Unverified50% confidenceFactExact time
When measuring kernel execution time with standard CPU timers, if cudaDeviceSynchronize() is not called or CUDA events are not used, what is measured is often the kernel enqueue time rather than the actual execution time.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Hands-On GPU Kernel Development Environment Setup: CUDA 13 Thresholds and Performance Analysis Pitfalls
redditr/deeplearning10/27/2026
Related Entities
Related Claims
UnverifiedCUDA events (cudaEvent_t) provide timing capability: cudaEventRecord inserts a timestamp marker into a stream, and cudaEventElapsedTime computes the GPU time between two events, with a precision of about 0.5 microseconds.79% similarUnverifiedWhen kernels execute concurrently in multiple streams, simple wall-clock timing includes the execution time of other streams, contaminating the performance data of an individual kernel; the correct approach is to use CUDA events to record timestamps within the target stream.78% similarUnverifiedBack-calculating from 2,560 CUDA cores and two operations per clock, 8.1 TFLOPS requires about 1,582 MHz to hold.71% similarUnverifiedCUDA kernels require time-consuming operations such as JIT compilation and kernel selection upon first execution, causing abnormally high inference latency for the first batch, which can be mitigated by using dummy inputs to trigger initialization in advance.71% similarUnverifiedBecause the prefill phase must handle variable-length inputs, it often needs to capture CUDA graphs separately for multiple sequence lengths, further amplifying the recording time.70% similar
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