Computer Performance Metrics to Know for Intro to Computer Architecture

Understanding computer performance metrics is key in computer architecture. Metrics like clock speed, IPC, and CPI help evaluate how efficiently a CPU executes tasks, guiding decisions on system design and optimization for better overall performance.

1. Clock Speed (Frequency)

   • Measured in Hertz (Hz), it indicates how many cycles a CPU can perform in one second.
   • Higher clock speeds generally lead to better performance, but efficiency and architecture also play crucial roles.
   • It is not the sole indicator of performance; other factors like IPC and CPI must be considered.

2. Instructions Per Cycle (IPC)

   • Represents the average number of instructions executed per clock cycle.
   • A higher IPC means better utilization of the CPU's resources, leading to improved performance.
   • IPC can vary based on the type of workload and the architecture of the CPU.

3. Cycles Per Instruction (CPI)

   • Indicates the average number of clock cycles needed to execute an instruction.
   • Lower CPI values are desirable as they suggest more efficient instruction execution.
   • CPI can be affected by factors such as instruction type, pipeline depth, and memory access times.

4. MIPS (Million Instructions Per Second)

   • A performance metric that quantifies how many millions of instructions a CPU can process in one second.
   • Useful for comparing the performance of different processors, but can be misleading if not considered with IPC and CPI.
   • MIPS does not account for the complexity of instructions, which can vary significantly.

5. FLOPS (Floating-Point Operations Per Second)

   • Measures a computer's performance in executing floating-point calculations, crucial for scientific and engineering applications.
   • Higher FLOPS indicate better performance in tasks that require extensive mathematical computations.
   • Like MIPS, FLOPS should be considered alongside other metrics for a complete performance picture.

6. Execution Time

   • The total time taken to complete a specific task or program, often influenced by clock speed, IPC, and CPI.
   • Execution time can be calculated using the formula: Execution Time = (Instruction Count × CPI) / Clock Speed.
   • Understanding execution time helps in evaluating the efficiency of algorithms and system performance.

7. Throughput

   • Refers to the amount of work done by a system in a given period, often measured in tasks completed per unit time.
   • High throughput indicates a system's ability to process a large volume of data or tasks efficiently.
   • It is an important metric for evaluating the performance of servers and data processing systems.

8. Latency

   • The time delay from the initiation of a task to its completion, often measured in milliseconds.
   • Lower latency is critical for applications requiring real-time processing, such as gaming and video conferencing.
   • Latency can be affected by various factors, including network speed, memory access times, and CPU performance.

9. Amdahl's Law

   • A formula used to find the maximum improvement of a system when only part of the system is improved.
   • It highlights the diminishing returns of performance enhancements, emphasizing that speedup is limited by the non-parallelizable portion of a task.
   • Understanding Amdahl's Law is essential for optimizing system performance and resource allocation.

10. Benchmarks (e.g., SPEC)

    • Standardized tests used to evaluate and compare the performance of computer systems.
    • Benchmarks provide a consistent way to measure performance across different architectures and configurations.
    • They help in identifying strengths and weaknesses of systems, guiding purchasing and optimization decisions.