This document discusses high performance computing and Flynn's taxonomy of computer architectures. It describes MIMD architectures including shared memory SMP systems and distributed memory clusters. SMP systems have multiple similar processors that share main memory and I/O. Clusters are groups of interconnected computers that function as a single system. The document compares SMP and cluster architectures.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
Conventional architectures coarsely comprise of a processor, memory system, and the datapath.
Each of these components present significant performance bottlenecks.
Parallelism addresses each of these components in significant ways.
Different applications utilize different aspects of parallelism - e.g., data itensive applications utilize high aggregate throughput, server applications utilize high aggregate network bandwidth, and scientific applications typically utilize high processing and memory system performance.
It is important to understand each of these performance bottlenecks.
Coherence and consistency models in multiprocessor architectureUniversity of Pisa
Cache coherence and consistency model in multiprocessor architecture. These slide show the introduction of multiprocessor and cache multilevel and then describe the basic mechanism of coherence and consistency protocols. In particular the protocols describe are the following: snooping and directory protocols for the coherence part and sequential protocol for the consistency part. There are also example of (in)consistency and (in)coherence.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
Conventional architectures coarsely comprise of a processor, memory system, and the datapath.
Each of these components present significant performance bottlenecks.
Parallelism addresses each of these components in significant ways.
Different applications utilize different aspects of parallelism - e.g., data itensive applications utilize high aggregate throughput, server applications utilize high aggregate network bandwidth, and scientific applications typically utilize high processing and memory system performance.
It is important to understand each of these performance bottlenecks.
Coherence and consistency models in multiprocessor architectureUniversity of Pisa
Cache coherence and consistency model in multiprocessor architecture. These slide show the introduction of multiprocessor and cache multilevel and then describe the basic mechanism of coherence and consistency protocols. In particular the protocols describe are the following: snooping and directory protocols for the coherence part and sequential protocol for the consistency part. There are also example of (in)consistency and (in)coherence.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
The motherboard serves to connect all of the parts of a computer together. The CPU, memory, hard drives, optical drives, video card, sound card and other ports and expansion cards all connect to the motherboard directly or via cables.
The motherboard is the piece of computer hardware that can be thought of as the "back bone" of the PC.......
"PC support definition" And I like it a lot.
MODULE III Parallel Processors and Memory Organization 15 Hours
Parallel Processors: Introduction to parallel processors, Concurrent access to memory and cache
coherency. Introduction to multicore architecture. Memory system design: semiconductor memory
technologies, memory organization. Memory interleaving, concept of hierarchical memory
organization, cache memory, cache size vs. block size, mapping functions, replacement
algorithms, write policies.
Case Study: Instruction sets of some common CPUs - Design of a simple hypothetical CPU- A
sequential Y86-64 design-Sun Ultra SPARC II pipeline structure
G.Sumithra,II-M.sc(computer Science),Bon Secours college for women,Thanjavur.SumithraG2
Terminologies and its types
In-Memory Analytics
In-Database processing
Symmetric Multiprocessor system(SMP)
Massively Parallel Processing
Difference Between Parallel and Distributed Systems
Shared Nothing Architecture
Advantages of a “ shared nothing Architecture”
CAP Theorem Explained
CAP Theorem
G.Sumithra,II-M.sc(computer science),Bon secours college for women,thanjavur.SumithraG2
Terminologies and its types
In-Memory Analytics
In-Database processing
Symmetric Multiprocessor system(SMP)
Massively Parallel Processing
Difference Between Parallel and Distributed Systems
Shared Nothing Architecture
Advantages of a “ shared nothing Architecture”
CAP Theorem Explained
CAP Theorem
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- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
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- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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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.
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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
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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:
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- 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.
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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.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
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Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
2. Recap
• Pipelining
• Super Scalar Execution
– Dependencies
• Branch
• Data
• Resource
• Effect of Latency and Memory
• Effect of Parallelism
3. Flynn’s taxanomy
It distinguishes multiprocessor computers according
to data and instruction
• the dimensions of Instruction and Data
• SISD: Single Instruction Single data (Uniprocessor)
• SIMD: Single Instruction Multiple data (Vector
Processing)
• MISD: Multiple Instruction Single date
• MIMD: Multiple Instruction Multiple data (SMP,
cluster, NUMA)
12. Processor Design: Modes of
Parallelism
• Two ways to increase parallelism
– Superscaling
• Instruction level parallelism
– Threading
• Thread level parallelism
– Concept of Multithreaded processors
» May or may not be different than OS level mult-threading
• Temporal Multi-threading (also called implicit)
– Instructions from only one thread
• Simultaneous Multi-threading (explicit)
– Instructions from more than one thread can be executed
13. Scalar Processor Approaches
• Single-threaded scalar
– Simple pipeline
– No multithreading
• Interleaved multithreaded scalar
– Easiest multithreading to implement
– Switch threads at each clock cycle
– Pipeline stages kept close to fully occupied
– Hardware needs to switch thread context between cycles
• Blocked multithreaded scalar
– Thread executed until latency event occurs
– Would stop pipeline
– Processor switches to another thread
14. Clusters
• Alternative to SMP
• High performance
• High availability
• Server applications
• A group of interconnected whole computers
• Working together as unified resource
• Illusion of being one machine
• Each computer called a node
15. Cluster Benefits
• Absolute scalability
• Incremental scalability
• High availability
• Superior price/performance
17. Cluster v. SMP
• Both provide multiprocessor support to high demand
applications.
• Both available commercially
– SMP for longer
• SMP:
– Easier to manage and control
– Closer to single processor systems
• Scheduling is main difference
• Less physical space
• Lower power consumption
• Clustering:
– Superior incremental & absolute scalability
– Superior availability
• Redundancy