COMPUTER ARCHICTURE AND ORGINATION Parallel Processing, Array Processors, The Structure of General Purpose Multiple Processors, Symmetric, Multiprocessors, Multithreading and Chip Multiprocessors, Clusters, Multicore Organization, Memory Organization in Multiprocessors. Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards.

 

COMPUTER ARCHICTURE AND ORGINATION 

UNIT 6 PARALLEL ORGINNATION AND PIPLING

COVERED TOPICES:- Parallel Processing, Array Processors, The Structure of General Purpose Multiple Processors, Symmetric, Multiprocessors, Multithreading and Chip Multiprocessors, Clusters, Multicore Organization, Memory Organization in Multiprocessors. Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards.

ASKED QUESTION 

Q1. General Organization for Multicore System

In a multicore system, multiple processor cores are integrated onto a single chip or die. These cores can operate independently and execute instructions concurrently, leading to improved performance and efficiency compared to single-core processors. The general organization of a multicore system involves:

• Integration of multiple processor cores onto a single chip.
• Shared resources such as cache, memory, and I/O interfaces.
• Interconnection network for communication between cores and shared resources.
• Cache coherence mechanisms to maintain consistency between private caches.
• Synchronization mechanisms for coordinating concurrent execution of threads or processes.
• Operating system support for scheduling and resource management.

Q2. Advantages of SMP Compared with Uniprocessor

Symmetric Multiprocessing (SMP) offers several advantages over uniprocessor systems, including:

• Increased processing power and performance due to multiple processors working in parallel.
• Better utilization of system resources by distributing tasks across multiple processors.
• Improved scalability, allowing additional processors to be added to the system to handle increasing workloads.
• Enhanced fault tolerance and reliability through redundancy and load balancing.
• Support for multitasking and multi-user environments, enabling concurrent execution of multiple applications or processes.

Q3. Design Considerations for Multiprocessor Systems

Some of the key design considerations for multiprocessor systems include:

• Scalability: The system should be able to accommodate a varying number of processors to support different workloads.
• Cache Coherence: Mechanisms must be in place to ensure consistency between the private caches of each processor and shared memory.
• Interconnection Network: An efficient communication infrastructure is essential for facilitating data transfer and synchronization between processors.
• Synchronization: Techniques for coordinating concurrent access to shared resources and preventing race conditions.
• Fault Tolerance: Measures to detect and recover from hardware or software failures to ensure system reliability.
• Power Efficiency: Strategies for minimizing power consumption while maximizing performance in multiprocessor systems.

Q4. Cluster Computer Architecture

Cluster computer architecture involves interconnected groups of computers or nodes that work together to perform a common task. Each node in a cluster typically has its own processor, memory, and storage, and communication between nodes is facilitated through a high-speed network. Cluster architectures can be classified into various types, such as homogeneous clusters with identical nodes or heterogeneous clusters with diverse hardware configurations. Clusters are widely used in distributed computing environments for tasks such as scientific simulations, data analysis, and high-performance computing.

Q5. SIMD vs. MIMD

SIMD (Single Instruction, Multiple Data) and MIMD (Multiple Instruction, Multiple Data) are two parallel processing architectures:

• SIMD: In SIMD architecture, multiple processing elements execute the same instruction simultaneously on different data elements. It is suitable for tasks involving data parallelism, such as vector operations and multimedia processing.
• MIMD: In MIMD architecture, multiple processing elements execute different instructions on different data independently. It allows for more flexibility and is suitable for tasks with task-level parallelism, such as independent program executions or distributed computing.

Q6. Types of Parallel Processor Systems

Parallel processor systems can be classified into different types based on their architecture and organization, including:

• Flynn's Taxonomy: Classified into Single Instruction, Single Data (SISD), Single Instruction, Multiple Data (SIMD), Multiple Instruction, Single Data (MISD), and Multiple Instruction, Multiple Data (MIMD).
• Vector Processors: Designed to perform operations on vectors or arrays of data elements simultaneously.
• Array Processors: Specialized processors optimized for parallel processing of arrays or matrices.
• Massively Parallel Processors: Systems with a large number of processing elements working in parallel, often used for scientific simulations and data-intensive tasks.

Q7. Approaches of Multithreading

Multithreading involves executing multiple threads of execution concurrently within the same process. There are several approaches to multithreading:

• Kernel-Level Threading: Managed entirely by the operating system kernel, providing full control over thread management but may incur higher overhead.
• User-Level Threading: Managed by user-level libraries or runtime environments, offering lower overhead but limited control over thread scheduling and management.
• Hybrid Threading: Combines both kernel-level and user-level threading to leverage the benefits of both approaches.
• Many-to-One Threading Model: Maps multiple user-level threads to a single kernel-level thread, providing simplicity but limiting parallelism.
• One-to-One Threading Model: Maps each user-level thread to a separate kernel-level thread, offering better parallelism and scalability but may incur higher overhead.

Q8. Characteristics of Symmetric Multiprocessor (SMP)

Symmetric Multiprocessor (SMP) systems have several key characteristics:

• Shared Memory: All processors have equal access to a shared memory space, allowing them to communicate and share data efficiently.
• Uniform Memory Access (UMA): Each processor can access any location in memory with uniform latency, simplifying memory management and access patterns.
• Cache Coherence: Mechanisms are in place to ensure consistency between the private caches of each processor and shared memory, preventing data inconsistencies.
• Scalability: SMP systems can scale up by adding additional processors to the system to handle increasing workloads, providing flexibility and performance scalability.
• Operating System Support: SMP systems require specialized operating system support for managing processor scheduling, resource allocation, and synchronization.

Computer Architecture and Organization

1. Parallel Processing

Parallel processing involves the simultaneous execution of multiple instructions or processes to solve a computational problem. It aims to improve performance by dividing tasks into smaller sub-tasks that can be processed concurrently.

In parallel processing, tasks are broken down into smaller units, which are then distributed across multiple processors or processing cores. Each processor or core works on its assigned task simultaneously with others. This approach can significantly reduce the overall execution time of tasks, leading to faster results.

2. Array Processors

Array processors are specialized parallel computing devices designed to perform computations on arrays or matrices efficiently. They consist of multiple processing elements (PEs) operating in parallel to process different elements of an array simultaneously.

These processors excel at tasks involving repetitive operations on large datasets, such as mathematical calculations or image processing. Each processing element in the array processor is responsible for handling a portion of the input data, allowing for high throughput and efficient utilization of computational resources.

3. The Structure of General-Purpose Multiple Processors

General-purpose multiple processors are systems that contain multiple processing units or cores capable of executing independent instructions concurrently. These processors can be structured in various configurations, such as symmetric multiprocessing (SMP), where all processors have equal access to memory and I/O devices.

In an SMP system, multiple processors share access to a common memory and I/O devices, enabling them to work together on a wide range of tasks. Each processor operates independently, but they can communicate with each other through shared memory, enabling efficient coordination and collaboration.

4. Symmetric Multiprocessors

Symmetric multiprocessing (SMP) is a type of multiprocessing architecture where multiple identical processors share access to a common memory and I/O devices. In SMP systems, each processor performs similar tasks independently, and communication between processors is facilitated through shared memory.

SMP systems are commonly used in servers, workstations, and high-performance computing environments where parallelism is crucial for achieving optimal performance. They offer scalability and flexibility, allowing additional processors to be added to the system to handle increasing workloads.

5. Multithreading and Chip Multiprocessors

Multithreading is a technique that allows multiple threads of execution to share the resources of a single processor core. Chip multiprocessors (CMPs) integrate multiple processor cores onto a single chip, enabling simultaneous execution of multiple threads and increasing overall system performance.

CMPs leverage multithreading to maximize resource utilization and improve throughput. By allowing multiple threads to execute concurrently on each core, CMPs can better exploit parallelism and handle a larger number of tasks simultaneously, resulting in improved performance and responsiveness.

6. Clusters

Clusters are interconnected groups of computers or nodes that work together to perform a common task. Each node in a cluster may have its own processor and memory, and communication between nodes is typically facilitated through a high-speed network.

Clusters are often used in distributed computing environments to tackle large-scale computational problems that require significant processing power and memory resources. By distributing tasks across multiple nodes, clusters can achieve higher levels of performance, fault tolerance, and scalability compared to individual systems.

7. Multicore Organization

Multicore organization refers to the arrangement of multiple processor cores on a single integrated circuit (IC) or chip. These cores can operate independently and execute instructions concurrently, leading to improved performance and efficiency compared to single-core processors.

Multicore processors are commonly found in modern computing devices, including smartphones, tablets, laptops, and desktop computers. By integrating multiple cores onto a single chip, multicore processors can execute multiple tasks simultaneously, enabling smoother multitasking and faster response times for applications.

8. Memory Organization in Multiprocessors

Memory organization in multiprocessor systems involves managing access to shared memory resources among multiple processors. Various techniques, such as cache coherence protocols and memory consistency models, are employed to ensure data consistency and coherence across multiple processor cores.

Cache coherence protocols are used to maintain consistency between the private caches of each processor and the shared main memory. These protocols ensure that all processors have a consistent view of memory, preventing data inconsistencies and race conditions that can occur in multiprocessor systems.

Pipelining: Basic Concepts

Pipelining is a technique used in computer architecture to improve processor throughput by breaking down the execution of instructions into multiple stages. Each stage of the pipeline performs a specific task, and multiple instructions can be processed simultaneously, overlapping their execution.

The pipeline consists of several stages, including instruction fetch, instruction decode, execution, memory access, and write-back. By dividing the instruction execution process into smaller stages, pipelining can increase the overall throughput of the processor and improve performance.

Throughput and Speedup

Throughput refers to the rate at which a system can process tasks or instructions. Speedup measures the improvement in performance achieved by parallel processing compared to sequential processing. It is calculated as the ratio of the execution time of a task in a sequential system to the execution time of the same task in a parallel system.

Parallel processing can significantly increase system throughput and speedup by enabling multiple tasks to be processed simultaneously. However, achieving optimal speedup requires careful consideration of factors such as task parallelism, load balancing, and communication overhead.

Pipeline Hazards

Pipeline hazards are situations that can occur in pipelined processors, causing delays or incorrect execution of instructions. There are three types of pipeline hazards:

1. Structural hazards occur when multiple instructions require access to the same hardware resource simultaneously.
2. Data hazards arise when there is a dependency between instructions that may result in incorrect data being used or produced.
3. Control hazards occur when the pipeline must be stalled or flushed due to conditional branches or other control flow instructions.

These hazards can impact pipeline performance and efficiency, leading to reduced throughput and increased latency. Techniques such as forwarding, speculation, and branch prediction are used to mitigate pipeline hazards and improve overall processor performance.