In this blog post, we’ll explore the basic principles of pipelining, how it increases the processing rate in a central processing unit (CPU), and the actual process of performance improvement along with its limitations.
What Is a Pipeline?
The device that ultimately processes instructions in a computer is the Central Processing Unit (CPU). One of the key metrics for evaluating CPU performance is the number of instructions it can process per unit of time—that is, throughput. All other things being equal, the higher the throughput, the better the CPU’s performance. A pipeline is a representative CPU design technique used to increase this throughput.
A CPU that does not use a pipeline reads instructions from memory one at a time and processes them sequentially. During this process, the various components that make up the CPU do not all operate simultaneously while a single instruction is being processed. For example, during the stage of reading an instruction from memory, the Arithmetic Logic Unit (ALU) is idle and waiting; conversely, while performing arithmetic operations, the component responsible for reading instructions remains idle. In other words, during the processing of a single instruction, there are moments at specific points in time when certain hardware components are not being used. Pipelining is a technology that minimizes this idle time as much as possible to utilize CPU resources efficiently.
How Pipelining Works and Examples
In pipelining, a single instruction is divided into multiple processing stages, and each stage is configured to be executed simultaneously by different hardware components. Furthermore, the execution times of each stage are designed to be as similar as possible to improve overall processing efficiency.
For example, let’s assume that the process of executing a single instruction is divided into five stages. A single instruction progresses sequentially from Stage 1 to Stage 5, with each stage handled by a different circuit. If the total processing time is divided evenly, each stage takes only about 1/5 of the original processing time.
Now, let’s consider this in terms of the clock. To help illustrate this, let’s assume that in a conventional CPU, processing a single instruction requires one clock cycle, and that the clock cycle is 10 nanoseconds. In that case, it takes 10 nanoseconds to complete a single instruction. If this is divided evenly into five stages, each stage takes about 2 nanoseconds, so the clock cycle can be set to 2 nanoseconds. In this scenario, a single instruction requires five clock cycles to complete, but at each clock cycle, different instructions are processed simultaneously at different stages.
For example, during the first clock cycle, Instruction 1 executes Stage 1 (A). During the second clock cycle, Instruction 1 executes Stage 2 (B) while Instruction 2 executes Stage 1 (A). During the third clock cycle, Instruction 1 executes Stage 3 (C), Instruction 2 executes Stage 2 (B), and Instruction 3 executes Stage 1 (A), and so on. Once all stages are filled, multiple instructions are processed simultaneously at different stages within the pipeline, and in an ideal environment, one instruction is completed per clock cycle.
Theoretically, this can achieve a throughput approximately five times higher than before; however, this assumes an ideal situation where the processing time for all stages is identical and there are no interfering factors.
In actual CPUs, it is difficult to divide all instructions into equal time intervals, and the workload for each stage is not completely uniform. Furthermore, various pipeline hazards can occur, such as data hazards between instructions, control hazards caused by branch instructions, and structural hazards due to memory access conflicts.
While modern CPUs utilize technologies such as branch prediction, out-of-order execution, and data forwarding to mitigate these issues, actual performance improvements often fall short of the theoretical maximum.
When examining the operation of a pipeline over time, each instruction sequentially passes through five stages—A, B, C, D, and E—with the processing of the previous and next instructions overlapping. Initially, a preparation period is required to fill the pipeline, and at the end, the pipeline is drained to complete all remaining instructions. Excluding these initial “fill” and final “drain” phases, multiple instructions are processed simultaneously for most of the time, allowing for a high throughput. This is the core of pipeline technology.
The pipeline technique is very similar to a production line in manufacturing. There are multiple processes involved in completing a single product, and different workers perform tasks simultaneously at each process. Once a product has completed the previous process, it moves on to the next one, while the previous process begins work on a new product. In a CPU, a single instruction corresponds to a single product, and each processing stage plays a role similar to a work process on a production line. This analogy provides an intuitive understanding of why pipelining can significantly increase a CPU’s throughput.
