Business sense / NVIDIA
Accelerate the job, not just the kernel.
CUDA exposes parallel GPU computation; the customer still buys an end-to-end result.
How the choices connect
- 01
Prepare
CPU work and preprocessing remain in the job.
- 02
Transfer
Moving inputs and outputs consumes time.
- 03
Compute
Only the parallel portion benefits from GPU acceleration.
- 04
Deliver
Compare total time, cost and accuracy per completed job.
The economic question
The fastest component does not set total application speed. Start with a timing trace. A tenfold improvement to one stage matters much less when most elapsed time is outside that stage. Engineering and migration costs belong in the commercial decision.
What to investigate
Benchmark a representative production workload including data movement, warm-up, concurrency and the required accuracy. A kernel-only benchmark cannot establish customer savings.
Separate the fact from the inference
Reported · historical snapshot
NVIDIA introduced CUDA in 2006 so computational workloads could use GPUs independently of graphics APIs. Its programming model organizes parallel work into threads and thread blocks.
NVIDIA CUDA C++ Programming Guide · legacy historical introduction ↗Reporting period is stated above; this is not a live financial feed.
Method · Harper analysis
Benchmark the customer’s actual workload: total time = data transfer + GPU computation + remaining CPU work. Add engineering migration cost before comparing cost per completed job.
A falsifying check
Test the share of work that can run in parallel and whether data movement becomes the bottleneck. A faster GPU kernel does not guarantee a faster end-to-end application.
Would you recommend GPUs if computation is ten times faster but data transfer takes most of the job? Ask for the current timing breakdown first.
Original case lab · illustrative, not company results
Where does a 10× GPU improvement matter?
Original job: 100 seconds. GPU stage is 10× faster; fixed transfer overhead adds 5 seconds. All other work unchanged. Synthetic benchmark; not an NVIDIA product claim.
Range 0–100. Enter an exact value or use the slider.
Temporary scenario · reset on reload.
New seconds = (100 − parallel share) + parallel share ÷ 10 + 5.
At 80% parallel share: 33 seconds, or 3.03× end-to-end speedup. At 0%, added transfer makes the job slower.
Carry the method into a complete fictional case. The Studio exercise practices a related skill; it does not simulate NVIDIA.
Open related Case Studio practice →Read the framework →