GitLab

- 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.

- Update rocm_commom.cmake for CMake>=3.24
- Prevent isolation build
- Add BabelStream as a submodule
- Update dockerignore

**Description**

- When building the CUDA 11.1.1 image, pip (Python 3.8) cannot find a
pre-built wheel for the latest wandb release (v0.23.1). As a result, pip
attempts to build wandb from source. However, the build fails because
the image does not have Go installed, which is required for building
wandb from source. Then the error appears.

**Solution**

- For the CUDA 11.1.1 build, install the required build tools (e.g., Go,
Rust, and Cargo) needed for wandb.

---------
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

**Description**

The ib-loopback test was regressed due to this recent
[change](https://github.com/microsoft/superbenchmark/commit/c65ae56713d6bfcc4a3be37d7fe24779590f9791).
When running ib-loopback using the standard
[config](https://github.com/microsoft/superbenchmark/blob/c65ae56713d6bfcc4a3be37d7fe24779590f9791/superbench/config/default.yaml#L69

),
the test would fail since it would pass numeric values like `0` into the
test command which would break since it is not a valid IB device name.

Example failure:

```
 [2025-11-25 22:08:38,100 vmssnc6ec000003:141056][micro_base.py:200][INFO] Execute command - round: 0, benchmark: ib-loopback, command: /usr/local/bin/run_perftest_loopback 47 45 /usr/local/b                                                                                                                                                        in/ib_write_bw -s 8388608 -F --iters=20000 -d 0 -p 45617 -x 0 --report_gbits.
[0]: IB device 0 not found
 Unable to find the Infiniband/RoCE device
IB device 0 not found
 Unable to find the Infiniband/RoCE device
[2025-11-25 22:08:39,113 vmssnc6ec000003:141056][micro_base.py:209][ERROR] Microbenchmark execution failed - round: 0, benchmark: ib-loopback, error message: IB device 0 not found
 Unable to find the Infiniband/RoCE device
IB device 0 not found
 Unable to find the Infiniband/RoCE device
```


**Major Revision**
- Major Revision A
- Major Revision B
- ...

**Minor Revision**
- Minor Revision A
- Minor Revision B
- ...

---------
Co-authored-by: Henry Li <lihl@microsoft.com>

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

**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

Benchmarks: Micro benchmark - Support verification and parallel run for disk performance benchmark (#741)

**Description**
Adds verification and parallel run support for disk performance
benchmark.

**Major Revision**
- Adds `--verify` flag to support verify written data.
- Supports loading benchmark options from `PROC_RANK`, `BLOCK_DEVICES`
and `NUMA_NODES` environmental variables.

---------
Co-authored-by: guoshzhao <guzhao@microsoft.com>

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: Hongtao Zhang <hongtaozhang@microsoft.com>

Add support for cuda13.0.
Add cuda13.0.dockerfile.
Add cuda13.0 image building task to github pipeline.
Update GPU STREAM to work with cuda13.0.
Fix data type conversion perf bug in GPU stream.
Update nvbandwidth submodule to be v0.8.
Update perftest submodule to be 4bee61f80d9e268fc97eaf40be00409e91d3a19e
(recent master).

---------
Co-authored-by: Ubuntu <dilipreddi@gmail.com>
Co-authored-by: guoshzhao <guzhao@microsoft.com>

**Description**
add option to exclude data copy time in model benchmarks.

**Major Revision**
- add an option --no_copy
- move start time after data copy finish

**Description**
Add numa support for nvbandwidth.

Benchmarks: micro benchmarks - change cublasLtMatmulDescCreate scaleType  from CUDA_R_32F to CUDA_R_16F in FP16 dist inference  (#732)

**Description**
change cublasLtMatmulDescCreate scaleType from CUDA_R_32F to CUDA_R_16F
in FP16 dist inference to fix cublaslt error.

**Description**

Cherry-pick bug fixes from v0.12.0 to main.

**Major Revisions**

* #725
* #727
* #728
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>
Co-authored-by: Yifan Xiong <yixio@microsoft.com>
Co-authored-by: Guoshuai Zhao <guzhao@microsoft.com>

---------
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>

Added MoE model using MixtralConfig. 

1. Added 8x7b and 8x22b variants 
2. Requires high VRAM as all experts are loaded in memory. Thus,
disabled training due to memory constraint on test worker.

---------
Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>

**Description**
Add deepseek megatron-lm benchmark.

---------
Co-authored-by: yukirora <yuting.jiang@microsoft.com>
Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>

**Description**
Add cuda 12.9 dockerfile and build in pipeline.

---------
Co-authored-by: Guoshuai Zhao <microsoft@microsoft.com>
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>
Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>

**Description**
Add FP4 precision support for cublaslt_gemm benchmark.

**Major Revision**
- Add new type `fp4e2m1` and `__nv_fp4_e2m1`.
- For FP4 matmul, precision of MatrixC (add) should be FP16, precision
of MatricD (output) should be FP4, otherwise, it will not work.
- Add macro `CUDA_VERSION` to resolve the compatibility issue of
different CUDA versions.

---------
Co-authored-by: Ubuntu <aiperf@aiperf000000.hp5z1gqeinfufbj2u3jcty5fme.cdmx.internal.cloudapp.net>
Co-authored-by: AVA <39534996+avazr@users.noreply.github.com>
Co-authored-by: Guoshuai Zhao <microsoft@microsoft.com>

**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 furt...

**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: dpower4 <dilipreddi@gmail.com>

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`.

In the current implementation, the CPU‑stream benchmark code renames the
binary before the microbench base class can verify its existence,
causing the default‐binary check to fail.

This PR adds a “default” binary—built with the standard compile
parameters—so that the base class can always find and validate it. Once
the default binary is in place, the CPU‑stream code will rename it as
needed and re‑check its presence before running the benchmark.

The PR also enable CPU stream in the default settings.

---------
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>

adding gb200 cuda arch flag for cublaslt compilation

**Description**
Updated docker for 12.8
Use cutlass latest relase 3.8 with ARCH 100(blackwell) support
add latest nccl-test release with ARCH 100(blackwell) 
Updated msccl to support build for sm_100
No breaking changes, so backward compatible tested with  cuda 12.4

---------
Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>

Improves logging info for diagnosis rule op baseline errors. This allows
developers to easily detect errors in their rule files as well as
baseline files, improving end-user experience.

Co-authored-by: Yifan Xiong <yifan.xiong@microsoft.com>
Co-authored-by: Hongtao Zhang <garyworkzht@gmail.com>

Root Cause:

1. '_get_all_test_cases()' was called in '_parser' while '_parser' was
defined in the base class.
2.  in '_get_all_test_cases()', cmd path was not included.

Fix:

1. Remove '_get_all_test_cases()' from '_parser'.
2. Construct path for cmd.

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>

**Description**

1. Fixed the bug that nvbandwidth benchmark need to handle 'N/A' values
in nvbandwidth cmd output.
2. Replaced the input format of test cases with a list.
3. Add nvbandwidth configuration example in default config files.

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>
Co-authored-by: Yifan Xiong <yifan.xiong@microsoft.com>

**Description**
Today I was running a benchmark on my machine. And encountered a fancy
issue with tensorrt-inference.
I got code 33, which according to the source code is:
```
MICROBENCHMARK_RESULT_PARSING_FAILURE = 33
```
I dived deep into the code and found out the following problem. The
parser stumbled upon getting to the following line:
```
[11/28/2024-17:03:11] [I] Latency: min = 7.2793 ms, max = 10.1606 ms, mean = 7.41642 ms, median = 7.39551 ms, percentile(99%) = 8 ms
```
I ran it separately on the code and found out that the regular
expression was not suitable for the cases like this, when you encounter
an INT as a result in milliseconds.
That's why this pull request is created.
I came up with the closest possible regular expression to fix this issue
and not to introduce any other bug.

**Major Revision**
- 0.11.0

**Description**
Introduce architecture support for version 10.0 in gemm-flops.

Added llama benchmark - training and inference in accordance with the
existing pytorch models implementation like gpt2, lstm etc.

- added llama fp8 unit test for better code coverage, to reduce memory
required
- updated transformers version >= 4.28.0 for LLamaConfig
- set tokenizers version <= 0.20.3 to avoid 0.20.4 version
[issues](https://github.com/huggingface/tokenizers/issues/1691

) with
py3.8
- added llama2 to tensorrt
- llama2 tests not added to test_tensorrt_inference_performance.py due
to large memory requirement for worker gpu. tests validated separately
on gh200

---------
Co-authored-by: dpatlolla <dpatlolla@microsoft.com>

Fix ordering of args in err messages.

Upgrade dependency versions in Azure pipeline:

* Remove Python 3.6 and add Python 3.10 for cpu-unit-test
* Upgrade CUDA from 11.1 to 12.4 for cuda-unit-test
* Update labels accordingly

---------
Co-authored-by: Dilip Patlolla <dilipreddi@gmail.com>

**Description**

Add nvbandwidth benchmark.

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>

**Description**
Add micro benchmark to measure general CPU bandwidth and latency without 'mlc'.

Test output:
```
{
"cpu-memory-bw-latency/return_code": 0,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_0_1_bw": 5388.75021,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_0_1_lat": 0.185571786,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_1_0_bw": 4634.82028,
"cpu-memory-bw-latency/mem_bandwidth_matrix_numa_1_0_lat": 0.215758096,
}
```

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>

Add support for arm64 build:

- Updated dockerfile for arm64 build
- extend cpu stream compilation for neoverse 
- handle onnxruntime-gpu installation
- third party builds filtering based on arch
- disable cuda decode perf build for non x86

The current GPU Copy BW Performance fails on Nvidia Grace systems. This
is due to the memory only numa node and thus the numa_run_on_node fails
for such nodes and halts completely.

This fix checks for the presence of assigned CPU cores for the numa
node, on checking if it has no cpu cores assigned, it skips that
specific node during the args creation and continues.

**Description**
Cherry pick bug fixes from v0.11.0 to main

**Major Revision**
* #645 
* #648 
* #646 
* #647 
* #651 
* #652 
* #650

---------
Co-authored-by: hongtaozhang <hongtaozhang@microsoft.com>
Co-authored-by: Yifan Xiong <yifan.xiong@microsoft.com>