Why Parallel Computing?

Last updated on 2026-08-27 | Edit this page

Estimated time: 20 minutes

Overview

Questions

  • When should I parallelize my code?
  • What is speedup and how do I measure it?
  • What is the difference between wall clock time and CPU time?

Objectives

  • Understand the difference between serial and parallel execution
  • Learn about speedup and efficiency metrics
  • Distinguish between wall clock time and CPU time

The Serial Computing Assumption

Processor speeds have plateaued around 3-4 GHz. The solution? Parallelism: doing multiple things at once.

Serial vs. Parallel Execution

Serial execution (one task at a time):

Task A: [===========]
Task B:             [===========]
Task C:                         [===========]

Total time: 30 seconds

Parallel execution (on 3 processors):

Task A: [===]
Task B: [===]
Task C: [===]

Total time: 10 seconds

Parallelism trades computational work across multiple processors to reduce wall clock time.

Two panels. On the left, serial execution: Task A, Task B and Task C each take two hours and run one after another, giving a total of six hours. On the right, parallel execution: the same three tasks run at the same time and all feed into a total of two hours, with a note that the job finishes when the slowest task does.
Three independent two-hour tasks: run one after another and the job takes six hours; run them at the same time and it takes two.

Understanding Speedup and Efficiency

Speedup

\[S_p = \frac{T_1}{T_p}\]

Where \(T_1\) = time on 1 processor and \(T_p\) = time on p processors.

Example: Serial time 100s, parallel time on 4 cores 30s: \(S_4 = 100/30 = 3.33\)

Efficiency

\[E_p = \frac{S_p}{p}\]

With \(S_4 = 3.33\) and \(p = 4\): \(E_4 = 3.33/4 = 83\%\). The other 17% is overhead.

Strong vs. Weak Scaling

Strong scaling: Same problem size, more processors. Ideal is linear speedup.

Weak scaling: Problem size grows with processors, execution time stays constant. Often more achievable.

Wall Clock Time vs. CPU Time

CPU time = sum of compute time across all cores. Wall clock time = actual elapsed time the user experiences.

1 core:  [============================] 100s wall time, 100 CPU seconds
4 cores: [========] 30s wall time, 120 CPU seconds (100s compute + 20s overhead)

If parallelism is working, wall clock time decreases even if total CPU time increases slightly.

When Parallelism Helps (and When It Doesn’t)

Callout

Parallelism Helps When: - The problem is genuinely parallel (independent data points, loosely coupled) - The parallelizable fraction is high (> 80% of execution time) - Communication overhead is small relative to computation - Wall clock time reduction matters to you

Parallelism Does NOT Help When: - Code is inherently serial (long dependency chains) - Communication overhead exceeds computation benefit - Serial fraction is large (> 20%) - Code already completes in seconds

Challenge

Challenge

Analyzing a Workload

You have a program: reading a file takes 50s (serial), processing takes 50s (parallelizable), writing output takes 10s (serial). Total: 110s. With 4 processors, how fast can it go?

Serial fraction = (50 + 10) / 110 = 55%. Only the 50s processing can be parallelized.

Best case: 50 + 50/4 + 10 = 72.5 seconds. Speedup: 110/72.5 = 1.52x.

Only 52% speedup – the effort may not be worth it. Better to optimize I/O first.

Key Points
  • Parallel computing trades computational resources to reduce wall clock time
  • Speedup and efficiency quantify parallel performance
  • Profile and measure before parallelizing
  • Not all problems benefit from parallelism