GitLab

- Enable `computation-communication-overlap` and `sharding-matmul` in
some configs through the existing PyTorch distributed mode.
- Use `torchrun --standalone` for single-node `torch.distributed` runs
to avoid fixed rendezvous port conflicts on 29500.
- Update runner command-generation test expectation for the new
single-node torchrun behavior.

* Support rocm in ort-inference

* Add tests

* Update dockerfiles for docker 18

* Install onnx, add params to ort-inference

* Update docs

Add gpu-hpl and gpu-hpl-mxp micro benchmarks backed by rocHPL and rocHPL-MxP.

Implemented a shared GPU HPL base that:
- Generates per-workload HPL dat files and parses the corresponding output files.
- Supports common HPL inputs such as process grid, matrix size, block size, broadcast topology, warmup, iterations, and reduce operator.
- Adds rocHPL-specific tuning parameters for gpu-hpl.
- Formats metric keys from input-derived workload attributes.
- Reports `flops`, `time`, and `tests_pass` metrics with warmup-aware aggregation.

Add benchmark registrations, parser tests, sample output fixtures, documentation, and recommended configurations for gpu-hpl and gpu-hpl-mxp.

Update rocHPL and rocHPL-MxP third-party integration with build patches, install targets, and SuperBench run helper scripts.

Also update gpu-hpcg metric naming to use flops instead of gflops, remove standalone domain/verification-style metrics from the documented metric surface, and refresh Hygon HPCG documentation/config references accordingly.

Summary

The gpu_stream benchmark has NVIDIA-specific dependencies that prevent
it from compiling on ROCm 6.3+. This change makes it CUDA-only,
gracefully skipping the build with a warning on non-NVIDIA
  environments.

  Problem

The gpu_stream benchmark fails to compile on ROCm 6.3+ due to multiple
NVIDIA-specific dependencies:

1. nvml.h — NVIDIA Management Library header, used for querying actual
memory clock rates. No HIP equivalent. Referenced in gpu_stream.cu and
gpu_stream_utils.hpp.
2. cuda.h in headers — Three .hpp files (gpu_stream.hpp,
gpu_stream_kernels.hpp, gpu_stream_utils.hpp) directly include <cuda.h>
and <cuda_runtime.h>. These headers are not processed by hipify-perl
(only
  .cu source files are), so they fail to resolve on ROCm.
3. Deprecated hipDeviceProp_t struct fields — The code accesses
memoryBusWidth, memoryClockRate, and ECCEnabled from the device
properties struct. These fields were removed from hipDeviceProp_t in
ROCm
    6.3, causing compilation errors after hipification.

The existing ROCm path was marked as incomplete (# TODO: test for ROC)
and was never fully functional on recent ROCm versions.

  Changes

- Removed the non-functional ROCm/HIP build path from
gpu_stream/CMakeLists.txt
- When CUDA is not found, prints a warning and returns gracefully
instead of attempting a broken hipify build or raising FATAL_ERROR
- No changes to the NVIDIA/CUDA build path — it continues to work as
before

  Impact

   - NVIDIA builds: No change — gpu_stream builds and installs normally
- ROCm builds: gpu_stream is skipped with a warning message. Previously
it would fail the entire make cppbuild step, blocking the Docker image
build
- Other benchmarks: Unaffected — build.sh continues to the next
benchmark after gpu_stream returns
Co-authored-by: Hongtao Zhang <hongtaozhang@microsoft.com>

* Update gpu-hpcg metrics to encode process and problem shape

* Fix tests

- Add `numactl` support for local runner modes, including `cpunodebind`, `membind`, and `physcpubind`.
- Add `gpu_affinity` resolution through `sb node topo --get gpu-numa-affinity --gpu-id`.
- Add `sb node topo` support for GPU NUMA topology queries.
- Update BW1000 config to use the new local `numactl` semantics.
- Document the new `numactl` mode fields and limitations.

* Update mem-bw to use BandwidthTest

* Update config and format code

* Fix some lint warnings
* Exclude some paths in cpplint
* Fix some tests and formatting

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: Aishwarya Tonpe <aishwarya.tonpe25@gmail.com>
Co-authored-by: Ubuntu <rdadmin@HPCPLTNODE0.n3kgq4m0lhoednrx3hxtad2nha.cdmx.internal.cloudapp.net>

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: Ubuntu <rdadmin@HPCPLTNODE0.n3kgq4m0lhoednrx3hxtad2nha.cdmx.internal.cloudapp.net>

- 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.

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>

**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.

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**
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>

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.

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>

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>

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>