Logical time - Distributed systems : Notes

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Logical time 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.

Engineering

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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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A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_. https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are: - Vertex following and Minimum label - Data caching - Graph coloring - Threshold scaling With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well. From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.

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1. Webpages tend to behave as authorities or hubs. 2. An authority represents an research thesis, and a hub represents an encyclopedia. 3. Each page has an authority and a hub score. 4. The graph is based on query, included pointed to and from pages. 5. Authority score is the sum of scores of all hubs pointing to it. 6. Hub score is the sum of scores of all authorities is pointing to. 7. Score are normalized with L2-norm in each iteration (root of sum of squares). 8. Needs to be performed at query time. 9. Two scores are returned, instead of just one. https://gist.github.com/wolfram77/3d9ef6c5a5b63f53caabce4812c7ea81

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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

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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.

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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

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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

Seizure stage detection of epileptic seizure using convolutional neural networks

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Explore the revolutionary concept of microkernel architecture in operating systems with this comprehensive presentation. From its inception to its contemporary applications, this PowerPoint provides a deep dive into the essence and significance of microkernels. Covering the following key aspects: Understanding Microkernel Architecture: Delve into the core principles of microkernel design, distinguishing it from monolithic kernels. Learn how microkernels streamline the operating system by delegating essential functions to user-space processes. Advantages of Microkernels: Uncover the myriad benefits of microkernel architecture, including increased system stability, modularity, and flexibility. Explore how microkernels facilitate easier maintenance, extensibility, and customization of operating systems. Challenges and Trade-offs: Navigate through the challenges and trade-offs associated with microkernel design, such as performance overhead and complexity. Gain insights into strategies for mitigating these challenges and optimizing microkernel-based systems. Real-world Implementations: Explore notable examples of operating systems built on microkernel architecture, including GNU Hurd, MINIX, and QNX. Understand how these systems leverage microkernels to achieve specific goals in terms of reliability, security, and real-time performance. Whether you're a budding operating system enthusiast or a seasoned developer exploring alternative system architectures, this PowerPoint presentation offers a comprehensive overview of microkernel technology. Download now and embark on a journey to reimagine the future of operating systems with microkernels.

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Logical time - Distributed systems : Notes

1. LOG1CAL TIME ScAnr ime logrent kme 8 te captsre nugntty oh e t s n diktrikuHad ve cker hime Systens, phgsitnt tne à net aeunate cneuyh metrtc fime necesy ber Knetolesdge e usnlt dlstr w fed ntg erethm dgh vmsS atvnss in matml enciuzkn algvithms mintain steney tn pitafod olsfabharcs Af sesgh terrec dielieck oefettbn agrt auephanten and ondetecttd dadiecks yacking 6 epeeent evovts VSu ming eehecLN bA, *n fure v ad ih detection of ledncoirgistenc cs in dtof natie erk peveitie ning Kmacledga aeot pege5 ducerdi ng eelerc nbevmation 0ore otlectitn atren etetecon Cencorren cywdegure t cleck C:H T mede n cieck censictency cenditen ce)CCei) o&consertent cCe) C)

2. datagt rueturs pretecel Ce represtnt tgt ome) Cupdnte data to ensorc ee ngisfen ondn.) Tecal eleck un proJ pdathng fetat cleck go bal tiecklecat vie or s 2pdatins i e n ced SCALAR TIME La m lecal clocK & local v ob globnt me nrg ECC+a R2 Ct max CCe,Cmag on ntorna, sond 3 Geoute RI deliver mág ten ruieve foteilordtring n possta evont ceun n mung pstea fomesiampt : * t,t) , j d strong cnsStency v avents tuih Sam d0alar tome ert dholepndtont tie-bvanin9 chaniSm vA a

3. JECTOR TIME Fodgt attrn Jthmutn bimt BC Ce) CeCe] t d R C max CCi, Cmag) en internel, 4tnd tere.cute Ri delivor Lon recieves Ce tn CiCtn C&C Yn GiEns Cgtn CC G&C and. n CeC Ctn Ci 1Somovphm uun unE &sntafinps STOn corsASTC re ss t ralud evcnt cout & cand memeg. saah gad Ariespants, anaarsmy thatpnts

4. LFEICIENT IMPLEMENTATIONS OF VECIOR OCKS SINGHAL KSHEMKA LYANII DIEFERENTIAL TECHNIQUE osc vothion: bctuoeen fucLeasive sendt to pmepeprocess, ony e cnv vEctor clock cat SUnlcr Proces charg eg LS: lost sont LU. Los upclof value oral ctock tohen Pe tst Scnt vafueeglecalcleK whan P cpcoteol masng te P vEAp¢ctV entr o t ClecK CPc) dom cn be Send tn em o tuplal instcad of ful vecro. MATRIX TIME Cabl Cirid P lacal Clec ( C -vuerdleck FCttc] maCCE1, Cm i,.ten tntnel, Aondy Ci max CCr, Cag) x t t e Ri a. deliver ms .disrand ebsolete inhor mation C,Caul>t e prece4 knous thnt

5. PHYStCAL CLec &YNCHRONIZATION MTP n e n eb tine, intervaL, and relative enslering o evots d e ** tect tes, penvedi'e clock nchitation easn i ton Ca.Ct) 6quen y Ca C) m Cleck ow macy t a tohrth clech pregreue o6sat aGt) - t Sk Ca' Ct) - tome dibb. cwrt rtal me att li6 to prject clock drita'e) attaterahen a cleck vogress UTC t tuur K mi prcfel CHTP) Do itmet NTP involves a hievvehrmre.e * crversHert, poas A,& *h inylu en 'a lenHy calcakatc deng. & H6set uging a Srk bidirachie na' naage nn.66et e wre ) eundrp AnidÁnt he Pair twith min.& Ischesen. (T,T)-(TT

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