Reduce timing overhead in examples 20 and 21 by measuring actual work

[Copilot]

Problem

When using --gemm_only, --comm_only, or running both operations in examples 20 and 21, total_ms was incorrectly reporting a time significantly larger than the individual kernel times (gemm_ms or communication_ms).

For example, with --gemm_only:

{
    "only_gemm": true,
    "total_ms": 1.3279694771766664,
    "gemm_ms": 0.48436891938012744
}

The total_ms was nearly 3x larger than gemm_ms.

Even when both operations run, total_ms ≠ gemm_ms + communication_ms:

{
    "total_ms": 1.6188844799995423,
    "gemm_ms": 0.6009117960929871,
    "communication_ms": 0.8070347838931613
}

Expected: ~1.41ms, Actual: 1.62ms (~13% overhead)

Root Cause

The total_ms was calculated using iris.do_bench(run_experiment, shmem.barrier), which measures the entire run_experiment() function including overhead from:

• shmem.barrier() synchronization calls added by do_bench for cache clearing between iterations
• NVTX range operations
• Stream context management
• Event recording setup

This overhead from do_bench's cache clearing barriers was being included in the timing but is not part of the actual work being measured.

Solution

Added timing events inside run_experiment() that measure the actual work being performed:

1. Record start event after the initial barrier
2. Record end event before the final barrier
3. Use this "total" timing for total_ms instead of do_bench return value

# Inside run_experiment(), after initial barrier
kernel_timing["total"]["start_event"].record()

# ... GEMM and communication kernels execute ...

# Before final barrier
kernel_timing["total"]["end_event"].record()

# In benchmarking section
iris.do_bench(run_experiment, shmem.barrier)  # Populate timing measurements
triton_ms = kernel_timing["total"]["ms"] / kernel_timing["total"]["experiments"]

This approach measures the actual work (kernel execution + NVTX ranges + stream management) while excluding the overhead that do_bench adds for cache clearing between benchmark iterations.

Impact

After this fix:

• With --gemm_only: total_ms ≈ gemm_ms
• With --comm_only: total_ms ≈ communication_ms
• Default (both operations): total_ms ≈ gemm_ms + communication_ms

This ensures total_ms accurately reflects the sum of kernel execution times without artificial overhead from benchmarking infrastructure, making results more interpretable and reliable.

Files Modified

• examples/20_gemm_all_scatter_independent/benchmark.py
• examples/21_gemm_one_shot_all_reduce_independent/benchmark.py

Fixes #264

Original prompt

---

This section details on the original issue you should resolve

<issue_title>[Bug]: total_time more than gemm/comm time added up.</issue_title>
<issue_description>For example 20, when I do --gemm_only why is the total_ms greater than gemm_ms? Similar behavior for --comm_only.

Fix the timings in relevant examples so it accurately reflects the correct time or identify where the time is being spent?

{
    "world_size": 8,
    "m": 3840,
    "n": 3840,
    "k": 4352,
    "m_comm": 3840,
    "n_comm": 3840,
    "debug": false,
    "validate": false,
    "trace_tiles": false,
    "benchmark": true,
    "datatype": "bf16",
    "output_file": "log.json",
    "BLK_M": 256,
    "BLK_N": 64,
    "BLK_K": 64,
    "gsize_m": 6,
    "heap_size": 8589934592,
    "gemm_sms": 256,
    "comm_sms": 64,
    "num_ranks": 8,
    "csv": null,
    "only_gemm": true,
    "only_comm": false,
    "tflops": 96.64808160566294,
    "total_ms": 1.3279694771766664,
    "gemm_ms": 0.48436891938012744,
    "gemm_experiments": 126
}
```</issue_description>

## Comments on the Issue (you are @copilot in this section)

<comments>
</comments>

Fixes #264

---

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[neoblizz]

@copilot Instead of ignoring the total_ms for individual runs, reduce the overhead so that its the exact stuff you need and gets measured appropriately. Because even when I run both gemm + comm, the total_ms =/= gemm + comm, for e.g.:

{
    "world_size": 8,
    "m": 3840,
    "n": 3840,
    "k": 4352,
    "m_comm": 3840,
    "n_comm": 3840,
    "debug": false,
    "validate": true,
    "trace_tiles": false,
    "benchmark": true,
    "datatype": "bf16",
    "output_file": "log.json",
    "BLK_M": 256,
    "BLK_N": 64,
    "BLK_K": 64,
    "gsize_m": 6,
    "heap_size": 8589934592,
    "gemm_sms": 256,
    "comm_sms": 48,
    "num_ranks": 8,
    "csv": null,
    "only_gemm": false,
    "only_comm": false,
    "distribution": 0,
    "success": true,
    "success_gemm": true,
    "success_comm": true,
    "gemm_registers": 112,
    "gemm_spills": 0,
    "comm_registers": 330,
    "comm_spills": 0,
    "tflops": 79.28033407302557,
    "total_ms": 1.6188844799995423,
    "gemm_ms": 0.6009117960929871,
    "gemm_experiments": 126,
    "communication_ms": 0.8070347838931613,
    "communication_experiments": 126
}

[Copilot]

@copilot Instead of ignoring the total_ms for individual runs, reduce the overhead so that its the exact stuff you need and gets measured appropriately. Because even when I run both gemm + comm, the t...

You're right - I've updated the approach to reduce overhead instead of working around it.

The fix adds timing events inside run_experiment() that measure the actual work (kernel execution + NVTX/stream overhead) while excluding the barriers that do_bench adds for cache clearing. This ensures total_ms ≈ gemm_ms + communication_ms for all operation modes.

Changes in commit 5aa23c9.