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Cycles per instruction
Aspect of CPU performance
Aspect of CPU performance
In computer architecture, cycles per instruction (aka clock cycles per instruction, clocks per instruction, or CPI) is one aspect of a processor's performance: the average number of clock cycles per instruction for a program or program fragment. It is the multiplicative inverse of instructions per cycle.
Definition
The average of Cycles Per Instruction in a given process (CPI) is defined by the following weighted average:
: \mathrm{CPI} := \frac{\Sigma_i(\mathrm{IC}_i)(\mathrm{CC}_i)}{\mathrm{IC}} = \frac{\Sigma_i(\mathrm{IC}_i \cdot \mathrm{CC}_i)}{\Sigma_i(\mathrm{IC}_i)}
Where \mathrm{IC}_i is the number of instructions for a given instruction type i, \mathrm{CC}_i is the clock-cycles for that instruction type and \mathrm{IC}=\Sigma_i(\mathrm{IC}_i) is the total instruction count. The summation sums over all instruction types for a given benchmarking process.
Explanation
Let us assume a classic RISC pipeline, with the following five stages:
- Instruction fetch cycle (IF).
- Instruction decode/Register fetch cycle (ID).
- Execution/Effective address cycle (EX).
- Memory access (MEM).
- Write-back cycle (WB).
Each stage requires one clock cycle and an instruction passes through the stages sequentially. Without pipelining, in a multi-cycle processor, a new instruction is fetched in stage 1 only after the previous instruction finishes at stage 5, therefore the number of clock cycles it takes to execute an instruction is five (CPI = 5 1). In this case, the processor is said to be subscalar. With pipelining, a new instruction is fetched every clock cycle by exploiting instruction-level parallelism, therefore, since one could theoretically have five instructions in the five pipeline stages at once (one instruction per stage), a different instruction would complete stage 5 in every clock cycle and on average the number of clock cycles it takes to execute an instruction is 1 (CPI = 1). In this case, the processor is said to be scalar.
With a single-execution-unit processor, the best CPI attainable is 1. However, with a multiple-execution-unit processor, one may achieve even better CPI values (CPI
Examples
Example 1
For the multi-cycle MIPS, there are five types of instructions:
- Load (5 cycles)
- Store (4 cycles)
- R-type (4 cycles)
- Branch (3 cycles)
- Jump (3 cycles)
If a program has:
- 50% load instructions
- 25% store instructions
- 15% R-type instructions
- 8% branch instructions
- 2% jump instructions
then, the CPI is:
\text{CPI} = \frac{5 \times 50 + 4 \times 25 + 4 \times 15 + 3 \times 8 + 3 \times 2}{100} = 4.4
Example 2Advanced Computer Architecture by Kai Hwang, Chapter 1, Exercise Problem 1.1
A 400MHz processor was used to execute a benchmark program with the following instruction mix and clock cycle count:
| Instruction TYPE | Instruction count | Clock cycle count |
|---|---|---|
| Integer Arithmetic | 45000 | 1 |
| Data transfer | 32000 | 2 |
| Floating point | 15000 | 2 |
| Control transfer | 8000 | 2 |
Determine the effective CPI, MIPS (Millions of instructions per second) rate, and execution time for this program.
\text{CPI} = \frac{45000 \times 1 + 32000 \times 2 + 15000 \times 2 + 8000 \times 2}{100000} = \frac{155000}{100000} = 1.55
400,\text{MHz} = 400 ,000 ,000,\text{Hz}
since: \text{MIPS} \propto 1/\text{CPI} and \text{MIPS} \propto \text{clock frequency}
\text{Effective processor performance} = \text{MIPS} = \frac{\text{clock frequency}}{\text{CPI}} \times {\frac{1}{\text{1 Million}}} = \frac{400,000,000 }{1.55 \times 1000000}= \frac{400}{1.55} = 258 , \text{MIPS}
Therefore:
\text{Execution time}(T) = \text{CPI} \times \text{Instruction count} \times \text{clock time} = \frac{\text{CPI} \times \text{Instruction Count}}{\text{frequency}} = \frac{1.55 \times 100000}{400 \times 1000000} = \frac{1.55}{4000} = 0.0003875 , \text{sec} = 0.3875 , \text{ms}
References
References
- (1994). "Computer Organization and Design: The Hardware/Software Interface". Morgan Kaufmann.
- Advanced Computer Architecture by Kai Hwang, Chapter 1, Exercise Problem 1.1
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