# Different results with different macros for profiling

**URL:** <https://discourse.julialang.org/t/different-results-with-different-macros-for-profiling/117592>\
**Category:** GPU\
**Tags:** cuda\
**Created:** [July 29, 2024, 12:29pm UTC](https://discourse.julialang.org/t/different-results-with-different-macros-for-profiling/117592 "2024-07-29T12:29:43Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![HAP40HTT](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/hap40htt/32/52904_2.png) [@HAP40HTT](https://discourse.julialang.org/u/HAP40HTT)\
**Post date:** [July 29, 2024, 12:29pm UTC](https://discourse.julialang.org/t/different-results-with-different-macros-for-profiling/117592/1 "2024-07-29T12:29:43Z")

</div>

Hi everyone,

I’m relatively new to kernel programming with CUDA.jl and am currently implementing a kernel to compute attention using a sliding-window attention mechanism.

While profiling the code, I observed that the runtime varies depending on the macro used:

```julia
CUDA.@profile CUDA.@sync AttnGPU.sliding_window_attn!(
    attn.window_attn, Q, K, mask, idx_matrix,
    embed, seq_len, batch_size, w, kernels_window[1]
)

```

Total time: 346.58 µs

```julia
CUDA.@bprofile CUDA.@sync AttnGPU.sliding_window_attn!(
    attn.window_attn, Q, K, mask, idx_matrix, 
    embed, seq_len, batch_size, w, kernels_window[1]
    )

```

Time distribution: 59.37 µs ± 32.58 ( 44.32 ‥ 149.44)

```julia
time = fill(0.0, 1000)
for i in 1:1000
    time[i] = CUDA.@elapsed CUDA.@sync AttnGPU.sliding_window_attn!(
        attn.window_attn, Q, K, mask, idx_matrix,
        embed, seq_len, batch_size, w, kernels_window[1]
    )
end
println("Average time in μs: ", sum(time) * 1e6 / size(time, 1))

```

Mean time: 180.53 µs

I’m unsure if these differences arise from an error on my part or if there are other factors at play. Additionally, I’m curious which method provides a more reliable estimate of the kernel’s execution time.

Thanks for your insights!

---

<div class="post-metadata">

**Author:** ![eldee](https://avatars.discourse-cdn.com/v4/letter/e/b5a626/32.png) [@eldee](https://discourse.julialang.org/u/eldee)\
**Post date:** [July 30, 2024, 11:38am UTC](https://discourse.julialang.org/t/different-results-with-different-macros-for-profiling/117592/2 "2024-07-30T11:38:27Z")

</div>

Hi,

`CUDA.@profile` will execute your code only once, so if you run a small code block, you will get a poor (high variance) estimate of the expected run time. Additionally, I think there’s also some ‘warm-up’ involved (apart from compilation), meaning your code will run slower if it only runs once. Of course, you could get around this by profiling a loop repeating your code, but that’s already close to the point of `CUDA.@bprofile`.

Without access to the `AttnGPU.sliding_window_attn!` implementation, I’ll use

```julia
using CUDA
x = CUDA.rand(Float32, 10^8)
y = similar(x)
y .= x .* 2

```

as an example. With `CUDA.@profile y .= x .* 2`, I get anything between 2 and 100 ms, but typically around 80 ms. By contrast,

```julia
CUDA.@profile begin
    for _ = 1:1000
        CUDA.@sync y .= x .* 2
    end
end

```

gives me fairly consistent values, e.g. `2.25 ms ± 4.02 ( 1.99 ‥ 86.06)` device-side. Using `CUDA.@bprofile y .= x .* 2`, I get similar values, e.g. `2.17 ms ± 2.64 ( 1.99 ‥ 72.2)`

(By the way, note that `CUDA.@time`, `CUDA.@elapsed`, `CUDA.@profile`, `CUDA.@bprofile` already `synchronize()`, so you don’t have to do this manually. (You can check this yourself using e.g. `@less CUDA.@elapsed y .= x .* 2` or `Using MacroTools; @macroexpand CUDA.@elapsed y .= x .* 2`.) In the loop example, we want to measure the time of the iterations themselves, not of the entire loop, so have to synchronise after each step, not just once at the end.)

I cannot replicate the difference with the manual `CUDA.@elapsed` loop:

```julia
mean([CUDA.@elapsed y .= x .* 2 for _ = 1:1000])

```

gives me around 2.2 ms. Also

```julia
Using BenchmarkTools
@benchmark CUDA.@sync y .= x .* 2

```

gives me similar (total, device + host) times, e.g. ` Time (mean ± σ): 2.141 ms ± 55.957 μs`.

Not sure what would be the exact cause of your problem then, but it seems likely to me that it is specific to your `AttnGPU.sliding_window_attn!`. Though you might want to check if you get consistent profiling results for something simple like `y .= x .* 2`, and that there’s not a larger problem somehow specific to your machine (been there 🙂 ).

---

<div class="post-metadata">

**Author:** ![HAP40HTT](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/hap40htt/32/52904_2.png) [@HAP40HTT](https://discourse.julialang.org/u/HAP40HTT)\
**Post date:** [August 2, 2024, 9:24am UTC](https://discourse.julialang.org/t/different-results-with-different-macros-for-profiling/117592/3 "2024-08-02T09:24:57Z")

</div>

Hi eldee,

thanks for you help and your comments 😀.

I repeated the execution with `CUDA.@profile` around 100 times to check for consistency. The results for `AttnGPU.sliding_window_attn!` were all between 330 and 370 µs.  
I will try profiling the code with a simpler function when I am back. Currently, I only have my notebook with me and it does not have a GPU.

---

<div class="post-metadata">

**Author:** ![maleadt](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/maleadt/32/10097_2.png) [@maleadt](https://discourse.julialang.org/u/maleadt)\
**Post date:** [August 6, 2024, 7:53pm UTC](https://discourse.julialang.org/t/different-results-with-different-macros-for-profiling/117592/4 "2024-08-06T19:53:09Z")

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Another possible explanation is the overhead imposed by profiling. It’s normally very small, but depending on your kernel’s characteristics it can matter (refer to the CUPTI docs for more details, e.g., [2. Usage — Cupti 12.6 documentation](https://docs.nvidia.com/cupti/main/main.html#serialization)).

You can try the serial profiling mode instead by doing `CUDA.@profile concurrent=false ...`