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PYDOTCOMPUTE EXTENDED BENCHMARK REPORT
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Generated: 2025-11-25 16:42:24
CUDA Available: True

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LARGE-SCALE GRAPH RESULTS
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Nodes        Edges        Impl         Time         Throughput      Memory    
---------------------------------------------------------------------------
10.0K        50.0K        CPU Sparse   245.91ms     203.3K          3.9MB
10.0K        50.0K        GPU Sparse   1.026s       48.7K           0.0MB
50.0K        250.0K       CPU Sparse   1.067s       234.3K          19.2MB
50.0K        250.0K       GPU Sparse   150.67ms     1.7M            0.0MB
100.0K       500.0K       CPU Sparse   2.218s       225.4K          38.6MB
100.0K       500.0K       GPU Sparse   325.04ms     1.5M            0.0MB
500.0K       2.5M         CPU Sparse   12.179s      205.3K          195.5MB
500.0K       2.5M         GPU Sparse   1.943s       1.3M            0.0MB
1.0M         5.0M         CPU Sparse   39.653s      126.1K          392.8MB
1.0M         5.0M         GPU Sparse   4.250s       1.2M            0.0MB

  Analysis:
    10.0K nodes: CPU wins (0.24x)
    50.0K nodes: GPU wins (7.08x)
    100.0K nodes: GPU wins (6.82x)
    500.0K nodes: GPU wins (6.27x)
    1.0M nodes: GPU wins (9.33x)

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STREAMING THROUGHPUT RESULTS
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This benchmark shows where GPU Actors shine!
As message count increases, actor overhead is amortized.


--- Payload: 10 floats ---
Messages     Impl               Time         Throughput      Latency     
----------------------------------------------------------------------
100          Batch Processing   47.3μs       2.1M msg/s    0.000ms
100          GPU Actors         2.18ms       45.8K msg/s    0.022ms
1.0K         Batch Processing   191.9μs      5.2M msg/s    0.000ms
1.0K         GPU Actors         13.79ms      72.5K msg/s    0.014ms
5.0K         Batch Processing   626.1μs      8.0M msg/s    0.000ms
5.0K         GPU Actors         65.57ms      76.3K msg/s    0.013ms
10.0K        Batch Processing   1.03ms       9.7M msg/s    0.000ms
10.0K        GPU Actors         138.30ms     72.3K msg/s    0.014ms
50.0K        Batch Processing   5.61ms       8.9M msg/s    0.000ms
50.0K        GPU Actors         823.84ms     60.7K msg/s    0.016ms

--- Payload: 100 floats ---
Messages     Impl               Time         Throughput      Latency     
----------------------------------------------------------------------
100          Batch Processing   72.1μs       1.4M msg/s    0.001ms
100          GPU Actors         1.68ms       59.4K msg/s    0.017ms
1.0K         Batch Processing   615.3μs      1.6M msg/s    0.001ms
1.0K         GPU Actors         17.59ms      56.8K msg/s    0.018ms
5.0K         Batch Processing   3.31ms       1.5M msg/s    0.001ms
5.0K         GPU Actors         89.66ms      55.8K msg/s    0.018ms
10.0K        Batch Processing   4.89ms       2.0M msg/s    0.000ms
10.0K        GPU Actors         148.20ms     67.5K msg/s    0.015ms
50.0K        Batch Processing   23.11ms      2.2M msg/s    0.000ms
50.0K        GPU Actors         864.95ms     57.8K msg/s    0.017ms

--- Payload: 1000 floats ---
Messages     Impl               Time         Throughput      Latency     
----------------------------------------------------------------------
100          Batch Processing   382.6μs      261.4K msg/s    0.004ms
100          GPU Actors         3.72ms       26.9K msg/s    0.037ms
1.0K         Batch Processing   5.16ms       194.0K msg/s    0.005ms
1.0K         GPU Actors         24.25ms      41.2K msg/s    0.024ms
5.0K         Batch Processing   19.83ms      252.2K msg/s    0.004ms
5.0K         GPU Actors         93.89ms      53.3K msg/s    0.019ms
10.0K        Batch Processing   35.61ms      280.8K msg/s    0.004ms
10.0K        GPU Actors         185.96ms     53.8K msg/s    0.019ms
50.0K        Batch Processing   185.33ms     269.8K msg/s    0.004ms
50.0K        GPU Actors         1.024s       48.8K msg/s    0.020ms

  Actor Overhead Amortization:
    Setup overhead: 38.22ms
    At 100 msgs: 0.022ms/msg
    At 50.0K msgs: 0.016ms/msg
    Overhead reduction: 1.3x

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LATENCY DISTRIBUTION RESULTS
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GPU Actors (1000 samples):
  p50:  0.063ms
  p95:  0.103ms
  p99:  0.131ms
  min:  0.056ms
  max:  0.189ms
  mean: 0.070ms (std: 0.016ms)

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CONCURRENT ACTORS RESULTS
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Actors     Messages     Time         Throughput      Speedup   
------------------------------------------------------------
1          1.0K         16.31ms      61.3K msg/s    1.00x
2          2.0K         30.63ms      65.3K msg/s    0.53x
4          4.0K         62.68ms      63.8K msg/s    0.26x
8          8.0K         116.82ms     68.5K msg/s    0.14x

  Scaling efficiency: 12.5% (1.00x with 8 actors)

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CONCLUSIONS
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1. LARGE-SCALE GRAPHS:
   - GPU achieves up to 1.7M edges/sec
   - GPU excels at graphs with 50.0K+ nodes

2. STREAMING THROUGHPUT:
   - GPU Actors achieve 76.3K msg/sec
   - Actor overhead amortizes with high message counts
   - Best for: persistent streaming workloads

3. LATENCY:
   - p99 latency: 0.131ms
   - Consistent performance (std: 0.016ms)

4. CONCURRENCY:
   - 1 actors achieve 1.00x speedup
   - Good scaling with multiple actors

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