Computer Architecture: Pipelining Concepts

Parallel processing is a technique that involves executing multiple tasks or operations simultaneously on a computer system to achieve faster processing speeds.

The primary goal of parallel processing is to increase the processing speed of a computer system by allowing it to handle more operations concurrently.

Parallel processing can be classified into different categories based on how instructions and data are handled, such as Single Instruction Single Data (SISD), Single Instruction Multiple Data (SIMD), Multiple Instruction Single Data (MISD), and Multiple Instruction Multiple Data (MIMD).

In SISD, a single processor executes instructions sequentially on a single data stream. This is the traditional way computers operate.

SIMD systems use a single instruction to operate on multiple data points simultaneously, allowing for parallel computations on a single processor.

MISD, a less common type, involves multiple instructions working on a single data stream, often used in special applications.

In MIMD, multiple processors execute different instructions on different data sets concurrently, providing significant parallel processing capabilities.

A single processor executes a single instruction on a single piece of data at a time. Think of it as a 'one at a time' approach.

A single instruction is executed on multiple pieces of data simultaneously. Imagine a team working on different parts of a project at the same time.

Multiple processors perform different instructions on the same set of data simultaneously. Think of it as multiple cooks using different recipes on the same ingredients.

A technique where instructions are broken down into smaller steps and processed in a pipeline, allowing for faster execution. Think of a conveyor belt where each step in the pipeline performs a specific task.

Multiple functional units or pipelines can be used to achieve parallel processing, allowing for faster execution of instructions. Think of having multiple lanes on a highway, each handling a different task.

A type of parallel processing where instructions are processed in a sequential manner, but using multiple processors to speed up execution. Think of a team working on a project sequentially but dividing the workload among several people.

A type of parallel processing where instructions are processed in parallel using multiple processors. Think of a team working on a project simultaneously, each member working on a different part of the task.

A type of processor architecture where a single instruction is executed on a single piece of data, but the instruction is broken down into smaller steps and processed in a pipeline, which speeds up execution.

A balanced pipeline has all stages taking the same amount of time. This results in maximum efficiency as all stages are always utilized.

The average time needed to execute a task in a pipelined system is reduced by a factor equal to the number of stages in the pipeline.

The speedup achieved by pipelining is directly proportional to the number of stages in the pipeline.

The average time per instruction in a pipelined system is the time per instruction of the unpipelined system divided by the number of stages.

A system with multiple processors executing independent instructions on different data sets. Each processor can work on its own task, making it ideal for multitasking. Imagine a team of chefs preparing different complex dishes simultaneously, each with their own recipe and ingredients.

A task scheduling technique where tasks are executed one after another, finishing one task before starting the next. Imagine a queue where people wait their turn to use a resource.

A type of parallel processing where multiple processors are connected and work together to execute a single task. This is often used for high-performance computing and data analysis.

A task is broken down into a series of steps (stages) that must be completed in a specific order. For example, washing clothes requires washing, drying, and folding.

Some stages of a task depend on the completion of previous stages. You can't dry clothes before washing them.

The slowest stage in a task determines how long it takes to complete all tasks in a series. The slowest stage will hold up the entire process.

In pipelined processing, a new task can start before the previous task is completely finished. This helps to reduce the overall time it takes to complete all the tasks.

Pipelined processing doesn't make individual tasks faster. Each task still takes the same amount of time to complete. However, overall processing time is reduced because tasks overlap.

Even though each individual task in a series might take the same amount of time, pipelined processing reduces the average execution time for all tasks by overlapping them.

The amount of time it takes to complete a task (such as laundry) remains the same. Pipelined processing doesn't magically make the task itself faster. Instead, it optimizes the overall process by allowing tasks to start and end earlier.

A pipeline is a technique where instructions are broken down into smaller steps and processed in stages, similar to a conveyor belt where each stage performs a specific task.

In a pipeline, each stage processes a part of the instruction, and the stages operate concurrently, allowing for faster execution.

The time required for the first task to complete in a k-segment pipeline is ktp, where tp is the clock cycle time.

After the first task is completed, the pipeline can process one task per clock cycle, so subsequent tasks take (n-1)tp time.

The speedup of a pipeline over a non-pipelined process is calculated as the ratio of the time taken by the non-pipelined process to the time taken by the pipelined process.

When the number of tasks is large, the speedup of a pipeline can approach the number of segments in the pipeline (k).

The theoretical maximum speedup of a pipeline is determined by the number of segments (k) in the pipeline.

A k-segment pipeline can theoretically achieve a maximum speedup of k. This means that a pipeline with 4 segments could potentially achieve a speedup of 4 times compared to a non-pipelined process.