micro-benchmarks.md

---
id: micro-benchmarks
---

# Micro Benchmarks

## Computation Benchmarks

### `kernel-launch`

#### Introduction

Measure GPU kernel launch performance from multiple perspectives, including end-to-end latency,
host-side dispatch overhead, steady-state launch throughput, and device-side launch time.

#### Metrics

| Name                                | Unit               | Description                                                      |
|-------------------------------------|--------------------|------------------------------------------------------------------|
| kernel-launch/e2e_latency_us        | time (us)          | Single-shot end-to-end latency measured in CPU time.             |
| kernel-launch/host_dispatch_us      | time (us)          | Host-side dispatch overhead per kernel measured in CPU time.     |
| kernel-launch/launch_throughput_mkps| throughput (MKPS)  | Steady-state kernel launch throughput.                           |
| kernel-launch/device_launch_us      | time (us)          | Device-side average launch time per kernel measured by events.   |

### `gemm-flops`

#### Introduction

Measure the GPU GEMM FLOPS for different float and int data types, with or without Tensor Core (XDLOPS),
performed by NVIDIA [cutlass](https://github.com/NVIDIA/cutlass/tree/ccb697bac77fcc898e9c897b2c90aa5b60ac72fb)
or AMD [rocblas-bench](https://github.com/ROCmSoftwarePlatform/rocBLAS/tree/develop/clients/benchmarks).
The benchmark supports one or more GEMM shapes in `m,n,k` format.

#### Metrics

| Name                         | Unit           | Description                                             |
|------------------------------|----------------|---------------------------------------------------------|
| gemm-flops/fp64_m${m}_n${n}_k${k}_flops        | FLOPS (GFLOPS) | GEMM float64 peak FLOPS.                                |
| gemm-flops/fp32_m${m}_n${n}_k${k}_flops        | FLOPS (GFLOPS) | GEMM float32 peak FLOPS.                                |
| gemm-flops/fp16_m${m}_n${n}_k${k}_flops        | FLOPS (GFLOPS) | GEMM float16 peak FLOPS.                                |
| gemm-flops/fp64_tc_m${m}_n${n}_k${k}_flops     | FLOPS (GFLOPS) | GEMM float64 peak FLOPS with NVIDIA Tensor Core.        |
| gemm-flops/tf32_tc_m${m}_n${n}_k${k}_flops     | FLOPS (GFLOPS) | GEMM tensor-float32 peak FLOPS with NVIDIA Tensor Core. |
| gemm-flops/fp16_tc_m${m}_n${n}_k${k}_flops     | FLOPS (GFLOPS) | GEMM float16 peak FLOPS with NVIDIA Tensor Core.        |
| gemm-flops/bf16_tc_m${m}_n${n}_k${k}_flops     | FLOPS (GFLOPS) | GEMM bfloat16 peak FLOPS with NVIDIA Tensor Core.       |
| gemm-flops/int8_tc_m${m}_n${n}_k${k}_iops      | IOPS (GIOPS)   | GEMM int8 peak IOPS with NVIDIA Tensor Core.            |
| gemm-flops/int4_tc_m${m}_n${n}_k${k}_iops      | IOPS (GIOPS)   | GEMM int4 peak IOPS with NVIDIA Tensor Core.            |
| gemm-flops/fp32_xdlops_m${m}_n${n}_k${k}_flops | FLOPS (GFLOPS) | GEMM tensor-float32 peak FLOPS with AMD XDLOPS.         |
| gemm-flops/fp16_xdlops_m${m}_n${n}_k${k}_flops | FLOPS (GFLOPS) | GEMM float16 peak FLOPS with AMD XDLOPS.                |
| gemm-flops/bf16_xdlops_m${m}_n${n}_k${k}_flops | FLOPS (GFLOPS) | GEMM bfloat16 peak FLOPS with AMD XDLOPS.               |
| gemm-flops/int8_xdlops_m${m}_n${n}_k${k}_iops  | IOPS (GIOPS)   | GEMM int8 peak IOPS with AMD XDLOPS.                    |

### `matmul`

#### Introduction

Large scale matmul operation using `torch.matmul` with one GPU.

#### Metrics

| Name                           | Unit      | Description                    |
|--------------------------------|-----------|--------------------------------|
| pytorch-matmul/nosharding_time | time (ms) | Time of pure matmul operation. |

### `cublaslt-gemm` / `hipblaslt-gemm`

#### Introduction

Measure the GEMM performance of [`cublasLtMatmul`](https://docs.nvidia.com/cuda/cublas/#cublasltmatmul) or [`hipblasLt-bench`](https://github.com/ROCm/hipBLASLt/blob/develop/clients/benchmarks/README.md).

#### Metrics

| Name                                                      | Unit           | Description                     |
|-----------------------------------------------------------|----------------|---------------------------------|
| cublaslt-gemm/${dtype}\_${batch}\_${m}\_${n}\_${k}_flops  | FLOPS (TFLOPS) | TFLOPS of measured GEMM kernel. |
| hipblaslt-gemm/${dtype}\_${batch}\_${m}\_${n}\_${k}_flops | FLOPS (TFLOPS) | TFLOPS of measured GEMM kernel. |

### `cublas-function`

#### Introduction

Measure the performance of most common Nvidia cuBLAS functions with parameters in models training including ResNet, VGG, DenseNet, LSTM, BERT, and GPT-2.

The supported functions for cuBLAS are as follows:
 - cublasSgemm
 - cublasSgemmStridedBatched
 - cublasGemmStridedBatchedEx
 - cublasGemmEx
 - cublasCgemm3mStridedBatched
 - cublasCgemm

#### Metrics

| Name                                                              | Unit      | Description                                                                                                                                  |
|-------------------------------------------------------------------|-----------|----------------------------------------------------------------------------------------------------------------------------------------------|
| cublas-function/name\_${function_name}\_${parameters}_time        | time (us) | The mean time to execute the cublas function with the parameters.                                                                            |
| cublas-function/name\_${function_name}\_${parameters}_correctness |           | Whether the calculation results of executing the cublas function with the parameters pass the correctness check if enable correctness check. |
| cublas-function/name\_${function_name}\_${parameters}_error       |           | The error ratio of the calculation results of executing the cublas function with the parameters if enable correctness check.                 |

### `cudnn-function`

#### Introduction

Measure the performance of most common Nvidia cuDNN functions with parameters in models training including ResNet, VGG, DenseNet, LSTM, BERT, and GPT-2.

The supported functions for cuDNN are as follows:
 - cudnnConvolutionBackwardFilter
 - cudnnConvolutionBackwardData
 - cudnnConvolutionForward

#### Metrics

| Name                                                      | Unit      | Description                                                      |
|-----------------------------------------------------------|-----------|------------------------------------------------------------------|
| cudnn-function/name\_${function_name}\_${parameters}_time | time (us) | The mean time to execute the cudnn function with the parameters. |

### `tensorrt-inference`

#### Introduction

Inference PyTorch/ONNX models on NVIDIA GPUs with [TensorRT](https://developer.nvidia.com/tensorrt).

Currently the following models are supported:

> alexnet, densenet121, densenet169, densenet201, densenet161, googlenet, inception_v3, mnasnet0_5,
> mnasnet1_0, mobilenet_v2, resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d,
> resnext101_32x8d, wide_resnet50_2, wide_resnet101_2, shufflenet_v2_x0_5, shufflenet_v2_x1_0,
> squeezenet1_0, squeezenet1_1, vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19_bn, vgg19
> lstm, bert-base, bert-large, gpt2-small

> Do not support large models like `gpt2-large` currently because models larger than 2GB (maximum protobuf size) cannot be exported in one ONNX file.

#### Metrics

| Name                                             | Unit      | Description                                                                                              |
|--------------------------------------------------|-----------|----------------------------------------------------------------------------------------------------------|
| tensorrt-inference/${model}_gpu_time_mean        | time (ms) | The mean GPU latency to execute the kernels for a query.                                                 |
| tensorrt-inference/${model}_gpu_time_99          | time (ms) | The 99th percentile GPU latency to execute the kernels for a query.                                      |
| tensorrt-inference/${model}_host_time_mean       | time (ms) | The mean H2D, GPU, and D2H latency to execute the kernels for a query.                                   |
| tensorrt-inference/${model}_host_time_99         | time (ms) | The 99th percentile H2D, GPU, and D2H latency to execute the kernels for a query.                        |
| tensorrt-inference/${model}_end_to_end_time_mean | time (ms) | The mean duration from when the H2D of a query is called to when the D2H of the same query is completed. |
| tensorrt-inference/${model}_end_to_end_time_99   | time (ms) | The P99 duration from when the H2D of a query is called to when the D2H of the same query is completed.  |

### `ort-inference`

#### Introduction

Inference performance of the torchvision models using ONNXRuntime. Currently the following models are supported:

> alexnet, densenet121, densenet169, densenet201, densenet161, googlenet, inception_v3, mnasnet0_5,
> mnasnet1_0, mobilenet_v2, resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d,
> resnext101_32x8d, wide_resnet50_2, wide_resnet101_2, shufflenet_v2_x0_5, shufflenet_v2_x1_0,
> squeezenet1_0, squeezenet1_1, vgg11, vgg11_bn, vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19_bn, vgg19

The supported percentiles are 50, 90, 95, 99, and 99.9.

#### Parameters

| Parameter              | Default | Description                                                                 |
|------------------------|---------|-----------------------------------------------------------------------------|
| `--pytorch_models`     | See above | Torchvision models to export to ONNX and run with ONNX Runtime.           |
| `--precision`          | `float16` | Inference precision: `float32`, `float16`, or `int8`.                     |
| `--graph_opt_level`    | `3`     | ONNX Runtime graph optimization level: `0`, `1`, `2`, or `3`.              |
| `--batch_size`         | `32`    | Batch size of the generated input tensor.                                  |
| `--num_warmup`         | `64`    | Number of warmup inference iterations excluded from metrics.               |
| `--num_steps`          | `256`   | Number of measured inference iterations.                                   |
| `--execution_provider` | `auto`  | ONNX Runtime execution provider: `auto`, `cuda`, `rocm`, `migraphx`, `cpu`, or a full provider name. |
| `--pretrained`         | `false` | Use pretrained torchvision weights when exporting ONNX models.             |

#### Metrics

| Name                                                | Unit      | Description                                                              |
|-----------------------------------------------------|-----------|--------------------------------------------------------------------------|
| ort-inference/{precision}_{model}_time              | time (ms) | The mean latency to execute one batch of inference.                      |
| ort-inference/{precision}_{model}_time_{percentile} | time (ms) | The {percentile}th percentile latency to execute one batch of inference. |

### `gpu-burn`

#### Introduction

Multi-GPU CUDA stress test for GPU compute and memory utilization, performed by [gpu-burn](https://github.com/wilicc/gpu-burn).
Supports the use of double unit types and the use of tensor cores.

#### Metrics

| Name                    | Unit     | Description                                                                        |
|-------------------------|----------|------------------------------------------------------------------------------------|
| gpu-burn/time           | time (s) | The runtime for gpu-burn test.                                                     |
| gpu-burn/gpu_[0-9]_pass | yes/no   | The result of the gpu-burn test for each GPU (1: yes, 0: no).                      |
| gpu-burn/abort          | yes/no   | Whether or not GPU-burn test aborted before returning GPU results (1: yes, 0: no). |

### `cpu-hpl`

#### Introduction

HPL or High Performance Computing Linpack evaluates compute bandwidth by solving dense linear systems in double precision arethmetic.
Performed by [High-Performance Linpack Benchmark for Distributed-Memory Computers](https://netlib.org/benchmark/hpl/)

#### Metrics

| Name               | Unit               | Description                                                               |
|--------------------|--------------------|---------------------------------------------------------------------------|
| cpu-hpl/tests_pass |                    | HPL completed running and correctness test has passed (1: pass, 0: fail). |
| cpu-hpl/throughput | bandwidth (GFlops) | Compute bandwidth.                                                        |
| cpu-hpl/time       | time (s)           | Time elapsed during HPL run.                                              |

### `gpu-hpl`

#### Introduction

Measure GPU HPL performance for dense linear algebra workloads.
Performed by [rocHPL](https://github.com/ROCm/rocHPL).

#### Parameters

`gpu-hpl` always generates an HPL input `.dat` file from the command-line parameters.
The generated file name and output file name are derived from the same workload prefix used in metric keys.

| Parameter              | Default | Description                                                                 |
|------------------------|---------|-----------------------------------------------------------------------------|
| `--p`                  | `1`     | Number of rows in the MPI process grid.                                     |
| `--q`                  | `1`     | Number of columns in the MPI process grid.                                  |
| `--local-p`            |         | Optional number of rows in the node-local MPI process grid.                 |
| `--local-q`            |         | Optional number of columns in the node-local MPI process grid.              |
| `--n`                  | `45312` | Global matrix size.                                                         |
| `--nb`                 | `384`   | Panel/block size.                                                           |
| `--warmup`             | `0`     | Number of warmup HPL runs to exclude from result aggregation.               |
| `--iterations`         | `1`     | Number of measured HPL runs to include in result aggregation.               |
| `--reduce-op`          | `max`   | Reduce operator for measured runs by FLOPS: `mean`, `median`, `max`, `min`. |
| `--pmap`               | `1`     | Process mapping: `0` for row-major, `1` for column-major.                   |
| `--bcast`              | `0`     | Broadcast topology: `0` for 1rg, `1` for 1rM, `2` for 2rg, `3` for 2rM, `4` for Lng, `5` for LnM. |
| `--threshold`          | `16.0`  | Residual check threshold.                                                   |
| `--pfact`              | `2`     | Panel factorization: `0` for left, `1` for Crout, `2` for right.            |
| `--nbmin`              | `32`    | Recursive stopping criterion.                                               |
| `--ndiv`               | `2`     | Number of panels in recursion.                                              |
| `--rfact`              | `2`     | Recursive panel factorization: `0` for left, `1` for Crout, `2` for right.  |
| `--depth`              | `1`     | Lookahead depth.                                                            |
| `--swap`               | `1`     | Swapping algorithm: `0` for binary exchange, `1` for long, `2` for mix.     |
| `--swapping-threshold` | `64`    | Swapping threshold.                                                         |
| `--l1`                 | `0`     | L1 storage form: `0` for transposed, `1` for non-transposed.                |
| `--u`                  | `0`     | U storage form: `0` for transposed, `1` for non-transposed.                 |
| `--equilibration`      | `0`     | Equilibration: `0` for no, `1` for yes.                                     |
| `--memory-alignment`   | `8`     | Memory alignment in doubles.                                                |

`--warmup` runs are excluded from result aggregation. `--reduce-op` is applied to the measured FLOPS values.
The reported `_time` metric is reduced in the same performance direction by applying `--reduce-op` to `1 / time`
and then converting the result back to seconds.

#### Metrics

rocHPL reports performance, time, and correctness metrics.
The metric key includes the configured HPL variant, process grid, matrix size, and block size:
`${tv}_P${P}_Q${Q}_N${N}_NB${NB}`.
The `tv` field is based on the rocHPL `T/V` value and includes an extended suffix for `L1`, `U`,
`Equilibration`, and `memory-alignment`. For example, `WC11R2R32_TTN8` uses transposed `L1`,
transposed `U`, no equilibration, and memory alignment `8`.

| Name                                                  | Unit           | Description                                      |
|-------------------------------------------------------|----------------|--------------------------------------------------|
| `gpu-hpl/${tv}_P${P}_Q${Q}_N${N}_NB${NB}_flops`      | FLOPS (GFLOPS) | Throughput for the specified rocHPL run.         |
| `gpu-hpl/${tv}_P${P}_Q${Q}_N${N}_NB${NB}_time`       | time (s)       | Time elapsed during the specified HPL run.       |
| `gpu-hpl/${tv}_P${P}_Q${Q}_N${N}_NB${NB}_tests_pass` |                | Whether residual checks passed (1: pass, 0: fail). |

### `gpu-hpl-mxp`

#### Introduction

Measure GPU HPL-MxP performance for mixed-precision dense linear algebra workloads.
Performed by [rocHPL-MxP](https://github.com/ROCm/rocHPL-MxP).

#### Parameters

`gpu-hpl-mxp` always generates an HPL-MxP input `.dat` file from the command-line parameters.
The generated file name and output file name are derived from the same workload prefix used in metric keys.

| Parameter      | Default | Description                                                                 |
|----------------|---------|-----------------------------------------------------------------------------|
| `--p`          | `1`     | Number of rows in the MPI process grid.                                     |
| `--q`          | `1`     | Number of columns in the MPI process grid.                                  |
| `--local-p`    |         | Optional number of rows in the node-local MPI process grid.                 |
| `--local-q`    |         | Optional number of columns in the node-local MPI process grid.              |
| `--n`          | `61440` | Global matrix size.                                                         |
| `--nb`         | `2560`  | Panel/block size.                                                           |
| `--warmup`     | `0`     | Number of warmup HPL-MxP runs to exclude from result aggregation.           |
| `--iterations` | `1`     | Number of measured HPL-MxP runs to include in result aggregation.           |
| `--reduce-op`  | `max`   | Reduce operator for measured runs by FLOPS: `mean`, `median`, `max`, `min`. |
| `--pmap`       | `1`     | Process mapping: `0` for row-major, `1` for column-major.                   |
| `--bcast`      | `0`     | Broadcast topology: `0` for 1rg, `1` for 1rM, `2` for 2rg, `3` for 2rM, `4` for Lng, `5` for LnM. |
| `--threshold`  | `16.0`  | Residual check threshold.                                                   |

`--warmup` runs are excluded from result aggregation. `--reduce-op` is applied to the measured FLOPS values.
The reported `_time` metric is reduced in the same performance direction by applying `--reduce-op` to `1 / time`
and then converting the result back to seconds.

#### Metrics

rocHPL-MxP reports performance, time, and correctness metrics.
The metric key includes the configured HPL-MxP variant, process grid, matrix size, and block size:
`${tv}_P${P}_Q${Q}_N${N}_NB${NB}`.
The `tv` field is based on the rocHPL-MxP `T/V` value, for example `WC1`.

| Name                                                      | Unit           | Description                                      |
|-----------------------------------------------------------|----------------|--------------------------------------------------|
| `gpu-hpl-mxp/${tv}_P${P}_Q${Q}_N${N}_NB${NB}_flops`      | FLOPS (GFLOPS) | Throughput for the specified rocHPL-MxP run.     |
| `gpu-hpl-mxp/${tv}_P${P}_Q${Q}_N${N}_NB${NB}_time`       | time (s)       | Time elapsed during the specified HPL-MxP run.   |
| `gpu-hpl-mxp/${tv}_P${P}_Q${Q}_N${N}_NB${NB}_tests_pass` |                | Whether residual checks passed (1: pass, 0: fail). |

### `gpu-hpcg`

#### Introduction

Measure GPU HPCG performance for sparse linear algebra and multigrid-style workloads.
Performed by [rocHPCG](https://github.com/ROCm/rocHPCG).

#### Parameters

| Parameter | Default | Description                                                                 |
|-----------|---------|-----------------------------------------------------------------------------|
| `--npx`   | `1`     | Number of MPI processes in the x dimension.                                 |
| `--npy`   | `1`     | Number of MPI processes in the y dimension.                                 |
| `--npz`   | `1`     | Number of MPI processes in the z dimension.                                 |
| `--nx`    | `560`   | Local problem size in the x dimension.                                      |
| `--ny`    | `280`   | Local problem size in the y dimension.                                      |
| `--nz`    | `280`   | Local problem size in the z dimension.                                      |
| `--rt`    | `60`    | Benchmark runtime in seconds.                                               |
| `--tol`   | `1.0`   | Verification control: `0` runs reference verification; non-zero skips it.   |
| `--pz`    | `0`     | Partition boundary in the z process dimension.                              |
| `--zl`    | `--nz`  | Local `nz` value for processes with z rank lower than `--pz`.               |
| `--zu`    | `--nz`  | Local `nz` value for processes with z rank greater than or equal to `--pz`. |

#### Metrics

rocHPCG reports performance and time metrics.
Performance metrics are reported for `final`, `ddot`, `waxpby`, `spmv`, `mg`, and `total`.
The metric key includes the configured process domain and local problem size:
`p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}`.

| Name                                                                                             | Unit             | Description                                             |
|--------------------------------------------------------------------------------------------------|------------------|---------------------------------------------------------|
| `gpu-hpcg/${operation}_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}_flops`                          | FLOPS (GFLOPS)   | Throughput for the specified rocHPCG operation.         |
| `gpu-hpcg/${operation}_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}_bandwidth`                      | bandwidth (GB/s) | Bandwidth for the specified rocHPCG operation.          |
| `gpu-hpcg/${operation}_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}_flops_per_process`              | FLOPS (GFLOPS)   | Per-process throughput for the specified operation.     |
| `gpu-hpcg/${operation}_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}_bandwidth_per_process`          | bandwidth (GB/s) | Per-process bandwidth for the specified operation.      |
| `gpu-hpcg/setup_time_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}`                                  | time (s)         | Setup phase duration.                                   |
| `gpu-hpcg/optimization_time_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}`                           | time (s)         | Optimization phase duration.                            |
| `gpu-hpcg/total_time_p${npx}x${npy}x${npz}_n${nx}x${ny}x${nz}`                                  | time (s)         | Total runtime.                                          |

### `cpu-stream`

#### Introduction

Measure of memory bandwidth and computation rate for simple vector kernels.
performed by [University of Virginia STREAM benchmark](https://www.cs.virginia.edu/stream/ref.html).

#### Metrics

| Name                                                     | Unit             | Description                                                    |
|----------------------------------------------------------|------------------|----------------------------------------------------------------|
| cpu-stream/threads                                       |                  | Number of threads used for the test. Determined by core count. |
| cpu-stream/['copy', 'scale', 'add', 'triad']\_throughput | bandwidth (MB/s) | Memory throughput of designated kerel operation.               |
| cpu-stream/['copy', 'scale', 'add', 'triad']\_time_avg   | time (s)         | Average elapsed times over all iterations.                     |
| cpu-stream/['copy', 'scale', 'add', 'triad']\_time_min   | time (s)         | Minimum elapsed times over all iterations.                     |
| cpu-stream/['copy', 'scale', 'add', 'triad']\_time_max   | time (s)         | Maximum elapsed times over all iterations.                     |

## Communication Benchmarks

### `cpu-memory-bw-latency`

#### Introduction

Measure the memory copy bandwidth and latency across different CPU NUMA nodes.
performed by [Intel MLC Tool](https://www.intel.com/content/www/us/en/developer/articles/tool/intelr-memory-latency-checker.html).

#### Metrics

| Name                                                                    | Unit             | Description                                                         |
|-------------------------------------------------------------------------|------------------|---------------------------------------------------------------------|
| cpu-memory-bw-latency/mem\_bandwidth\_matrix\_numa\_[0-9]+\_[0-9]+\_bw  | bandwidth (MB/s) | Former NUMA to latter NUMA memory bandwidth.                        |
| cpu-memory-bw-latency/mem\_bandwidth\_matrix\_numa\_[0-9]+\_[0-9]+\_lat | time (ns)        | Former NUMA to latter NUMA memory latency.                          |
| cpu-memory-bw-latency/mem\_max\_bandwidth\_all\_reads\_bw               | bandwidth (MB/s) | Whole-CPU maximum memory bandwidth, full read.                      |
| cpu-memory-bw-latency/mem\_max\_bandwidth\_3_1\_reads-writes\_bw        | bandwidth (MB/s) | Whole-CPU maximum memory bandwidth, read : write = 3 : 1.           |
| cpu-memory-bw-latency/mem\_max\_bandwidth\_2_1\_reads-writes\_bw        | bandwidth (MB/s) | Whole-CPU maximum memory bandwidth, read : write = 2 : 1.           |
| cpu-memory-bw-latency/mem\_max\_bandwidth\_1_1\_reads-writes\_bw        | bandwidth (MB/s) | Whole-CPU maximum memory bandwidth, read : write = 1 : 1.           |
| cpu-memory-bw-latency/mem\_max\_bandwidth\_stream-triad\_like\_bw       | bandwidth (MB/s) | Whole-CPU maximum memory bandwidth, with stream-triad like pattern. |

### `mem-bw`

#### Introduction

Measure the memory copy bandwidth across PCI-e and memory copy bandwidth between GPUs,
performed by [NVIDIA](https://github.com/NVIDIA/cuda-samples/tree/master/Samples/1_Utilities/bandwidthTest)
or [AMD](https://github.com/ROCm-Developer-Tools/HIP/tree/master/samples/1_Utils/hipBusBandwidth) bandwidth test tool.
The bandwidthTest sample was out-of-date and has been removed as of the CUDA Samples 12.9 release. For up-to-date bandwidth measurements, refer instead to the nvbandwidth benchmark.

#### Metrics

| Name          | Unit             | Description                      |
|---------------|------------------|----------------------------------|
| mem-bw/h2d_bw | bandwidth (GB/s) | Host to device copy bandwidth.   |
| mem-bw/d2h_bw | bandwidth (GB/s) | Device to host copy bandwidth.   |
| mem-bw/d2d_bw | bandwidth (GB/s) | Device to device copy bandwidth. |

### `gpu-copy-bw`

Measure the memory copy bandwidth performed by GPU SM/DMA engine, including device-to-host, host-to-device and device-to-device.
For measurements of peer-to-peer communication performance between AMD GPUs, GPU memory buffers are allocated in `hipDeviceMallocUncached` (previous `hipDeviceMallocFinegrained`) mode to maximize performance.

#### Metrics

| Name                                                        | Unit             | Description                                                                                                                              |
|-------------------------------------------------------------|------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| cpu\_to\_gpu[0-9]+\_by\_(sm\|dma)\_under\_numa[0-9]+\_bw    | bandwidth (GB/s) | The unidirectional bandwidth of one GPU reading one NUMA node's host memory using DMA engine or GPU SM.                                  |
| gpu[0-9]+\_to\_cpu\_by\_(sm\|dma)\_under\_numa[0-9]+\_bw    | bandwidth (GB/s) | The unidirectional bandwidth of one GPU writing one NUMA node's host memory using DMA engine or GPU SM.                                  |
| gpu[0-9]+\_to\_gpu[0-9]+\_by\_(sm\|dma)\_bw                 | bandwidth (GB/s) | The unidirectional bandwidth of one GPU reading or writing self's memory using DMA engine or GPU SM.                                     |
| gpu[0-9]+\_to\_gpu[0-9]+\_(read\|write)\_by\_(sm\|dma)\_bw  | bandwidth (GB/s) | The unidirectional bandwidth of one GPU reading or writing peer GPU's memory using DMA engine or GPU SM with peer communication enabled. |
| cpu\_and\_gpu[0-9]+\_by\_(sm\|dma)\_under\_numa[0-9]+\_bw   | bandwidth (GB/s) | The bidirectional bandwidth of one GPU reading and writing one NUMA node's host memory using DMA engine or GPU SM.                       |
| gpu[0-9]+\_and\_cpu\_by\_(sm\|dma)\_under\_numa[0-9]+\_bw   | bandwidth (GB/s) | Same as above, but generated by --dtoh --bidirectional.                                                                                  |
| gpu[0-9]+\_and\_gpu[0-9]+\_by\_(sm\|dma)\_bw                | bandwidth (GB/s) | The bidirectional bandwidth of one GPU reading and writing self's memory using DMA engine or GPU SM.                                     |
| gpu[0-9]+\_and\_gpu[0-9]+\_(read\|write)\_by\_(sm\|dma)\_bw | bandwidth (GB/s) | The bidirectional bandwidth of one GPU reading and writing peer GPU's memory using DMA engine or GPU SM with peer communication enabled. |
| gpu[0-9]+\_to\_gpu\_all\_write\_by\_sm\_bw                  | bandwidth (GB/s) | The unidirectional bandwidth of one GPU writing all peer GPUs' memory using GPU SM with peer communication enabled.                      |
| gpu\_all\_to\_gpu[0-9]+\_write\_by\_sm\_bw                  | bandwidth (GB/s) | The unidirectional bandwidth of all peer GPUs writing one GPU's memory using GPU SM with peer communication enabled.                     |
| gpu\_all\_to\_gpu\_all\_write\_by\_sm\_bw                   | bandwidth (GB/s) | The unidirectional bandwidth of all peer GPUs writing all peer GPUs' memory using GPU SM with peer communication enabled.                |

### `gpu-stream`

#### Introduction

Measure the memory bandwidth of GPU using BabelStream (`hip-stream`) backend.
The benchmark executes copy, scale, add, triad, and dot operations.
The `array_size` parameter represents the number of elements.
Each benchmark run measures the GPU visible to the current process.

#### Metrics

| Metric Name                                                       | Unit             | Description                                                                                    |
|-------------------------------------------------------------------|------------------|------------------------------------------------------------------------------------------------|
| STREAM\_INIT\_[float\|double]\_array\_[0-9]+\_bw                 | bandwidth (GB/s) | Initialization phase bandwidth for the current benchmark run and one array size.              |
| STREAM\_INIT\_[float\|double]\_array\_[0-9]+\_time               | time (s)         | Initialization phase runtime for the current benchmark run and one array size.                |
| STREAM\_READ\_[float\|double]\_array\_[0-9]+\_bw                 | bandwidth (GB/s) | Read phase bandwidth for the current benchmark run and one array size.                        |
| STREAM\_READ\_[float\|double]\_array\_[0-9]+\_time               | time (s)         | Read phase runtime for the current benchmark run and one array size.                          |
| STREAM\_COPY\_[float\|double]\_array\_[0-9]+\_bw                 | bandwidth (GB/s) | Maximum copy bandwidth for the current benchmark run and one array size.                       |
| STREAM\_COPY\_[float\|double]\_array\_[0-9]+\_time\_min          | time (s)         | Minimum copy runtime for the current benchmark run and one array size.                        |
| STREAM\_COPY\_[float\|double]\_array\_[0-9]+\_time\_max          | time (s)         | Maximum copy runtime for the current benchmark run and one array size.                        |
| STREAM\_COPY\_[float\|double]\_array\_[0-9]+\_time\_avg          | time (s)         | Average copy runtime for the current benchmark run and one array size.                        |
| STREAM\_MUL\_[float\|double]\_array\_[0-9]+\_bw                  | bandwidth (GB/s) | Maximum mul bandwidth for the current benchmark run and one array size.                       |
| STREAM\_MUL\_[float\|double]\_array\_[0-9]+\_time\_min           | time (s)         | Minimum mul runtime for the current benchmark run and one array size.                         |
| STREAM\_MUL\_[float\|double]\_array\_[0-9]+\_time\_max           | time (s)         | Maximum mul runtime for the current benchmark run and one array size.                         |
| STREAM\_MUL\_[float\|double]\_array\_[0-9]+\_time\_avg           | time (s)         | Average mul runtime for the current benchmark run and one array size.                         |
| STREAM\_ADD\_[float\|double]\_array\_[0-9]+\_bw                  | bandwidth (GB/s) | Maximum add bandwidth for the current benchmark run and one array size.                        |
| STREAM\_ADD\_[float\|double]\_array\_[0-9]+\_time\_min           | time (s)         | Minimum add runtime for the current benchmark run and one array size.                         |
| STREAM\_ADD\_[float\|double]\_array\_[0-9]+\_time\_max           | time (s)         | Maximum add runtime for the current benchmark run and one array size.                         |
| STREAM\_ADD\_[float\|double]\_array\_[0-9]+\_time\_avg           | time (s)         | Average add runtime for the current benchmark run and one array size.                         |
| STREAM\_TRIAD\_[float\|double]\_array\_[0-9]+\_bw                | bandwidth (GB/s) | Maximum triad bandwidth for the current benchmark run and one array size.                      |
| STREAM\_TRIAD\_[float\|double]\_array\_[0-9]+\_time\_min         | time (s)         | Minimum triad runtime for the current benchmark run and one array size.                       |
| STREAM\_TRIAD\_[float\|double]\_array\_[0-9]+\_time\_max         | time (s)         | Maximum triad runtime for the current benchmark run and one array size.                       |
| STREAM\_TRIAD\_[float\|double]\_array\_[0-9]+\_time\_avg         | time (s)         | Average triad runtime for the current benchmark run and one array size.                       |
| STREAM\_DOT\_[float\|double]\_array\_[0-9]+\_bw                  | bandwidth (GB/s) | Maximum dot bandwidth for the current benchmark run and one array size.                        |
| STREAM\_DOT\_[float\|double]\_array\_[0-9]+\_time\_min           | time (s)         | Minimum dot runtime for the current benchmark run and one array size.                         |
| STREAM\_DOT\_[float\|double]\_array\_[0-9]+\_time\_max           | time (s)         | Maximum dot runtime for the current benchmark run and one array size.                         |
| STREAM\_DOT\_[float\|double]\_array\_[0-9]+\_time\_avg           | time (s)         | Average dot runtime for the current benchmark run and one array size.                         |

`gpu-stream` reports `phase` and `function` metrics. `_ratio` and `block_*` metrics are removed.
Bandwidth metrics are converted from BabelStream `max_mbytes_per_sec` by using `GB/s = MB/s / 1000`.

### `ib-loopback`

#### Introduction

Measure the InfiniBand loopback verbs bandwidth, performed by
[OFED performance tests](https://github.com/linux-rdma/perftest/tree/7504ce48ac396a02f4d00de359257b2cb8458f06).

#### Metrics

| Name                                | Unit             | Description                                                  |
|-------------------------------------|------------------|--------------------------------------------------------------|
| ib-loopback/ib_write_bw_${msg_size} | bandwidth (GB/s) | InfiniBand loopback write bandwidth with given message size. |
| ib-loopback/ib_read_bw_${msg_size}  | bandwidth (GB/s) | InfiniBand loopback read bandwidth with given message size.  |
| ib-loopback/ib_send_bw_${msg_size}  | bandwidth (GB/s) | InfiniBand loopback send bandwidth with given message size.  |

### `nccl-bw` / `rccl-bw`

#### Introduction

Measure the performance of NCCL/RCCL operations under multi nodes' traffic pattern,
performed by [nccl-tests](https://github.com/NVIDIA/nccl-tests/tree/44df0bf010dcc95e840ca0fb7466c67cff3f1f0f)
or [rccl-tests](https://github.com/ROCmSoftwarePlatform/rccl-tests/tree/dc1ad4853d7ec738387d42a75a58a98d7af00c7b).
Support the following operations currently: allreduce, allgather, broadcast, reduce, reducescatter, alltoall.
Support both in-place and out-of-place measurements.

Support the following traffic patterns:
* `all-nodes`, validate the NCCL/RCCL performance across all VM nodes simultaneously.
* `pair-wise`, validate the NCCL/RCCL performance across VM pairs with all possible combinations in parallel.
* `k-batch`, validate the NCCL/RCCL performance across VM groups with a specified batch scale.
* `topo-aware`, validate the NCCL/RCCL performance across VM pairs with different distances/hops as a quick test.

#### Metrics

| Name                                   | Unit             | Description                                                 |
|----------------------------------------|------------------|-------------------------------------------------------------|
| nccl-bw/${operation}_${msg_size}_time  | time (us)        | NCCL operation lantency with given message size.            |
| nccl-bw/${operation}_${msg_size}_algbw | bandwidth (GB/s) | NCCL operation algorithm bandwidth with given message size. |
| nccl-bw/${operation}_${msg_size}_busbw | bandwidth (GB/s) | NCCL operation bus bandwidth with given message size.       |
| rccl-bw/${operation}_${msg_size}_time  | time (us)        | RCCL operation lantency with given message size.            |
| rccl-bw/${operation}_${msg_size}_algbw | bandwidth (GB/s) | RCCL operation algorithm bandwidth with given message size. |
| rccl-bw/${operation}_${msg_size}_busbw | bandwidth (GB/s) | RCCL operation bus bandwidth with given message size.       |

If mpi mode is enable and traffic pattern is specified, the metrics pattern will change to `nccl-bw/${operation}_${serial_index)_${parallel_index):${msg_size}_time`
- `serial_index` represents the serial index of the host group in serial.
- `parallel_index` represents the parallel index of the host list in parallel.

### `tcp-connectivity`

#### Introduction

Test the TCP connectivity between current node and nodes in the hostfile,
performed by [tcping](https://github.com/zhengxiaowai/tcping)

#### Metrics

| Metrics                                         | Unit      | Description                                                                           |
|-------------------------------------------------|-----------|---------------------------------------------------------------------------------------|
| tcp-connectivity/${hostname/ip}_successed_count | count     | successed times of tcp connections between current node and other nodes               |
| tcp-connectivity/${hostname/ip}_failed_count    | count     | failed times of tcp connections between current node and other nodes                  |
| tcp-connectivity/${hostname/ip}_success_rate    |           | success rate (successed/total) of tcp connection between current node and other nodes |
| tcp-connectivity/${hostname/ip}_time_min        | time (ms) | mininum latency of tcp connections between current node and other nodes               |
| tcp-connectivity/${hostname/ip}_time_max        | time (ms) | maximum latency of tcp connections between current node and other nodes               |
| tcp-connectivity/${hostname/ip}_time_avg        | time (ms) | average latency of tcp connections between current node and other nodes               |

### `gpcnet-network-test` / `gpcnet-network-load-test`

#### Introduction

Distributed test, test the global network performance and congestion,
performed by [GPCNET](https://github.com/netbench/GPCNET)

gpcnet-network-test: Full system network tests in random and natural ring, alltoall and allreduce, at least 2 nodes

gpcnet-network-load-test: Select full system network tests run with four congestors to measure network congestion or contention, at least 10 nodes

 - supporting network tests: RR Two-sided Lat (8 B), RR Get Lat (8 B), RR Two-sided BW (131072 B), RR Put BW (131072 B), RR Two-sided BW+Sync (131072 B), Nat Two-sided BW (131072 B), Multiple Allreduce (8 B), Multiple Alltoall (4096 B)
 - supporting congestors: Alltoall (4096 B), Two-sided Incast (4096 B), Put Incast (4096 B), Get Bcast (4096 B)

#### Metrics

| Metrics                                                 | Unit                   | Description                                                                                                                                                                |
|---------------------------------------------------------|------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| gpcnet-network-test/rr_two-sided_lat_${stat}            | time (us)              | statistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'random ring communication pattern two-side latency' for network testing                 |
| gpcnet-network-test/rr_two-sided+sync_bw_${stat}        | bandwidth (MiB/s/rank) | fstatistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'random ring communication pattern two-side bandwidth with barrier' for network testing |
| gpcnet-network-test/multiple_allreduce_time_${stat}     | time (us)              | statistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'multiple allreduce bandwidth' for network testing                                       |
| gpcnet-network-test/rr_get_lat_${stat}                  | bandwidth (MiB/s/rank) | statistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'RR GetLat (8 B)' for network testing                                                    |
| gpcnet-network-test/rr_two-sided_bw_${stat}             | bandwidth (MiB/s/rank) | statistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'RR Two-sidedBW (131072 B)' for network testing                                          |
| gpcnet-network-test/nat_two-sided_bw_${stat}            | bandwidth (MiB/s/rank) | statistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'Nat Two-sidedBW (131072 B)' for network testing                                         |
| gpcnet-network-test/multiple_alltoall_bw_${stat}        | bandwidth (MiB/s/rank) | statistical values(min, max, avg, 99%, 99.9%) obtained by all nodes use algorithm 'Multiple Alltoall (4096 B)' for network testing                                         |
| gpcnet-network-load-test/rr_two-sided_lat_x_${stat}     | factor (x)             | summary about congestion impact factor of the network test algorithm                                                                                                       |
| gpcnet-network-load-test/rr_two-sided+sync_bw_x_${stat} | factor (x)             | summary about congestion impact factor of the network test algorithm                                                                                                       |
| gpcnet-network-load-test/multiple_allreduce_x_${stat}   | factor (x)             | summary about congestion impact factor of the network test algorithm                                                                                                       |

### `ib-traffic`

#### Introduction

Measure the InfiniBand performance under multi nodes' traffic pattern.

The direction between client and server can be 'cpu-to-cpu'/'gpu-to-gpu'/'gpu-to-cpu'/'cpu-to-gpu'.

The traffic pattern is defined in a config file, which is pre-defined for one-to-many, many-to-one and all-to-all patterns.
Each row in the config is one round, and all pairs of nodes in a row run ib command simultaneously.

Besides the above three patterns, ib-traffic also supports topology-aware traffic pattern. To run ib-traffic with topology-aware
pattern, the user needs to specify 3 required (and 2 optional) parameters in YAML config file:
   - --pattern	 **topo-aware**
   - --ibstat	 **path to ibstat output**
   - --ibnetdiscover	 **path to ibnetdiscover output**
   - --min_dist	 **minimum distance of VM pairs (optional, default 2)**
   - --max_dist	 **maximum distance of VM pairs (optional, default 6)**

Each row in the config file has all VM pairs with a fixed distance (#hops). That's by default, 1st, 2nd, 3rd row has all VM pairs
with topology distance of 2, 4, 6, respectively.

#### Metrics

| Metrics                                                                                     | Unit             | Description                                                                                                                                                                                                                                                                                                                 |
|---------------------------------------------------------------------------------------------|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ib-traffic/ib\_write\_bw\_${msg_size}\_${direction}\_${line}\_${pair}:${server}\_${client}  | bandwidth (GB/s) | The max bandwidth of perftest (ib_write_bw, ib_send_bw, ib_read_bw) using ${msg_size} with ${direction}('cpu-to-cpu'/'gpu-to-gpu'/'gpu-to-cpu'/'cpu-to-gpu') run between the ${pair}<sup>th</sup> node pair in the ${line}<sup>th</sup> line of the config, ${server} and ${client} are the hostname of server and client.  |
| ib-traffic/ib\_write\_lat\_${msg_size}\_${direction}\_${line}\_${pair}:${server}\_${client} | time (us)        | The max latency of perftest (ib_write_lat, ib_send_lat, ib_read_lat) using ${msg_size} with ${direction}('cpu-to-cpu'/'gpu-to-gpu'/'gpu-to-cpu'/'cpu-to-gpu') run between the ${pair}<sup>th</sup> node pair in the ${line}<sup>th</sup> line of the config, ${server} and ${client} are the hostname of server and client. |

### `nvbandwidth`

#### Introduction

Measures bandwidth and latency for various memcpy patterns across different links using copy engine or kernel copy methods,
performed by [nvbandwidth](https://github.com/NVIDIA/nvbandwidth)

#### Metrics

| Metrics                                                 | Unit                   | Description                                                                                                                                                                |
|---------------------------------------------------------|------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| host_to_device_memcpy_ce_cpu[0-9]_gpu[0-9]_bw      | GB/s                | Host to device CE memcpy using cuMemcpyAsync                      |
| host_to_device_memcpy_ce_sum_bw                    | GB/s                | Sum of the output matrix                                           |
| device_to_host_memcpy_ce_cpu[0-9]_gpu[0-9]_bw      | GB/s                | Device to host CE memcpy using cuMemcpyAsync                      |
| device_to_host_memcpy_ce_sum_bw                    | GB/s                | Sum of the output matrix                                           |
| host_to_device_bidirectional_memcpy_ce_cpu[0-9]_gpu[0-9]_bw | GB/s      | A host to device copy is measured while a device to host copy is run simultaneously. Only the host to device copy bandwidth is reported. |
| host_to_device_bidirectional_memcpy_ce_sum_bw      | GB/s                | Sum of the output matrix                                           |
| device_to_host_bidirectional_memcpy_ce_cpu[0-9]_gpu[0-9]_bw | GB/s      | A device to host copy is measured while a host to device copy is run simultaneously. Only the device to host copy bandwidth is reported. |
| device_to_host_bidirectional_memcpy_ce_sum_bw      | GB/s                | Sum of the output matrix                                           |
| device_to_device_memcpy_read_ce_gpu[0-9]_gpu[0-9]_bw | GB/s               | Measures bandwidth of cuMemcpyAsync between each pair of accessible peers. Read tests launch a copy from the peer device to the target using the target's context. |
| device_to_device_memcpy_read_ce_sum_bw             | GB/s                | Sum of the output matrix                                           |
| device_to_device_memcpy_write_ce_gpu[0-9]_gpu[0-9]_bw | GB/s              | Measures bandwidth of cuMemcpyAsync between each pair of accessible peers. Write tests launch a copy from the target device to the peer using the target's context. |
| device_to_device_memcpy_write_ce_sum_bw            | GB/s                | Sum of the output matrix                                           |
| device_to_device_bidirectional_memcpy_read_ce_gpu[0-9]_gpu[0-9]_bw | GB/s | Measures bandwidth of cuMemcpyAsync between each pair of accessible peers. A copy in the opposite direction of the measured copy is run simultaneously but not measured. Read tests launch a copy from the peer device to the target using the target's context. |
| device_to_device_bidirectional_memcpy_read_ce_sum_bw | GB/s               | Sum of the output matrix                                           |
| device_to_device_bidirectional_memcpy_write_ce_gpu[0-9]_gpu[0-9]_bw | GB/s | Measures bandwidth of cuMemcpyAsync between each pair of accessible peers. A copy in the opposite direction of the measured copy is run simultaneously but not measured. Write tests launch a copy from the target device to the peer using the target's context. |
| device_to_device_bidirectional_memcpy_write_ce_sum_bw | GB/s               | Sum of the output matrix                                           |
| all_to_host_memcpy_ce_cpu[0-9]_gpu[0-9]_bw         | GB/s                | Measures bandwidth of cuMemcpyAsync between a single device and the host while simultaneously running copies from all other devices to the host. |
| all_to_host_memcpy_ce_sum_bw                       | GB/s                | Sum of the output matrix                                           |
| all_to_host_bidirectional_memcpy_ce_cpu[0-9]_gpu[0-9]_bw | GB/s              | A device to host copy is measured while a host to device copy is run simultaneously. Only the device to host copy bandwidth is reported. All other devices generate simultaneous host to device and device to host interfering traffic. |
| all_to_host_bidirectional_memcpy_ce_sum_bw         | GB/s                | Sum of the output matrix                                           |
| host_to_all_memcpy_ce_cpu[0-9]_gpu[0-9]_bw         | GB/s                | Measures bandwidth of cuMemcpyAsync between the host to a single device while simultaneously running copies from the host to all other devices. |
| host_to_all_memcpy_ce_sum_bw                       | GB/s                | Sum of the output matrix                                           |
| host_to_all_bidirectional_memcpy_ce_cpu[0-9]_gpu[0-9]_bw | GB/s              | A host to device copy is measured while a device to host copy is run simultaneously. Only the host to device copy bandwidth is reported. All other devices generate simultaneous host to device and device to host interfering traffic. |
| host_to_all_bidirectional_memcpy_ce_sum_bw         | GB/s                | Sum of the output matrix                                           |
| all_to_one_write_ce_gpu[0-9]_gpu[0-9]_bw           | GB/s                | Measures the total bandwidth of copies from all accessible peers to a single device, for each device. Bandwidth is reported as the total inbound bandwidth for each device. Write tests launch a copy from the target device to the peer using the target's context. |
| all_to_one_write_ce_sum_bw                         | GB/s                | Sum of the output matrix                                           |
| all_to_one_read_ce_gpu[0-9]_gpu[0-9]_bw            | GB/s                | Measures the total bandwidth of copies from all accessible peers to a single device, for each device. Bandwidth is reported as the total outbound bandwidth for each device. Read tests launch a copy from the peer device to the target using the target's context. |
| all_to_one_read_ce_sum_bw                          | GB/s                | Sum of the output matrix                                           |
| one_to_all_write_ce_gpu[0-9]_gpu[0-9]_bw           | GB/s                | Measures the total bandwidth of copies from a single device to all accessible peers, for each device. Bandwidth is reported as the total outbound bandwidth for each device. Write tests launch a copy from the target device to the peer using the target's context. |
| one_to_all_write_ce_sum_bw                         | GB/s                | Sum of the output matrix                                           |
| one_to_all_read_ce_gpu[0-9]_gpu[0-9]_bw            | GB/s                | Measures the total bandwidth of copies from a single device to all accessible peers, for each device. Bandwidth is reported as the total inbound bandwidth for each device. Read tests launch a copy from the peer device to the target using the target's context. |
| one_to_all_read_ce_sum_bw                          | GB/s                | Sum of the output matrix                                           |
| host_to_device_memcpy_sm_cpu[0-9]_gpu[0-9]_bw      | GB/s                | Host to device SM memcpy using a copy kernel                      |
| host_to_device_memcpy_sm_sum_bw                    | GB/s                | Sum of the output matrix                                           |
| device_to_host_memcpy_sm_cpu[0-9]_gpu[0-9]_bw      | GB/s                | Device to host SM memcpy using a copy kernel                      |
| device_to_host_memcpy_sm_sum_bw                    | GB/s                | Sum of the output matrix                                           |
| device_to_device_memcpy_read_sm_gpu[0-9]_gpu[0-9]_bw | GB/s               | Measures bandwidth of a copy kernel between each pair of accessible peers. Read tests launch a copy from the peer device to the target using the target's context. |
| device_to_device_memcpy_read_sm_sum_bw             | GB/s                | Sum of the output matrix                                           |
| device_to_device_memcpy_write_sm_gpu[0-9]_gpu[0-9]_bw | GB/s              | Measures bandwidth of a copy kernel between each pair of accessible peers. Write tests launch a copy from the target device to the peer using the target's context. |
| device_to_device_memcpy_write_sm_sum_bw            | GB/s                | Sum of the output matrix                                           |
| device_to_device_bidirectional_memcpy_read_sm_gpu[0-9]_gpu[0-9]_bw | GB/s | Measures bandwidth of a copy kernel between each pair of accessible peers. Copies are run in both directions between each pair, and the sum is reported. Read tests launch a copy from the peer device to the target using the target's context. |
| device_to_device_bidirectional_memcpy_read_sm_sum_bw | GB/s               | Sum of the output matrix                                           |
| device_to_device_bidirectional_memcpy_write_sm_gpu[0-9]_gpu[0-9]_bw | GB/s | Measures bandwidth of a copy kernel between each pair of accessible peers. Copies are run in both directions between each pair, and the sum is reported. Write tests launch a copy from the target device to the peer using the target's context. |
| device_to_device_bidirectional_memcpy_write_sm_sum_bw | GB/s               | Sum of the output matrix                                           |
| all_to_host_memcpy_sm_cpu[0-9]_gpu[0-9]_bw         | GB/s                | Measures bandwidth of a copy kernel between a single device and the host while simultaneously running copies from all other devices to the host. |
| all_to_host_memcpy_sm_sum_bw                       | GB/s                | Sum of the output matrix                                           |
| all_to_host_bidirectional_memcpy_sm_cpu[0-9]_gpu[0-9]_bw | GB/s              | A device to host bandwidth of a copy kernel is measured while a host to device copy is run simultaneously. Only the device to host copy bandwidth is reported. All other devices generate simultaneous host to device and device to host interfering traffic using copy kernels. |
| all_to_host_bidirectional_memcpy_sm_sum_bw         | GB/s                | Sum of the output matrix                                           |
| host_to_all_memcpy_sm_cpu[0-9]_gpu[0-9]_bw         | GB/s                | Measures bandwidth of a copy kernel between the host to a single device while simultaneously running copies from the host to all other devices. |
| host_to_all_memcpy_sm_sum_bw                       | GB/s                | Sum of the output matrix                                           |
| host_to_all_bidirectional_memcpy_sm_cpu[0-9]_gpu[0-9]_bw | GB/s              | A host to device bandwidth of a copy kernel is measured while a device to host copy is run simultaneously. Only the host to device copy bandwidth is reported. All other devices generate simultaneous host to device and device to host interfering traffic using copy kernels. |
| host_to_all_bidirectional_memcpy_sm_sum_bw         | GB/s                | Sum of the output matrix                                           |
| all_to_one_write_sm_gpu[0-9]_gpu[0-9]_bw           | GB/s                | Measures the total bandwidth of copies from all accessible peers to a single device, for each device. Bandwidth is reported as the total inbound bandwidth for each device. Write tests launch a copy from the target device to the peer using the target's context. |
| all_to_one_write_sm_sum_bw                         | GB/s                | Sum of the output matrix                                           |
| all_to_one_read_sm_gpu[0-9]_gpu[0-9]_bw            | GB/s                | Measures the total bandwidth of copies from all accessible peers to a single device, for each device. Bandwidth is reported as the total outbound bandwidth for each device. Read tests launch a copy from the peer device to the target using the target's context. |
| all_to_one_read_sm_sum_bw                          | GB/s                | Sum of the output matrix                                           |
| one_to_all_write_sm_gpu[0-9]_gpu[0-9]_bw           | GB/s                | Measures the total bandwidth of copies from a single device to all accessible peers, for each device. Bandwidth is reported as the total outbound bandwidth for each device. Write tests launch a copy from the target device to the peer using the target's context. |
| one_to_all_write_sm_sum_bw                         | GB/s                | Sum of the output matrix                                           |
| one_to_all_read_sm_gpu[0-9]_gpu[0-9]_bw            | GB/s                | Measures the total bandwidth of copies from a single device to all accessible peers, for each device. Bandwidth is reported as the total inbound bandwidth for each device. Read tests launch a copy from the peer device to the target using the target's context. |
| one_to_all_read_sm_sum_bw                          | GB/s                | Sum of the output matrix                                           |
| host_device_latency_sm_cpu[0-9]_gpu[0-9]_lat       | µs                  | Host - device SM copy latency using a ptr chase kernel            |
| host_device_latency_sm_sum_lat                     | µs                  | Sum of the output matrix                                           |
| device_to_device_latency_sm_gpu[0-9]_gpu[0-9]_lat  | µs                  | Measures latency of a pointer dereference operation between each pair of accessible peers. Memory is allocated on a GPU and is accessed by the peer GPU to determine latency. |
| device_to_device_latency_sm_sum_lat                | µs                  | Sum of the output matrix                                           |


## Computation-communication Benchmarks

### `computation-communication-overlap`

#### Introduction

Test the performance of single node when communication and computation overlap.

#### Metrics

| Name                                                  | Unit      | Description                                                  |
|-------------------------------------------------------|-----------|--------------------------------------------------------------|
| pytorch-computation-communication-overlap/mul_time    | time (ms) | Time of communication and mul kernel computation overlap.    |
| pytorch-computation-communication-overlap/matmul_time | time (ms) | Time of communication and matmul kernel computation overlap. |

####

### `sharding-matmul`

#### Introduction

Test the performance of large scale matmul operation with multiple GPUs:
* allreduce: Each GPU will calculate part of the MM calculation, and use AllReduce to merge all data into one tensor.
* allgather: Each GPU will calculate part of the MM calculation, and use AllGather + Concat to merge all data into one tensor.

#### Metrics

| Name                                   | Unit      | Description                              |
|----------------------------------------|-----------|------------------------------------------|
| pytorch-sharding-matmul/allreduce_time | time (ms) | Time of sharding matmul using allreduce. |
| pytorch-sharding-matmul/allgather_time | time (ms) | Time of sharding matmul using allgather. |

### `dist-inference`

#### Introduction

Test the performance of distributed model inference. Support both PyTorch implementation and cpp implementation.

#### Parameters

| Parameter                | Default     | Description                                                                 |
|--------------------------|-------------|-----------------------------------------------------------------------------|
| `--use_pytorch`          | `false`     | Use the PyTorch implementation. If omitted, the C++ implementation is used. |
| `--batch_size`           | `64`        | Batch size of the generated input tensor.                                   |
| `--input_size`           | `1024`      | Input dimension of the synthetic model.                                     |
| `--hidden_size`          | `1024`      | Hidden dimension of the synthetic model.                                    |
| `--alpha`                | `1.0`       | Alpha coefficient for `D = alpha * (A * B) + beta * C`.                    |
| `--beta`                 | `1.0`       | Beta coefficient for `D = alpha * (A * B) + beta * C`.                     |
| `--num_layers`           | `1`         | Number of repeated compute-communicate-activate layers.                     |
| `--computation_kernel`   | `matmul`    | Computation kernel: `addmm`, `matmul`, or `mul`.                           |
| `--communication_kernel` | `allreduce` | Communication kernel: `allgather`, `allreduce`, or `alltoall`.             |
| `--activation_kernel`    | `relu`      | Activation kernel: `relu`, `sigmoid`, or `tanh`.                           |
| `--precision`            | `float32`   | Model precision, such as `float32` or `float16`.                           |
| `--num_warmup`           | `50`        | Number of warmup steps excluded from metrics.                              |
| `--num_steps`            | `10000`     | Number of measured inference steps.                                        |
| `--distributed_impl`     | `ddp`       | Distributed implementation for the PyTorch path.                           |
| `--distributed_backend`  | `nccl`      | Distributed backend for the PyTorch path.                                  |
| `--use_cuda_graph`       | `false`     | Launch kernels in CUDA graph mode when supported.                          |
| `--tune_gemm`            | `false`     | Tune GEMM performance before measurement in the C++ implementation.         |

#### Metrics

| Name                                            | Unit      | Description                                           |
|-------------------------------------------------|-----------|-------------------------------------------------------|
| pytorch-dist-inference/step_times               | time (ms) | Average time of model inference runs.                 |
| pytorch-dist-inference/step_times_${percentile} | time (ms) | Tail (50,90,95,99,99.9) time of model inference runs. |

## Storage Benchmarks

### `disk-benchmark`

#### Introduction

Measure the disk performance through [FIO](https://github.com/axboe/fio/tree/0313e938c9c8bb37d71dade239f1f5326677b079).

#### Metrics

| Name                                                          | Unit         | Description                                              |
|---------------------------------------------------------------|--------------|----------------------------------------------------------|
| disk-benchmark/${disk_name}_rand_read_write_bs                | size (bytes) | Disk random read write block size.                       |
| disk-benchmark/${disk_name}_rand_read_write_read_iops         | IOPS         | Disk random read write read IOPS.                        |
| disk-benchmark/${disk_name}_rand_read_write_read_lat_ns_95.0  | time (ns)    | Disk random read write read latency in 95.0 percentile.  |
| disk-benchmark/${disk_name}_rand_read_write_read_lat_ns_99.0  | time (ns)    | Disk random read write read latency in 99.0 percentile.  |
| disk-benchmark/${disk_name}_rand_read_write_read_lat_ns_99.9  | time (ns)    | Disk random read write read latency in 99.9 percentile.  |
| disk-benchmark/${disk_name}_rand_read_write_write_iops        | IOPS         | Disk random read write write IOPS.                       |
| disk-benchmark/${disk_name}_rand_read_write_write_lat_ns_95.0 | time (ns)    | Disk random read write write latency in 95.0 percentile. |
| disk-benchmark/${disk_name}_rand_read_write_write_lat_ns_99.0 | time (ns)    | Disk random read write write latency in 99.0 percentile. |
| disk-benchmark/${disk_name}_rand_read_write_write_lat_ns_99.9 | time (ns)    | Disk random read write write latency in 99.9 percentile. |