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Practice tests · 30 questions

GPU & Accelerator Architecture: the practice test

SMs, warps and the memory hierarchy, tensor cores, the roofline model, FP8 and FP4 numerics, NVLink and HBM generations, and the non-NVIDIA canon: TPU, Trainium, MI300-class, Cerebras and Groq. The hardware physics every other round assumes you know cold. This test drills exactly that: 11 easy, 11 medium and 8 hard questions, every one explained, every explanation linking into the worked material.

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Sample questions, answered

easy · sample
In the CUDA execution model, what is the unit the hardware actually schedules, and how big is it?
A thread block of up to 1,024 threads, scheduled as one unit across the whole GPU
A warp of 32 threads executing the same instruction in lockstep
A single thread, scheduled independently like a CPU thread with its own program counter
A grid, which the driver assigns to one SM at a time until it drains

Threads are grouped into blocks and blocks into a grid, but the streaming multiprocessor issues instructions per warp: 32 threads that execute the same instruction on different data. A block is pinned to one SM for its lifetime and split into warps; the warp scheduler swaps among resident warps to hide memory latency. That is why divergence inside a warp serializes work and why occupancy is counted in warps per SM.

easy · sample
Order these GPU memories from fastest to slowest for a thread: registers, shared memory, L2 cache, HBM.
Shared memory, registers, HBM, L2, because shared memory is closest to the load units
L2, HBM, shared memory, registers, because larger memories are served first
Registers, shared memory, L2, HBM
HBM, L2, shared memory, registers, because HBM has the highest bandwidth

Registers are private to a thread and accessed at core speed; shared memory is on-chip per SM with latency in the tens of cycles and terabytes per second of aggregate bandwidth; the L2 is a chip-wide cache of tens of megabytes with a few hundred cycles of latency; HBM is off-chip at about 3.35 TB/s on an H100 with latency in the hundreds of nanoseconds. Kernel optimization is largely the art of moving a byte as few times as possible down that hierarchy.

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A practice test measures recall. The material it draws from teaches the reasoning: