Question 1

What is accelerated computing?

Accepted Answer

Accelerated computing pairs a general-purpose CPU with one or more domain-specific processors (GPUs, TPUs, NPUs, FPGAs). The CPU runs the operating system, branches, and serial logic. The accelerator absorbs parallel-friendly workloads such as dense linear algebra, ray traversal, FFTs, and neural network forward and backward passes.

Question 2

Why is a CPU not enough?

Accepted Answer

A CPU optimizes single-thread performance with deep pipelines, branch predictors, and large caches. That budget is wasted when the workload is the same operation repeated across millions of independent data points. Accelerators trade single-thread speed for many simple cores running in lockstep.

Question 3

Is accelerated computing just GPUs?

Accepted Answer

No. The family includes TPUs (systolic matrix arrays), NPUs (low-power inference at the edge), FPGAs (reconfigurable logic), and a long tail of ASICs targeted at specific workloads such as video transcoding and network packet processing.

Question 4

When does accelerated computing stop being worth it?

Accepted Answer

When the workload is branchy, latency-sensitive on a single transaction, or so small that PCIe transfer time dominates the math. Accelerators reward problems that are big, regular, and tolerant of pipelined latency.

Four questions, answered honestly.