Why Parallelism?
Session 01 • 2311CSC501J — Parallel Processing
What You'll Learn
- Why the "free lunch" of faster chips ended
- Concurrency vs parallelism vs multitasking
- Latency vs throughput
- Why not everything can be parallelized
"Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once."
— Rob Pike
The Free Lunch Is Over
For 40 years, code got faster for free
- Moore's Law (1965): transistors per chip double every ~2 years
- Dennard Scaling: smaller transistors → less power → higher clock speeds
- Result: 1 MHz → 100 MHz → 1 GHz → 3 GHz. Same code, faster every year.
Then, around 2005, the wall
Dennard scaling broke. Push the clock higher and the chip becomes a tiny electric heater. Intel cancelled its 4 GHz Pentium. We hit the Power Wall.
Clock speed over time (simplified)
3+ GHz | ________________ ← flat since ~2005
| /
| /
| /
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1 MHz |_____/
+----------------------------------
1975 2005 today
"free lunch" "the multicore era"Moore's Law kept giving us more transistors — but we could no longer spend them on speed. So we spent them on more cores.
Every Device You Own Is Parallel
The catch: a program not written for multiple cores uses only one of them.
A 16-core laptop running bad code = 1/16th of the machine. Parallel processing is the skill of using all of it.
Three Words People Confuse
| Term | Meaning | Needs many cores? |
|---|---|---|
| Concurrency | Dealing with many things at once (structure) | No |
| Parallelism | Doing many things at once (execution) | Yes |
| Multitasking | OS switching between tasks on shared cores | No |
The Chef Analogy (remember this)
1 chef, 1 dish
Sequential. No concurrency, no parallelism.
1 chef, 3 dishes, juggling
Concurrency. One core, but progress on many tasks by switching cleverly.
3 chefs, 3 dishes, at once
Parallelism. Real simultaneous work. Needs 3 chefs (3 cores).
Watch: Concurrency Is Not Parallelism
Rob Pike (co-creator of Go) — the definitive mental model. Show the first ~10 minutes.
You can have concurrency without parallelism (one core switching fast). You generally can't get useful parallelism without concurrency — you must split the work first.
Latency vs Throughput
Latency
How long one task takes.
How fast is one car through the toll booth?
Throughput
How many tasks finish per unit time.
How many cars per minute overall?
Opening more toll booths doesn't make any single car faster (latency unchanged) — but far more cars get through per minute (throughput up).
Most parallelism is a throughput win, not a latency win. A GPU doesn't compute one pixel faster — it computes millions at once.
Parallelism Already Runs Your Life
| You do this… | …parallelism makes it possible |
|---|---|
| Google a query | Thousands of servers search different slices of the web at once, merge in ~0.5s |
| Play BGMI / GTA | The GPU shades millions of pixels in parallel, 60+ times a second |
| Ask ChatGPT | Trained across tens of thousands of GPUs running in parallel for months |
| Pay via UPI at peak | Millions of transactions processed concurrently across many machines |
| Check the weather | Supercomputers split the atmosphere into a grid, simulate each cell in parallel |
Parallel computing isn't niche. It is the foundation of every system that operates at scale — which is every system you actually use.
Watch: CPU vs GPU, Visually
NVIDIA / Mythbusters — 1:30 that makes data parallelism unforgettable.
A CPU is a few very smart painters. A GPU is one giant machine that paints the entire picture in a single shot. Different tools for different jobs — we'll see why in Unit II.
Two Flavors of Parallelism
Data Parallelism
Same operation, lots of data.
"Add 1 to every element of a million-item array." Split the array; each core does its chunk. This is what GPUs are built for.
100 students each grade one page of the same exam.
Task Parallelism
Different operations, running together.
One thread loads the file, another compresses it, another uploads it. Different jobs, same time.
One person chops, another fries, another plates.
Most real systems mix both. We go deep in Unit III.
The Catch: Not Everything Speeds Up
"Nine women can't make a baby in one month."
- Some work is inherently sequential — step 2 needs the result of step 1.
- Every parallel program has parts that can't split: reading input, combining results, coordination.
Amdahl's Law (preview, Session 08): if 90% parallelizes but 10% can't, then even with infinite cores you can never beat 10×. That stubborn 10% is the ceiling.
And parallelism creates brand-new bugs
Race condition
Two cores update the same variable at once; result depends on luck.
Deadlock
Two cores each wait for the other, forever.
Load imbalance
One core gets 90% of the work while others sit idle.
A First Feel for Speedup
time on 1 core
Speedup = ---------------------
time on N cores100s on 1 core → 25s on 4 cores
= 4× speedup (perfect!)
100s on 1 core → 40s on 4 cores
= 2.5× (real life)
Perfect (linear) speedup = N× on N cores. It's the dream, rarely achieved — the sequential parts and coordination overhead eat into it. Session 08 turns this into real formulas.
Recap & What's Next
Key Takeaways
- The free lunch is over — chips got wider (more cores), not faster (higher clock).
- Concurrency = structure; parallelism = execution; multitasking = fast switching.
- Parallelism mostly buys throughput, not lower latency.
- Not everything parallelizes — sequential parts set a hard ceiling (Amdahl).
- It trades the speed problem for coordination problems.
Homework
- Explain to a non-technical friend why "more GHz" stopped being the way to get faster.
- Find the core count of your own phone and laptop.
- Come ready: "CPU with 8 strong cores vs GPU with 4000 weak cores — why does each exist?"
Next session: Flynn's Taxonomy & Memory Models
How we classify every parallel machine ever built.