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  1. 18 Apr, 2026 3 commits
    • one's avatar
      Benchmark: Model benchmark - deterministic training support (#731) (#2) · 47d4a79d
      one authored 
      

Adds opt-in deterministic training mode to SuperBench's PyTorch model
benchmarks. When enabled --enable-determinism. PyTorch deterministic
algorithms are enforced, and per-step numerical fingerprints (loss,
activation means) are recorded as metrics. These can be compared across
runs using the existing sb result diagnosis pipeline to verify bit-exact
reproducibility — useful for hardware validation and platform
comparison.
 
Flags added - 

--enable-determinism
--check-frequency: Number of steps after which you want the metrics to
be recorded
--deterministic-seed

Changes - 

Updated pytorch_base.py to handle deterministic settings, logging.
Added a new example script: pytorch_deterministic_example.py
Added a test file: test_pytorch_determinism_all.py to verify everything
works as expected.

Usage - 

Step 1: Run 1 - Run with --enable-determinism and the necessary metrics
will be recorded in the results-summary.jsonl file
Step 2: Generate the baseline file from the Run 1 results using - sb
result generate-baseline
Step 3: Run 2 - Run with --enable-determinism and the necessary metrics
will be recorded in the results-summary.jsonl file on a different
machine (or the same machine)
Step 4: Run diagnosis on the results generated from the 2 runs using the
- sb result diagnosis command

Note - 
1. Make sure all the parameters are constant between the 2 runs 
2. Running the diagnosis command requires the rules.yaml file

---------
Co-authored-by: default avatarAishwarya Tonpe <aishwarya.tonpe25@gmail.com>
Co-authored-by: default avatarUbuntu <rdadmin@HPCPLTNODE0.n3kgq4m0lhoednrx3hxtad2nha.cdmx.internal.cloudapp.net>
      47d4a79d
    • one's avatar
      Format python code · 8c28b69a
      one authored
      8c28b69a
    • one's avatar
      Runner: validate MPI bind-to option and cover configurable bind-to in tests · 655519cb
      one authored
      655519cb
  3. 17 Apr, 2026 3 commits
  5. 15 Apr, 2026 1 commit
  7. 02 Apr, 2026 5 commits
  9. 01 Apr, 2026 5 commits
  11. 27 Mar, 2026 1 commit
  13. 25 Mar, 2026 1 commit
  15. 19 Mar, 2026 3 commits
    • one's avatar
      Migrate gpu-stream to BabelStream v5.0 · d4051602
      one authored
      d4051602
    • one's avatar
      Enhance DTK platform support and GPU detection · 1a57f2d6
      one authored 
      - Added Platform.DTK in the microbenchmark framework.
- Introduced new DTK hipblaslt benchmark class and corresponding tests.
- Updated Dockerfile to include hipblaslt-bench and its permissions.
- Registered DTK benchmarks in the benchmark registry for various performance tests.
- Enhanced GPU detection logic to recognize HYGON GPUs.

This update improves the benchmarking capabilities for DTK, ensuring compatibility and performance testing across platforms.
      1a57f2d6
    • one's avatar
      Update DTK dockerfile and microbenchmarks · c4f39919
      one authored 
      - Update rocm_commom.cmake for CMake>=3.24
- Prevent isolation build
- Add BabelStream as a submodule
- Update dockerignore
      c4f39919
  17. 28 Jan, 2026 1 commit
  19. 04 Dec, 2025 1 commit
  21. 17 Nov, 2025 1 commit
    • Yuting Jiang's avatar
      Benchmarks: micro benchmarks - add --set_ib_devices option to auto-select IB... · c65ae567
      Yuting Jiang authored 
      Benchmarks: micro benchmarks - add --set_ib_devices option to auto-select IB device by MPI local rank in ib validation (#733)

**Description**
add --set_ib_devices option to auto-select IB device by MPI local rank 


**Major Revision**
- Add a new CLI flag --set_ib_devices to automatically select irregular
IB devices based on the MPI local rank.
- When enabled, the benchmark queries available IB devices via
network.get_ib_devices() and selects the device corresponding to
OMPI_COMM_WORLD_LOCAL_RANK.
- Fall back to existing --ib_dev behavior when the flag is not provided.

**Minor Revision**
- Add an env in network.get_ib_devices() to allow user to set the device
name
      c65ae567
  23. 23 Oct, 2025 1 commit
    • Yuting Jiang's avatar
      Benchmarks: Micro benchmark - add ncu profile support in cublaslt-gemm (#740) · f6e65a98
      Yuting Jiang authored 
      **Description**
This PR adds NCU (NVIDIA Nsight Compute) profiling support to the
cublaslt-gemm micro benchmark, enabling detailed kernel analysis
including DRAM throughput, compute throughput, and launch arguments.

**Major Revision**
- Add --enable_ncu_profiling and --profiling_metrics for ncu profiling
- Modifies command execution to use NCU when profiling is enabled
- Updates result parsing to handle both standard and NCU profiled output
formats
      f6e65a98
  25. 22 Oct, 2025 1 commit
  27. 08 Oct, 2025 1 commit
    • Hongtao Zhang's avatar
      Enhancement: Add nsys and pytorch profiler debug trace support (#744) · d804dbb6
      Hongtao Zhang authored 
      

To improve benchmark debugging, the following debug methods were added:

pytorch profiler in model benchmark

- SB_ENABLE_PYTORCH_PROFILER: switch to enable/disable
- SB_TORCH_PROFILER_TRACE_DIR: log path
These 2 runtime variables need to be configured in SB config file.

nsys in SB runner

- SB_ENABLE_NSYS: switch to enable/disable 
- SB_NSYS_TRACE_DIR: log path
These 2 runtime variables need to be configured in runner's ENV

---------
Co-authored-by: default avatarHongtao Zhang <hongtaozhang@microsoft.com>
      d804dbb6
  29. 01 Oct, 2025 1 commit
  31. 29 Sep, 2025 2 commits
  33. 19 Sep, 2025 1 commit
  35. 12 Aug, 2025 1 commit
  37. 30 Jun, 2025 1 commit
  39. 26 Jun, 2025 1 commit
  41. 25 Jun, 2025 1 commit
  43. 24 Jun, 2025 1 commit
  45. 20 Jun, 2025 2 commits
    • Babak Hejazi's avatar
      Benchmark - Support autotuning in cublaslt gemm (#706) · 60b13256
      Babak Hejazi authored 
      **Description**
Enable autotuning as an opt-in mode when benchmarking cublasLt via
`cublaslt_gemm`

The implementation is based on
https://github.com/NVIDIA/CUDALibrarySamples/blob/master/cuBLASLt/LtSgemmSimpleAutoTuning/sample_cublasLt_LtSgemmSimpleAutoTuning.cu

The behavior of original benchmark command remains unchanged, e.g.:
- `cublaslt_gemm -m 2048 -n 12288 -k 1536 -w10000 -i 1000 -t fp8e4m3`

The new opt-in options are `-a` (for autotune) and `-I` (for autotune
iterations, default is 50, same as the default for `-i`) and `-W` (for
autotune warmups, default=20, same as the default for `-w`), e.g.:
- `cublaslt_gemm -m 2048 -n 12288 -k 1536 -w 10000 -i 1000 -t fp8e4m3
-a`
- `cublaslt_gemm -m 2048 -n 12288 -k 1536 -w 10000 -i 1000 -t fp8e4m3 -a
-I 10 -W 10`

**Note:** This PR also changes the default `gemm_compute_type` for BF16
and FP16 to `CUBLAS_COMPUTE_32F`.

**Further observations:** 
1. The support matrix of the `cublaslt_gemm` could be further extended
in the future to support non-FP16 output as well for FP8 inputs.
2. Currently, the input matrices are initialized with values of 1.0 and
2.0 which makes them less demanding in terms of power. Another future
extension could be to enable another fill mode for, say, uniform random
numbers between -1 and 1.
3. cuBLAS workspace recommendations are listed under
https://docs.nvidia.com/cuda/cublas/#cublassetworkspace



Update (June 10, 2025): verified using higher level test driver with
these commands:

1. inline:
```
python3 -c "                                                                            
from superbench.benchmarks import BenchmarkRegistry, Platform
from superbench.common.utils import logger

parameters = (
    '--num_warmup 10 --num_steps 50 '
    '--shapes 512,512,512 1024,1024,1024 --in_types fp16 fp32 '
    '--enable_autotune --num_warmup_autotune 20 --num_steps_autotune 50'
)
context = BenchmarkRegistry.create_benchmark_context(
    'cublaslt-gemm', platform=Platform.CUDA, parameters=parameters
)
benchmark = BenchmarkRegistry.launch_benchmark(context)
logger.info('Result: {}'.format(benchmark.result))
"
```

2. newly added script: 
`python3 examples/benchmarks/cublaslt_function.py`

---------
Co-authored-by: default avatarBabak Hejazi <babakh@nvidia.com>
      60b13256
    • WenqingLan1's avatar
      Benchmark - Add Grace CPU support for CPU Stream (#719) · 0b8d1fd4
      WenqingLan1 authored 
      

**Description**
Added support for Grace CPU neo2 architecture in CPU Stream. Now CPU
Stream supports dual socket benchmarking.

Example config for this arch support:
```yaml
    cpu-stream:numa0:
      timeout: *default_timeout
      modes:
      - name: local
        parallel: no
      parameters:
        cpu_arch: neo2
        numa_mem_nodes: 0
        cores: 0 1 2 3 4 5 6 7 8
    cpu-stream:numa1:
      timeout: *default_timeout
      modes:
      - name: local
        parallel: no
      parameters:
        cpu_arch: neo2
        numa_mem_nodes: 1
        cores: 64 65 66 67 68 69 70 71 72
    cpu-stream:numa-spread:
      timeout: *default_timeout
      modes:
      - name: local
        parallel: no
      parameters:
        cpu_arch: neo2
        numa_mem_nodes: 0 1
        cores: 0 1 2 3 4 5 6 7 8 64 65 66 67 68 69 70 71 72
```

---------
Co-authored-by: default avatardpower4 <dilipreddi@gmail.com>
      0b8d1fd4
  47. 18 Jun, 2025 1 commit
    • WenqingLan1's avatar
      Benchmarks - Add GPU Stream Micro Benchmark (#697) · 4eddd50a
      WenqingLan1 authored 
      Added GPU Stream benchmark - measures the GPU memory bandwidth and
efficiency for double datatype through various memory operations
including copy, scale, add, and triad.
- added documentation for `gpu-stream` detailing its introduction,
metrics, and descriptions.
- added unit tests for `gpu-stream`. Example output is in
`superbenchmark/tests/data/gpu_stream.log`.
      4eddd50a