Triton Language for Custom GPU Kernels Quiz

Answers and explanations appear as you take the quiz.

1. How does Triton describe much GPU work compared with a thread-by-thread CUDA kernel?
   • As operations over logical blocks of elements
   • As a single CPU instruction for the full tensor
   • As one kernel per individual value
   • As a SQL query over GPU memory
2. Why do vector kernels mask their final block?
   • To force the last tile to launch fewer warps
   • To prevent loads or stores beyond the valid tensor extent
   • To pad the underlying tensor until its length is divisible by the block size
   • To skip the entire final program instance when a partial block remains
3. What does tl.program_id commonly identify?
   • The GPU device ordinal that compiled the function
   • The total number of warps in a program instance
   • The index of the active logical program instance in the launch grid
   • The element offset produced by tl.arange within that instance
4. Why can a tiled matrix kernel reduce memory traffic?
   • It reduces the mathematical number of output elements
   • It eliminates every global-memory access
   • It makes all matrices contiguous automatically
   • It reuses loaded values across multiple calculations
5. What should a benchmark do with JIT compilation time?
   • Count compilation as device execution only
   • Separate first-call compilation from steady-state kernel runtime
   • Exclude all warmup and synchronization unconditionally
   • Assume every shape uses the same compiled binary
6. Why might a Triton kernel be slower than a library operation?
   • Custom kernels cannot use memory tiling
   • Triton always executes on a CPU
   • The library may already use optimized kernels and launch overhead can dominate
   • GPU kernels never benefit from fusion
7. What can change numerical results between Triton and a reference implementation?
   • The number of code comments
   • The order of Python imports only
   • The tensor's file name
   • Floating-point operation order and precision
8. When is a custom Triton kernel most defensible?
   • Profiling identifies a bottleneck and a specialized operation can justify maintenance
   • Before measuring any baseline
   • Whenever a tensor contains more than one value
   • When correctness tests are unavailable