Introduction to Parallel and Distributed Computing

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Questions and Answers

What is a major challenge in developing parallel and distributed applications?

Simplicity in programming

High network bandwidth

Synchronization and communication complexities (correct)

Low system availability

Which issue is specifically related to maintaining data consistency across distributed systems?

Interoperability

Network latency

Fault tolerance

Data management and integrity (correct)

What do security concerns in distributed environments primarily focus on?

Improving network bandwidth

Unauthorized access and data breaches (correct)

Managing node failures

Synchronization issues

Which of the following is a key factor affecting application performance in wide-area distributed systems?

Network latency and bandwidth limitations Signup and view all the answers

What is one of the goals of ongoing research in parallel and distributed computing?

Enhance computational algorithms and system architectures Signup and view all the answers

What is a primary benefit of using parallel computing in data analytics?

It enables efficient analysis of large datasets. Signup and view all the answers

Which open-source project is commonly associated with parallel and distributed application management?

Apache Hadoop Signup and view all the answers

In which field is parallel computing NOT commonly applied?

Social media marketing Signup and view all the answers

What role do standardization bodies like IEEE and ISO play in distributed computing?

They define standards to ensure interoperability and security. Signup and view all the answers

What is one major challenge faced in parallel and distributed computing?

Managing synchronization and communication overhead. Signup and view all the answers

Which of the following is a use case of distributed computing?

Genetic data analysis across multiple machines. Signup and view all the answers

What is a benefit of using distributed databases like MongoDB and Cassandra?

Scalability and data availability Signup and view all the answers

Which distributed computing service is known for providing resources over the internet?

Google Cloud Platform Signup and view all the answers

What is the purpose of load balancing in parallel and distributed computing?

To distribute tasks evenly among processors or nodes. Signup and view all the answers

What feature distinguishes cloud computing from traditional computing methods?

The provision of distributed computing resources over the internet. Signup and view all the answers

Which aspect can be improved by using distributed databases?

Fault tolerance and scalability Signup and view all the answers

What was a key feature that made personal computing more user-friendly during the desktop era?

Graphical User Interface (GUI) Signup and view all the answers

Which development is considered a significant milestone of the network era?

Netscape Navigator Signup and view all the answers

What does parallel computing offer compared to serial computing?

Higher speed and efficiency Signup and view all the answers

During which era did computing begin to focus on interconnected networks and the internet?

Network Era Signup and view all the answers

How did the introduction of cloud computing change the delivery of computing services?

It enabled scalable services over the internet. Signup and view all the answers

What limitation is associated with serial computing?

Performance limited by a single processor's speed Signup and view all the answers

What technology played a crucial role in making personal computers affordable during the desktop era?

Microprocessors Signup and view all the answers

Which of the following best describes client-server architecture used in the network era?

Clients interact with servers, enabling distributed computing. Signup and view all the answers

What is the main objective of reliable client-server communication?

To ensure messages are delivered correctly and in order Signup and view all the answers

How does reliable group communication differ from reliable client-server communication?

It ensures messages are delivered to multiple clients concurrently Signup and view all the answers

What is the main purpose of distributed commit protocols?

To coordinate changes across multiple distributed components Signup and view all the answers

What role do recovery mechanisms serve in a distributed system?

They restore the system to a consistent state after failures Signup and view all the answers

Which of the following is NOT a benefit of load balancing?

Increased Implementation Complexity Signup and view all the answers

What is a key characteristic of load balancing in distributed computing?

It aims to evenly distribute workloads Signup and view all the answers

Which of the following best describes the effect of effective load balancing?

High resource utilization and reduced response times Signup and view all the answers

What prevents overloading of any single resource in load balancing?

Even distribution of computational tasks Signup and view all the answers

What is the main characteristic of static mapping in load balancing?

It involves pre-determined assignment of tasks to resources. Signup and view all the answers

Which of the following is NOT a scheme for static mapping?

Load-based Signup and view all the answers

What distinguishes dynamic mapping from static mapping in load balancing?

Dynamic mapping adapts to real-time changes in system conditions. Signup and view all the answers

Which of the following is an example of a mechanism used in concurrency control?

Optimistic concurrency control Signup and view all the answers

How does timestamp ordering work in concurrency control?

Conflicts are resolved by comparing transaction timestamps. Signup and view all the answers

Which statement is true about the optimistic concurrency control approach?

It operates under the assumption that conflicts are rare. Signup and view all the answers

What is the primary purpose of locking in concurrency control?

To prevent other transactions from accessing data concurrently. Signup and view all the answers

Which of the following describes feedback-based mapping in dynamic load balancing?

It continuously monitors system performance for task adjustments. Signup and view all the answers

What is a characteristic of thread-based concurrency?

Threads share the same memory space but have their own execution context. Signup and view all the answers

Which of the following best describes the principle of locality?

Programs tend to access a small subset of data frequently and nearby memory locations. Signup and view all the answers

What factor significantly impacts system performance related to memory?

Memory latency and bandwidth. Signup and view all the answers

Which statement about cache memory is accurate?

Cache memory reduces the effective memory latency. Signup and view all the answers

In what scenario are memory bandwidth limitations most impactful?

Data-intensive workloads such as scientific simulations. Signup and view all the answers

What is one effect of using multiple levels of cache memory?

It can further reduce effective memory latency. Signup and view all the answers

What type of applications struggle to utilize computational resources due to memory constraints?

Memory-bound applications. Signup and view all the answers

What describes the relationship between memory latency and performance?

Lower memory latency contributes to better system performance. Signup and view all the answers

Study Notes

Parallel Computing

Parallel computing involves many calculations simultaneously.
Large problems are broken down into smaller ones.
Key characteristics include multiple processors, concurrency, and performance improvement.
Parallel computing can be implemented at different levels, from low-level hardware circuits to high-level algorithms.

Distributed Computing

Multiple computers work together over a network.
Each computer performs part of the overall task.
Results are combined to form the final output.
Key features include geographically dispersed systems, autonomy, and resource sharing.
Distributed computing improves fault tolerance, scalability, and resource utilization.
Examples include cloud computing, grid computing, and peer-to-peer networks.

History of Computing: Key Eras

Batch Processing Era (1950s-1960s): Characterized by submitting jobs (programs and data) on punch cards for operators to process sequentially. Mainframes were expensive, so high utilization was crucial.
- Important systems include IBM 701 (1952) and IBM 1401 (1959).
Time-Sharing Era (1960s-1970s): Introduced interactive computing, allowing multiple users to concurrently access the computer. Multiple users share CPU time via terminals.
- Compatible Time-Sharing System (CTSS, 1961) and Multics (1965) were significant systems.
Desktop Era (1980s-1990s): Personal computers (PCs) became affordable and accessible. Graphical user interfaces (GUIs) improved user-friendliness.
Network Era (1990s-Present): Focuses on interconnected computing and the internet.

Parallel Computing Principles

Decomposition: Breaking down a problem into smaller tasks for concurrent execution (Task Decomposition) and splitting the data into chunks (Data Decomposition) for parallel processing.
Concurrency: Performing multiple tasks simultaneously for increased computational speed.
Communication: Mechanisms for processors to exchange information.
Synchronization: Techniques to coordinate parallel tasks to ensure correct execution. This includes locks, semaphores, and barriers.
Scalability: Ability of a parallel system to efficiently utilize increasing numbers of processors.
Load Balancing: Even distribution of work across processors to avoid bottlenecks.
Fault Tolerance: System's ability to continue working even if some components fail.
Granularity: Size of tasks in a decomposed problem (fine-grained parallelism for smaller, frequent communication tasks; coarse-grained parallelism for larger, less frequent communication tasks).

Parallel vs Serial Computing

Serial Computing: Tasks executed sequentially. Single processor. Performance limited by single processor speed
Parallel Computing: Tasks executed simultaneously. Multiple processors. Greater speed and efficiency. Increased Complexity.

Applications of Parallel and Distributed Computing

Scientific Simulations: Weather forecasting, climate modeling, molecular dynamics.
Data Analytics: Big data processing, machine learning, artificial intelligence.
Engineering: Computer-aided design (CAD), finite element analysis.
Financial Modeling: Risk analysis, option pricing, portfolio optimization.
Computer Graphics/Rendering: Visual effects in movies, realistic images.
Genomics & Bioinformatics: Analyzing genetic data, sequencing genomes.
Real-time Processing: Weather forecasting, financial modeling.

Distributed Computing Concepts

Decentralized Architecture: Multiple nodes, not a central machine, handle tasks.
Resource Sharing: Nodes share resources like processing power, memory, and data storage to improve efficiency.
Autonomy: Individual nodes operate independently without a central controller.
Concurrency: Tasks can run concurrently across multiple nodes.
Fault Tolerance: System can continue operating if nodes fail.
Scalability: Ability to handle increasing workloads by adding more nodes.
Examples: Cloud computing, peer-to-peer networks. Distributed databases

Issues in Parallel and Distributed Computing

Synchronization and Communication Overhead: Coordinating parallel tasks and communication between nodes.
Scalability Challenges: Systems ability to handle increasing loads and resources without sacrificing performance or reliability.
Fault Tolerance and Reliability: Dealing with node/network failures.
Complexity of Programming Models: Developing/debugging parallel applications, synchronization and communication management
Data Management and Consistency: Ensuring data consistency and integrity across distributed systems.
Security Concerns: Threats such as unauthorized access, breaches, and denial-of-service.
Scalability of Distributed Databases: Manage distributed databases large scale with consistent and reliable data accessibility.
Network Latency and Bandwidth Limitations: Delays and bandwidth issues in wide-area distributed systems.
Interoperability and Compatibility: Ensuring compatibility across different hardware and software platforms.

Load Balancing

Even Distribution of work across multiple processors or nodes in a system to ensure efficient resource utilization; prevents one node getting overloaded.
Improves Performance & Scalability and increases fault tolerance / resilience

Concurrency Control

Mechanisms in database management systems to ensure transactions execute concurrently without interfering with each other.
Common approaches include locking, timestamp ordering, and optimistic concurrency control.

Memory Hierarchies

Memory Levels: Registers, cache, main memory (RAM), and secondary storage (disk) have different speeds and capacities.
Locality Principle: Programs tend to access nearby data frequently (temporal and spatial locality.)
Memory Latency/Bandwidth: Time and rate data is accessed impacting system performance.
Cache Memory: Fast memory between processor/main memory reducing time to access instructions/data.

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Description

Explore the fundamentals of parallel and distributed computing in this quiz. Learn about key characteristics, historical eras, and the differences between these two computing paradigms. Enhance your understanding of how these systems optimize performance and resource utilization.