This document contains 29 questions about parallel computing and processor organizations. The questions cover important topics like models of parallel computing including hypercube networks, mesh networks, and butterfly networks; shuffle exchange networks; cube connected cycle networks; terms related to parallelization like speedup and efficiency; task graphs and Gantt charts; differences between binary k-cubes and cube-connected networks; embeddings of graphs like trees and meshes into processor topologies like hypercubes and meshes; dynamic load balancing algorithms; deterministic and non-deterministic models; and the Coffman-Graham scheduling algorithm.
DAOC: Stable Clustering of Large NetworksArtem Lutov
Presentation slides for "DAOC: Stable Clustering of Large Networks". Presented at IEEE BigData 2019, Special Session on Intelligent Data Mining.
The clustering algorithm is designed to build human perception-adapted taxonomies of large networks (i.e., graphs) without any manual tuning.
DAOC: Stable Clustering of Large NetworksArtem Lutov
Presentation slides for "DAOC: Stable Clustering of Large Networks". Presented at IEEE BigData 2019, Special Session on Intelligent Data Mining.
The clustering algorithm is designed to build human perception-adapted taxonomies of large networks (i.e., graphs) without any manual tuning.
These are also called as Grid or Mesh network. Lattice networks are simply networks, where nodes are arranged in a rectangular lattice, aimed to overcome the major drawback of the ER model. Copy the link given below and paste it in new browser window to get more information on Lattice Network:- http://www.transtutors.com/homework-help/networks-systems/two-port-network/lattice-network/
Haqr the hierarchical ant based qos aware on demand routing for manetscsandit
A Mobile Ad Hoc Network (MANET) is a collection of wireless mobile devices with no pre
existing infrastructure or centralized control. Supporting QoS during routing is a very
challenging task. Clustering is an effective method for resource management regarding network
performance, routing protocol design, QoS etc. In real time various types of nodes with different
computing and transmission power, different rolls and different mobility pattern may exist.
Hierarchical routing provides routing through this kind of heterogeneous nodes. In this paper,
HAQR, a novel ant based QoS aware routing is proposed on a three level hierarchical cluster
based topology in MANET which will be more scalable and efficient compared to flat
architecture and will give better throughput.
ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets.
Network-on-Chip (NoC) is a new approach for designing the communication subsystem among IP cores in a System-on-Chip (SoC). NoC applies networking theory and related methods to on-chip communication and brings out notable improvements over conventional bus and crossbar interconnections. NoC offers a great improvement over the issues like scalability, productivity, power efficiency and signal integrity challenges of complex SoC design. In an NoC, the communication among different nodes is achieved by routing packets through a pre-designed network fabric according to some routing algorithm. Therefore, architecture and related routing algorithm play an important role to the improvement of overall performance of an NoC. A Diametrical 2D Mesh routing architecture has the facility of having some additional diagonal links with simple 2D Mesh architecture. In this work, we have proposed a Modified Extended 2D routing algorithm for this architecture, which will ensure that a packet always reaches the destination through the possible shortest path, and the path is always deadlock free.
MPWide: A light-weight communication library for wide area message passing an...Derek Groen
MPWide is a light-weight communication library for wide area message passing and code coupling. We originally developed MPWide to efficiently exchange data between supercomputers, allowing us to run a cosmological simulation in parallel across multiple machines in different countries. However, we also used MPWide for other purposes, such as coupling the 3D HemeLB bloodflow solver. Overall MPWide allows users to obtain excellent performance, incurring as little as 7% overhead for the cosmological simulations and 1% overhead in the multiscale bloodflow simulations. We manage to obtain this performance despite the presence of background traffic and, in some cases, non-optimized network configurations.
I gave this presentation during the MAPPER Seasonal School in Barcelona in 2013. For more information, please feel free to contact me on Twitter or refer to my webpage.
Interconnection Network
in this presentation there are some explain to Interconnection Network , and espically in computer architecture and parallel processing.
These are also called as Grid or Mesh network. Lattice networks are simply networks, where nodes are arranged in a rectangular lattice, aimed to overcome the major drawback of the ER model. Copy the link given below and paste it in new browser window to get more information on Lattice Network:- http://www.transtutors.com/homework-help/networks-systems/two-port-network/lattice-network/
Haqr the hierarchical ant based qos aware on demand routing for manetscsandit
A Mobile Ad Hoc Network (MANET) is a collection of wireless mobile devices with no pre
existing infrastructure or centralized control. Supporting QoS during routing is a very
challenging task. Clustering is an effective method for resource management regarding network
performance, routing protocol design, QoS etc. In real time various types of nodes with different
computing and transmission power, different rolls and different mobility pattern may exist.
Hierarchical routing provides routing through this kind of heterogeneous nodes. In this paper,
HAQR, a novel ant based QoS aware routing is proposed on a three level hierarchical cluster
based topology in MANET which will be more scalable and efficient compared to flat
architecture and will give better throughput.
ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets.
Network-on-Chip (NoC) is a new approach for designing the communication subsystem among IP cores in a System-on-Chip (SoC). NoC applies networking theory and related methods to on-chip communication and brings out notable improvements over conventional bus and crossbar interconnections. NoC offers a great improvement over the issues like scalability, productivity, power efficiency and signal integrity challenges of complex SoC design. In an NoC, the communication among different nodes is achieved by routing packets through a pre-designed network fabric according to some routing algorithm. Therefore, architecture and related routing algorithm play an important role to the improvement of overall performance of an NoC. A Diametrical 2D Mesh routing architecture has the facility of having some additional diagonal links with simple 2D Mesh architecture. In this work, we have proposed a Modified Extended 2D routing algorithm for this architecture, which will ensure that a packet always reaches the destination through the possible shortest path, and the path is always deadlock free.
MPWide: A light-weight communication library for wide area message passing an...Derek Groen
MPWide is a light-weight communication library for wide area message passing and code coupling. We originally developed MPWide to efficiently exchange data between supercomputers, allowing us to run a cosmological simulation in parallel across multiple machines in different countries. However, we also used MPWide for other purposes, such as coupling the 3D HemeLB bloodflow solver. Overall MPWide allows users to obtain excellent performance, incurring as little as 7% overhead for the cosmological simulations and 1% overhead in the multiscale bloodflow simulations. We manage to obtain this performance despite the presence of background traffic and, in some cases, non-optimized network configurations.
I gave this presentation during the MAPPER Seasonal School in Barcelona in 2013. For more information, please feel free to contact me on Twitter or refer to my webpage.
Interconnection Network
in this presentation there are some explain to Interconnection Network , and espically in computer architecture and parallel processing.
Literature Review: Convey the Data in Massive Parallel ComputingAM Publications,India
In this paper we have studied several works on direct network architectures which are well-built contestant for useful in many successful cost-effective, experimental massive parallel computers and well scale up shared memory of multiprocessors. The uniqueness of direct networks, as reflected by the communication latency and routing latency metrics are significant to the performance of such systems. A multiprocessor system can be used for the wormhole routing for the most capable switching method and has been adopted in several new massive parallel computers. This technique is unique technical challenges in routing and flow control in particular system, and avoid deadlock. The highly scale up network is a combination of topology and hypercube. Due to the being of concurrent multiple mesh and hypercubes, this network provides a great architectural support for parallel processing. The growth of the network is more efficient in terms of communication, interconnection network is scaled up the network and will be more reliable and also the unreliability of the interconnection network to get minimized. This is very desirable characteristic for the interconnection network as the network remains equipped for more failure of adjoining nodes or links in parallel computer architecture. Formulations to optimize the performance of throughput of networks through queuing theory M\M\1 concept.
Hidden geometric correlations in real multiplex networksKolja Kleineberg
Read the paper at http://www.nature.com/nphys/journal/vaop/ncurrent/full/nphys3812.html
Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the layers. We find that these correlations are significant in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers. They also enable accurate trans-layer link prediction, meaning that connections in one layer can be predicted by observing the hidden geometric space of another layer. And they allow efficient targeted navigation in the multilayer system using only local knowledge, outperforming navigation in the single layers only if the geometric correlations are sufficiently strong.
HAQR: THE HIERARCHICAL ANT BASED QOS AWARE ON-DEMAND ROUTING FOR MANETScscpconf
A Mobile Ad Hoc Network (MANET) is a collection of wireless mobile devices with no pre existing infrastructure or centralized control. Supporting QoS during routing is a very challenging task. Clustering is an effective method for resource management regarding network performance, routing protocol design, QoS etc. In real time various types of nodes with different computing and transmission power, different rolls and different mobility pattern may exist.Hierarchical routing provides routing through this kind of heterogeneous nodes. In this paper,HAQR, a novel ant based QoS aware routing is proposed on a three level hierarchical clusterbased topology in MANET which will be more scalable and efficient compared to flat architecture and will give better throughput.
Presentation describes about process planning procedures & software project management methods employed in Infosys with reference from Textbook "Software Project management in practice by Pankaj Jalote"
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re 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 “Essentials of Automation” 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.
Here’s what you’ll 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.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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 ‘mechanical’ 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!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Designing Great Products: The Power of Design and Leadership by Chief Designe...
Qb pc ii
1. Question Bank: Parallel computing Faculty: Ayaz Ahmed Shariff K
Modules covered: Processor organizations, Mapping and Scheduling
1. What are three important models of parallel computing? Explain each one of them?
2. Explain the need of networks in parallel processing. Discuss about the following networks.
(a) Hyper tree networks (b) Hypercube Networks
(c) Mesh networks (d) Pyramid networks
(e) Butterfly networks (f) Shuffle exchange networks
3. Explain shuffle exchange network with 8 nodes?
4. Discuss about 24 cube connected cycle network?
5. Define the terms diameter, Bisection width, Number of edges per node, Max edge length,
speedup, parallelizability, efficiency?
6. Define the terms task graph, gantt chart, optimal schedule, simple task graphs, chain,
necklace, perfect shuffle, multistage network, Omega network, Dilation?
7. What is the difference between a binary k-cube and a cube-connected network of degree k?
8. Given a shuffle-exchange network, prove that if a shuffle link connects nodes i and j, then j is
a single-bit left cyclic rotation of i? Prove that the number of necklaces in an n-node shuffle
exchange network is O(2k / k)??
9. Show how to perform the perfect shuffle network’s exchange operation on a de-bruijn
network?
10. Is it possible for the average speedup exhibited by a parallel algorithm to be superlinear?
11. Embed a complete binary tree with 31 nodes in a 2D mesh, or prove no such embedding
exists?
12. With a block diagram, explain processor arrays?
13. Explain types of Multiprocessors with examples?
14. Write a block diagram of the sequent symmetry UMA multiprocessor?
15. How cache consistency is maintained in UMA Multiprocessors?
16. How processors communicate in multicomputers? Briefly discuss some popular
multicomputers?
17. Discuss the Flynn’s taxonomy for classifying for serial and parallel computer architectures
with simple block diagrams?
18. How to embed a complete binary tree of height 3 into a 2 D Mesh? Is it possible to embed a
tree greater than height 4 in a 2 D Mesh without increasing the dilation beyond 1?
19. Illustrate with example, embedding of binomial tree into 2 D mesh?
20. Discuss embedding of different graphs into hypercube processor organization with some
examples?
21. Prove that a dilation-1 embedding of complete binary tree of height n into hypercube of
dimension n+1 does not exist if n>1?
22. Prove that a binomial tree of height n can be embedded in a hypercube of dimension n such
that dilation is 1?
23. What are properties of encoding to embed rings and meshes into hypercube?
24. What is gray code? Does gray code provide solution to embed a mesh onto hypercube?
Explain the mapping of 4 X 8 mesh into a 32-node hypercube?
2. 25. Explain mapping of an 8 node ring into an 8-processor hypercube?
26. What are different dynamic load balancing algorithms on multicomputers?
27. What are deterministic models and non-deterministic models?
28. Illustrate Coffman-Graham scheduling algorithm with an example? Does Coffman graham
scheduling algorithm produce an optimal schedule?
29. What is task graph? Explain the importance of embedding one graph into another?