When building high-performance systems — especially in embedded, real-time, or low-latency trading systems — understanding the difference between _concurrency_ and _parallelism_ is fundamental.

Many developers use the terms interchangeably.

They are not the same.

Let’s break it down properly.

What is Concurrency?

Concurrency is about structure.

It means multiple tasks _can_ make progress during overlapping time periods.

It does not require multiple CPU cores.

It is about managing multiple things at once.

Think of it like this:

Time →
------------------------------------------------
Task A:  [----work----]       [---work---]
Task B:        [--work--]  [--work--]
Task C:                [---work---]
------------------------------------------------

One CPU core may be switching between tasks very quickly.

This is called context switching.

Key idea

Concurrency =

Dealing with multiple tasks logically at the same time.

It improves:

• Responsiveness
• Throughput
• Resource utilization

Real Example (Single Core)

Imagine your embedded device:

• Reading sensor
• Sending MQTT packet
• Updating UI
• Logging data

Even on one core, you can structure this concurrently:

while(true)
{
    read_sensor();
    process_data();
    send_network();
    update_ui();
}

Or using threads:

std::thread sensor_thread(read_sensor_loop);
std::thread network_thread(network_loop);

Even on one core, the OS schedules them.

That is concurrency.

What is Parallelism?

Parallelism is about execution.

It means tasks are literally running at the same time on multiple cores.

Core 1:  Task A  ─────────────
Core 2:  Task B  ─────────────
Core 3:  Task C  ─────────────

Now work is physically happening simultaneously.

Key idea

Parallelism =

Doing multiple tasks at the exact same time.

It improves:

• Speed
• Computational throughput
• Performance scaling

Example (Multi-core CPU)

Parallel for loop:

#include <execution>
#include <vector>
#include <algorithm>

std::vector<int> data(1'000'000);

std::for_each(std::execution::par,
              data.begin(),
              data.end(),
              [](int& x) {
                  x *= 2;
              });

Here:

• Work is split across multiple cores
• Actual parallel execution occurs

Concurrency vs Parallelism — The Core Difference