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Release 13.4 of the CUDA Samples supported by CUDA Toolkit 13.4. See Changelog for more information.
80 lines
3.6 KiB
Markdown
80 lines
3.6 KiB
Markdown
# clock - Per-Block Kernel Timing with CUB Reduction
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## Description
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A CUDA sample that demonstrates how to use the `clock()` function to accurately measure kernel execution time on a per-block basis. Each of the 64 blocks records its own start and end SM cycle counter, then performs a block-wide parallel **min-reduction** over 512 float elements using CUB's `BlockReduce`. The host collects all timestamps and computes the average elapsed clock cycles across all blocks.
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Because blocks execute in parallel and out of order with no cross-block synchronization, each block independently measures its own execution time — this is the correct way to time GPU work at block granularity.
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## What You'll Learn
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- Using `clock()` inside a CUDA kernel to capture per-block SM cycle counts
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- Performing a block-wide parallel reduction with `cub::BlockReduce` using a custom binary operator
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- Loading multiple elements per thread (`ITEMS_PER_THREAD = 2`) for CUB reductions
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- Understanding why only thread 0 holds the valid aggregate after a `BlockReduce`
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- Querying device properties (`cudaDeviceGetAttribute`) without helper libraries
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- Computing average elapsed clocks on the host from per-block timestamps
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## Key Concepts
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- **SM Clock Counter** — `clock()` reads the streaming multiprocessor's cycle counter; difference between two samples gives elapsed cycles for that block
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- **CUB BlockReduce** — warp-shuffle-based block-scope reduction; default constructor allocates shared memory internally via `PrivateStorage()`, no explicit `TempStorage` needed
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- **Items per thread** — each thread owns 2 elements; CUB's array overload of `Reduce` combines them before the cross-thread reduction
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- **Per-block timing** — since blocks run independently, each block times itself; the host averages results across all blocks
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## Key APIs
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### CUDA Runtime
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- `cudaSetDevice` — select the active GPU for all subsequent CUDA calls
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- `cudaDeviceGetAttribute` — query device properties (compute capability, SM count) without `cudaGetDeviceProperties`
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- `cudaMalloc` / `cudaFree` — allocate and release device memory
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- `cudaMemcpy` — transfer data between host and device
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- `clock()` — device-side SM cycle counter (returns `clock_t`)
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### CUB
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- `cub::BlockReduce<T, BLOCK_THREADS>` — block-scope reduction template
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- `BlockReduce::Reduce(T (&input)[ITEMS_PER_THREAD], ReductionOp op)` — reduce multiple items per thread with a custom binary operator
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## Requirements
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### Hardware
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- NVIDIA GPU with Compute Capability 7.5 or higher
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### Software
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- CMake 3.20 or newer
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- A C++17-capable host compiler
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## How to Build
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See the [top-level README](../../../README.md#building-cuda-samples) for full build instructions, including how to build all samples or a single sample standalone.
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## How to Run
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```bash
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./clock
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```
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No command-line arguments are required. The sample always runs on device 0.
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## Expected Output
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```text
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CUDA Clock sample
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GPU Device 0: with compute capability 8.9 and Number of SMs 142
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Average clocks/block = 1239.640625
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```
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The average clocks value varies by GPU and reflects how many SM cycles each block takes to complete the reduction. Blocks scheduled later on a busy GPU will show higher elapsed times.
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## Files
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- `clock.cu` — kernel implementation and host driver
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- `README.md` — this file
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- `CMakeLists.txt` — build configuration
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## See Also
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- [CUB BlockReduce documentation](https://nvidia.github.io/cccl/unstable/cub/developer/block_scope.html)
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- [CUDA C++ Programming Guide — clock()](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/cpp-language-support.html#clock-and-clock64)
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