared memory parallelism. Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].

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**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include: - Random walk methods - Spectral partitioning - Label propagation - Greedy agglomerative and divisive algorithms - Clique percolation https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6 There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*. **Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community. Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity. Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**. The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.

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In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later. https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a

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HITS algorithm : NOTES

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Computer architectures are facing issues: Memory latencies are far higher. Benefits from instruction level parallelism (ILP) is reducing. With increasing clock rates, power consumption is increasing. Increasing complexity with multi-stage pipelines, intermediate buffers, multi-level caches, out-of-order execution, branch prediction, ... GPUs are parallel computer architectures that are good at some tasks, not so good at others. Running routines with high arithmetic intensity with overlapped memory access is the preferred approach. They may be unsuitable for irregular algorithms, where it is difficult to get high efficiency due to the high latency of accesses. They are less versatile compared to CPUs, using SIMD parallelism, and are dense compute-wise (per currency). NVIDIA's CUDA programming model enables GPUs to be used for general-purpose computing, and hence the term GPGPU. GPU Architectural, Programming, and Performance Models presentation at PPoPP, 2010, Bangalore, India. By Prof. Kishore Kothapalli with Prof. P. J. Narayanan and Suryakant Patidar. https://gist.github.com/wolfram77/43a6660121eef45b78c10d4e652dad6c

Basic Computer Architecture and the Case for GPUs : NOTES

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For the IPDPS ParSocial event a presentation submission is required by 15th May. The event is on 3rd June. https://gist.github.com/wolfram77/51b15ca09eb28f6909673a2deb1a314d DYNAMIC BATCH PARALLEL ALGORITHMS FOR UPDATING PAGERANK Subhajit Sahut, Kishore Kothapallit and Dip Sankar Banerjeet tInternational Institute of Information Technology Hyderabad, India. tIndian Institute of Technology Jodhpur, India. subhajit.sahu@research. ,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16. FACEBOOK 15 TAKING A PAGE OUT OF GOOGLE’S PLAYBOOK 10 STOP FAKE NEWS FROM GOING VIRAL PUBLISHED APR 2015 BY SALVADOR RODRIGUEZ Click-Gap: When is Facebook is driving disproportionate amounts of traffic to websites. Effort to rid fakes news from Facebook’s services. Is a website relying on Facebook to drive significant traffic, but not well ranked by the rest of the web? Also News Citation Graph. PAGERANK APPLICATIONS Ranking of websites. Measuring scientific impact of researchers. Finding the best teams and athletes. Ranking companies by talent concentration. Predicting road/foot traffic in urban spaces. Analysing protein networks. Finding the most authoritative news sources Identifying parts of brain that change jointly. Toxic waste management. PAGERANK APPLICATIONS Debugging complex software systems (Moni torRank) Finding the most original writers (BookRank) Finding topical authorities (TwitterRank) WHAT IS PAGERANK l—-d Plu = Cus + —— UCIiNny Pru u->v = (1-—d) x “us ( ) outdegy, PageRank is a lLink-analysis algorithm. By Larry Page and Sergey Brin in 1996. For ordering information on the web. Represented with a random-surfer model. Rank of a page is defined recursively. Calculate iteratively with power-iteration.

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Satellites are usually covered in aluminized polyimide. The yellowish gold color of polyimide with silver aluminium side facing in gives the satellite the appearance of being wrapped in gold. The material is called Multi-layer Insulation (MLI). It helps in radiative insulation of the onboard instruments of satellite. Gold is actually used in electrical contacts to prevent corrosion due to Ultra-violet light or X-rays. https://gist.github.com/wolfram77/8ae2de1a29caf1a2f84babed79943389

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This tutorial discusses on tax calculation for long-term and short-term capital gains, as well as for business income that speculative (intraday, BTST) and non-speculative (F&O, BTST). Also a nice concept on tax-loss harvesting. When turnover for business income is more than 5Cr or gains less than 6% an audit is required by a CA. Business income requires maintaining balance sheet and income statement. I may need an audit by a CA.

Taxation for Traders < Markets and Taxation : NOTES

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This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?). --- Abstract— PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network. Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming

A Generalization of the PageRank Algorithm : NOTES

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With biomedical signal processing algorithms, such as the Pan-Tompkins QRS peak detection algorithm, FIR filters are utilized. Raw ECG signal can be fed to a Moving window filter, which helps filter out noise and get the signal of interest. These FIR filters involve the use of multipliers and adders, which take in several input sample and output a single sample. This paper replaces accurate adders in such filters with 10 16-bit signed approximate adders (power ve error parameters) from the EvoApprox library. Functional validation is done in MATLAB with Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of Moving Window Integration; and Mean Square Error (MSE) of thresholds as error metrics. MIT-BIH Arrythmia database is used as the raw ECG input. In hardware evaluation, a 100-point FIR filter is implemented with a single Multiply-accumulate unit where the exact adder is replaced with selected approximate adder. RTL model is synthesized with 45nm NandGate Open Cell library in Synopsys design compiler. Area, Average power, and Worst-case delay are measured. On average the presented methodology provides an area-saving of 19.71% and power-saving of 19.27%.

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Distributed Mutual exclusion algorithms - Distributed systems

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