The document provides information about the 8086 and 80386 microprocessors. It discusses the 8086's 16-bit architecture and its 14 registers including the accumulator, base, count, and data registers. It also describes the 8086's addressing modes. For the 80386, it discusses its 32-bit architecture with 32-bit general purpose registers and segment registers. It outlines the 80386's register organization, flag register, descriptor registers, addressing modes including scaled indexed mode, control registers, and debug registers.
8086 Microprocessor is an enhanced version of 8085 Microprocessor that was designed by Intel in 1976. It is a 16-bit Microprocessor having 20 address lines and 16 data lines that provides up to 1MB storage. In April 1978, intel introduced this microprocessor and it was officially released on June 8.
A introduction to 8086 microprocessor
Your Crash course in your Pocket! Microprocessors: 8085, 8086, 80386, Pentium⌠Microcontrollers: 8051, ARM⌠Computer Organisation & Architecture
what is 8086 microprocessor, and its features
But we couldnât pass up celebrating the 8086's anniversary in style. Time for a little DIY action to turn it into a work of art!
On this channel you can get education and knowledge for general issues and topics
8086 Microprocessor is an enhanced version of 8085 Microprocessor that was designed by Intel in 1976. It is a 16-bit Microprocessor having 20 address lines and 16 data lines that provides up to 1MB storage. In April 1978, intel introduced this microprocessor and it was officially released on June 8.
A introduction to 8086 microprocessor
Your Crash course in your Pocket! Microprocessors: 8085, 8086, 80386, Pentium⌠Microcontrollers: 8051, ARM⌠Computer Organisation & Architecture
what is 8086 microprocessor, and its features
But we couldnât pass up celebrating the 8086's anniversary in style. Time for a little DIY action to turn it into a work of art!
On this channel you can get education and knowledge for general issues and topics
computer organizaton and architecture
topic- microprocessors, segment registers
this ppt gives brief discription about microprocessors topic in computer organization and architecture
A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together. Some microprocessors in the 20th century required several chips. Microprocessors help to do everything from controlling elevators to searching the Web. Everything a computer does is described by instructions of computer programs, and microprocessors carry out these instructions many millions of times a second. [1]
Microprocessors were invented in the 1970s for use in embedded systems. The majority are still used that way, in such things as mobile phones, cars, military weapons, and home appliances. Some microprocessors are microcontrollers, so small and inexpensive that they are used to control very simple products like flashlights and greeting cards that play music when you open them. A few especially powerful microprocessors are used in personal computers.
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...Subhajit Sahu
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TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clockâs actual time for some time during the callâs execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are âtime mastersâ in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
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Abstract â Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting Bitset for graph : SHORT REPORT / NOTESSubhajit Sahu
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Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
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Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Experiments with Primitive operations : SHORT REPORT / NOTESSubhajit Sahu
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This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
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For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...Subhajit Sahu
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Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTESSubhajit Sahu
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https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
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].
A Dynamic Algorithm for Local Community Detection in Graphs : NOTESSubhajit Sahu
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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.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTESSubhajit Sahu
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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_.
Application Areas of Community Detection: A Review : NOTESSubhajit Sahu
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This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329
Survey for extra-child-process package : NOTESSubhajit Sahu
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Useful additions to inbuilt child_process module.
đŚ Node.js, đ Files, đ° Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTERSubhajit Sahu
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For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/692d263f463fd49be6eb5aa65dd4d0f9
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...Subhajit Sahu
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For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024
Fast Incremental Community Detection on Dynamic Graphs : NOTESSubhajit Sahu
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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
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
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Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navyâs DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATOâs (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
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Building better applications for business users with SAP Fiori.
⢠What is SAP Fiori and why it matters to you
⢠How a better user experience drives measurable business benefits
⢠How to get started with SAP Fiori today
⢠How SAP Fiori elements accelerates application development
⢠How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
⢠How SAP Fiori paves the way for using AI in SAP apps
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Are you looking to streamline your workflows and boost your projectsâ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youâre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part âEssentials of Automationâ series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereâs what youâll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
Weâll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Donât miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. Whatâs changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more âmechanicalâ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Leading Change strategies and insights for effective change management pdf 1.pdf
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110 ec0644
1. By
MD Nabil Shahriar(110EC0644)
Under Professor S K Patra
Department of electronics & communication Engineering
National Institute of technology Rourkela
Note: This document holds contents from various sources including internet sources & referred text
books. It doesnât hold any copyright claim.
2. 8086 Microprocessor:
In 1976, when Intel began designing the 8086 processor, which was the first 16 bit
microprocessor. Memory was very expensive. Personal computers at the time, typically
had four thousand bytes of memory. Even when IBM introduced the PC five years later,
64K was still quite a bit of memory; one megabyte was a tremendous amount. Intelâs
designers felt that 64K memory would remain a large amount throughout the lifetime of
the 8086. The only mistake they made was completely underestimating the lifetime of
the8086. They figured it would last about five years, like their earlier 8080 processor.
People were running up against the one megabyte limit
of 8086 . So Intel gave us the 80386 in 1985. This processor could address up to maximum
16 megabytes ofmemory.
Register Organization:
⢠The 8086 has four groups of the user accessible internal registers. They are
the instruction pointer, four data registers, four pointer and index register,
four segment registers.
⢠The 8086 has a total of fourteen 16-bit registers including a 16 bit register
called the status register, with 9 of bits implemented for status and control
flags.
⢠Most of the registers contain data offsets within 64 KB memory segment.
There are four different 64 KB segments for instructions, stack, data and extra
data. To specify where in 1 MB of processor memory these 4 segments are
located the processor uses four segment registers:
3. ⢠Code segment (CS) is a 16-bit register containing address of 64 KB segment
with processor instructions. The processor uses CS segment for all accesses to
instructions referenced by instruction pointer (IP) register. CS register cannot
be changed directly. The CS register is automatically updated during far jump,
far call and far return instructions.
⢠Stack segment (SS) is a 16-bit register containing address of 64KB segment
with program stack. By default, the processor assumes that all data referenced
by the stack pointer (SP) and base pointer (BP) registers is located in the stack
segment. SS register can be changed directly using POP instruction.
⢠Data segment (DS) is a 16-bit register containing address of 64KB segment
with program data. By default, the processor assumes that all data referenced
by general registers (AX, BX, CX, and DX) and index register (SI, DI) is located in
the data segment. DS register can be changed directly using POP and LDS
instructions.
⢠Extra segment (ES) is a 16-bit register containing address of 64KB segment,
usually with program data. By default, the processor assumes that the DI
register references the ES segment in string manipulation instructions. ES
register can be changed directly using POP and LES instructions.
⢠It is possible to change default segments used by general and index registers
by prefixing instructions with a CS, SS, DS or ES prefix.
⢠All general registers of the 8086 microprocessor can be used for arithmetic
and logic operations. The general registers are:
⢠Accumulator register consists of two 8-bit registers AL and AH, which can be
combined together and used as a 16- bit register AX. AL in this case contains
the low-order byte of the word, and AH contains the high-order byte.
Accumulator can be used for I/O operations and string manipulation.
⢠Base register consists of two 8-bit registers BL and BH, which can be
combined together and used as a 16-bit register BX. BL in this case contains the
low-order byte of the word, and BH contains the high-order byte. BX register
Usually contains a data pointer used for based, based indexed or register
indirect addressing.
4. ⢠Count register consists of two 8-bit registers CL and CH, which can be
combined together and used as a 16-bit register CX. When combined, CL
register contains the low-order byte of the word, and CH contains the high
order byte. Count register can be used in Loop, shift/rotate instructions and as
a counter in string manipulation,.
⢠Data register consists of two 8-bit registers DL and DH, which can be
combined together and used as a 16-bit register DX. When combined, DL
register contains the low-order byte of the word, and DH contains the high
order byte. Data register can be used as a port number in I/O operations. In
integer 32-bit multiply and divide instruction the DX register contains high
order word of the initial or resulting number.
⢠The following registers are both general and index registers:
⢠Stack Pointer (SP) is a 16-bit register pointing to program stack.
⢠Base Pointer (BP) is a 16-bit register pointing to data in stack segment. BP
register is usually used for based, based indexed or register indirect
addressing.
⢠Source Index (SI) is a 16-bit register. SI is used for indexed, based indexed
and register indirect addressing, as well as a source data address in string
manipulation instructions.
⢠Destination Index (DI) is a 16-bit register. DI is used for indexed, based
indexed and register indirect addressing, as well as a destination data address
in string manipulation instructions.
Other registers:
⢠Instruction Pointer (IP) is a 16-bit register.
⢠Flags is a 16-bit register containing 9 one bit flags.
⢠Overflow Flag (OF) - set if the result is too large positive number, or is too
small negative number to fit into destination operand.
5. ⢠Direction Flag (DF) - if set then string manipulation instructions will auto-
decrement index registers. If cleared then the index registers will be auto-
incremented.
⢠Interrupt-enable Flag (IF) - setting this bit enables makeable interrupts.
⢠Single-step Flag (TF) - if set then single-step interrupt will occur after the
next instruction.
⢠Sign Flag (SF) - set if the most significant bit of the result is set.
⢠Zero Flag (ZF) - set if the result is zero.
⢠Auxiliary carry Flag (AF) - set if there was a carry from or borrow to bits 0-3
in the AL register.
⢠Parity Flag (PF) - set if parity (the number of "1" bits) in the low-order byte of
the result is even.
⢠Carry Flag (CF) - set if there was a carry from or borrows to the most
significant bit during last result calculation.
Addressing Modes:
⢠Implied - the data value/data address is implicitly associated with the
instruction.
⢠Register - references the data in a register or in a register pair.
⢠Immediate - the data is provided in the instruction.
⢠Direct - the instruction operand specifies the memory address where data is
located.
⢠Register indirect - instruction specifies a register containing an address,
where data is located. This addressing mode works with SI, DI, BX and BP
registers.
6. ⢠Based: - 8-bit or 16-bit instruction operand is added to the contents of a base
register (BX or BP), the resulting value is a pointer to location where data
resides.
⢠Indexed: - 8-bit or 16-bit instruction operand is added to the contents of an
index register (SI or DI), the resulting value is a pointer to location where data
resides.
⢠Based Indexed: - the contents of a base register (BX or BP) is added to the
contents of an index register (SI or DI), the resulting value is a pointer to
location where data resides.
⢠Based Indexed with displacement: - 8-bit or 16-bit instruction operand is
added to the contents of a base register (BX or BP) and index register (SI or DI),
the resulting value is a pointer to location where data resides.
7. 80386 Microprocessor:
The Intel 80386, also known as the i386, or just 386, was a 32-
bit microprocessor introduced by Intel in 1985. The first versions had 275,000
transistors and were used as the central processing unit (CPU) of
many workstations and high-end personal computers of the time. As the
original implementation of the 32-bit extension of the 8086 architecture, the
80386 instruction set, programming model, and binary encodings are still
the common denominator for all 32-bit x86 processors
Register Organisation
⢠The 80386 has eight 32 - bit general purpose registers which may be used as
either 8 bit or 16 bit registers.
⢠A 32 - bit register known as an extended register, is represented by the
register name with prefix E.
⢠Example: A 32 bit register corresponding to AX is EAX, similarly BX is EBX etc.
⢠The 16 bit registers BP, SP; SI and DI in 8086 are now available with their
extended size of 32 bit and are names as EBP, ESP, ESI and EDI.
⢠AX represents the lower 16 bit of the 32 bit register EAX.
8. ⢠BP, SP, SI, DI represents the lower 16 bit of their 32 bit Counterparts, and can
be used as independent 16 bit registers.
⢠The six segment registers available in 80386 are CS, SS, DS, ES, FS and GS.
⢠The CS and SS are the code and the stack segment registers respectively,
while DS, ES, FS, GS are 4 data segment registers.
⢠A 16 bit instruction pointer IP is available along with 32 bit counterpart EIP.
⢠Flag Register of 80386: The Flag register of 80386 is a 32 bit register. Out of
the 32 bits, Intel has reserved bits D18 to D31, D5 and D3, while D1 is always
set at 1.Two extra new flags are added to the 80286 flag to derive the flag
register of 80386. They are VM and RF flags.
⢠VM - Virtual Mode Flag: If this flag is set, the 80386 enters the virtual 8086
mode within the protection mode. This is to be set only when the 80386 is in
protected mode. In this mode, if any privileged instruction is executed an
exception 13 is generated. This bit can be set using IRET instruction or any
task switch operation only in the protected mode.
⢠RF- Resume Flag: This flag is used with the debug register breakpoints. It is
checked at the starting of every instruction cycle and if it is set, any debug fault
is ignored during the instruction cycle. The RF is automatically reset after
Successful execution of every instruction, except for IRET and POPF
instructions.
⢠Also, it is not automatically cleared after the successful execution of JMP,
CALL and INT instruction causing a task switch. These instructions are used to
set the RF to the value specified by the memory data available at the stack.
⢠Segment Descriptor Registers: This registers are not available for
programmers, rather they are internally used to store the descriptor
information, like attributes, limit and base addresses of segments.
⢠The six segment registers have corresponding six 73 bit descriptor registers.
Each of them contains 32 bit base address, 32 bit base limit and 9 bit
attributes. These are automatically loaded when the corresponding segments
are loaded with selectors.
9. ⢠Scaled Indexed Mode: Contents of the index register are multiplied by a
scale factor that may be added further to get the operand offset.
⢠Control Registers: The 80386 has three 32 bit control registers CR), CR2 and
CR3 to hold global machine status independent of the executed task. Load and
store instructions are available to access these registers.
⢠System Address Registers: Four special registers are defined to refer to the
descriptor tables supported by 80386.
⢠The 80386 supports four types of descriptor table, viz. global descriptor table
(GDT), interrupt descriptor table (IDT), local descriptor table (LDT) and task
state segment descriptor (TSS).
⢠Debug and Test Registers: Intel has provided a set of 8 debug registers for
hardware debugging. Out of these eight registers DR0 to DR7, two registers
DR4 and DR5 are Intel reserved.
⢠The initial four registers DR0 to DR3 store four program controllable
breakpoint addresses, while DR6 and DR7 respectively hold breakpoint status
and breakpoint control information.
⢠Two more test register are provided by 80386 for page cacheing namely test
control and test status register.
⢠Addressing Modes:
The 80386 supports overall eleven addressing modes to facilitate efficient
execution of higher level language programs.
⢠In case of all those modes, the 80386 can now have 32-bit immediate or 32-
bit register operands or displacements.
⢠The 80386 has a family of scaled modes. In case of scaled modes, any of the
index register values can be multiplied by a valid scale factor to obtain the
displacement.
10. ⢠The valid scale factor are 1, 2, 4 and 8.
⢠The different scaled modes are as follows.
⢠Based Scaled Indexed Mode: Contents of the index register are multiplied by
a scale factor and then added to base register to obtain the offset.
⢠Based Scaled Indexed Mode with Displacement: The Contents of the index
register are multiplied by a scaling factor and the result is added to a base
register and displacement to get the offset of an operand.