Deciphering Negative "Warps per SM" in PyTorch: A Deep Dive

Seeing a negative value for "warps per SM" during PyTorch inference profiling can be perplexing. But fear not! Understanding what this metric represents and how it's calculated is crucial to troubleshooting performance bottlenecks. This article explains why "warps per SM" might appear negative and how to interpret it within the context of your profiling trace.

What are Warps and Streaming Multiprocessors (SM) in CUDA?

Before digging into the negative values, let's quickly recap the basics:

• Warp: A warp is a group of 32 threads that execute the same instruction simultaneously on a GPU.
• Streaming Multiprocessor (SM): An SM is the core processing unit on a GPU. It executes warps.

Understanding how your code utilizes these structures impacts performance.

The Mystery of the Negative "Warps per SM"

The "warps per SM" metric aims to represent the average number of warps actively running on each SM during the kernel's execution. A negative value is clearly nonsensical in a purely physical sense.

The reason you see a negative value in your pytorch trace likely stems from how the profiling tools aggregate and present data. Specifically, it could be an artifact of:

• Sampling Artifact: Profilers often sample metrics at intervals. If the kernel execution is very short relative to the sampling frequency, the profiler might not accurately capture the warp count, leading to miscalculations when averaged across the SM.
• Internal Bookkeeping Errors: There may be an issue with how the profiler is tracking warp occupancy or SM activity within the specific kernel you are profiling. This is less common but not impossible.

Example: Suppose a kernel runs for a tiny fraction of the profiling sampling window. The profiler picks value of -4, the value is correct, but the aggregate value of this parameter for the entire function gives incorrect value.*

How to Interpret and Troubleshoot

Instead of focusing solely on the negative value, consider these approaches:

1. Examine Related Metrics: Analyze other related metrics in your trace, such as "occupancy," "registers per thread," and "shared memory" usage. Look for imbalances or bottlenecks. Low occupancy could explain underutilization of SM's.

2. Focus on Overall Kernel Performance: The ultimate goal is performance, focus on total execution time. Does this kernel contribute significantly to the overall inference time?

3. Increase Profiling Granularity: If possible, increase the profiling frequency or use a more detailed profiling tool (like Nsight) to capture a more accurate representation of kernel execution.

4. Simplify the Kernel: If possible, try to isolate this specific kernel and reduce its complexity to see if the negative warps per SM value persists. This can help you identify potential issues with the kernel's structure or how it interacts with the CUDA runtime.

5. Long tail issue: Use Nsight Compute or Nsight Systems for granular system-wide visibility

   Nsight Compute: Deep insights into kernel performance, occupancy, and memory-related metrics Nsight Systems: System-level performance analysis across CPU, GPU, and I/O for overall visibility.*

Long-Tail Keyword Integration

• Addressing negative warps per SM pytorch needs a holistic approach, going beyond just the immediate metric.
• Investigating pytorch profiling inference requires examining multiple metrics rather than fixate on one anomalous output.

Actionable Steps

• Don't panic! View the negative "warps per SM" as a potential indicator, not a definitive diagnosis.
• Investigate profiling granularity and alternative profiling tools.
• Correlate with other performance metrics for a broader understanding.

By understanding the intricacies of CUDA execution and the limitations of profiling tools, you can effectively troubleshoot performance bottlenecks and optimize your PyTorch models for maximum efficiency.