cutlass/media/docs/quickstart.md

![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "CUTLASS Quick Start Guide")

[README](/README.md#documentation) > **Quick Start**

# Quickstart

## Prerequisites

CUTLASS requires:
- NVIDIA CUDA Toolkit (9.2 or later required, 10.2 recommended)
- CMake 3.12+
- host compiler supporting C++11 or greater (g++ 7.3.0 or Microsoft Visual Studio 2015 recommended)
- Python 3.5+

## Initial build steps

Construct a build directory and run CMake.
```bash
$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc

$ mkdir build && cd build

$ cmake .. -DCUTLASS_NVCC_ARCHS=75               # compiles for NVIDIA's Turing GPU architecture
```

## Clang

For experimental purposes, CUTLASS may be compiled with 
[clang 8.0](https://github.com/llvm/llvm-project/releases/download/llvmorg-8.0.1/clang+llvm-8.0.1-amd64-unknown-freebsd11.tar.xz) using the 
[CUDA 10.0 Toolkit](https://developer.nvidia.com/cuda-10.0-download-archive).
At this time, compiling with clang enables the CUTLASS SIMT GEMM kernels (sgemm, dgemm, hgemm, igemm)
but does not enable TensorCores.

```bash
$ mkdir build && cd build

$ cmake -DCUDA_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..

$ make test_unit -j
```

## Build and run the CUTLASS Profiler

From the `build/` directory created above, compile the the CUTLASS Profiler.
```bash
$ make cutlass_profiler -j12
```

Then execute the CUTLASS Profiler for a set of problem sizes.
```bash
$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=4352 --n=4096 --k=4096

=============================
  Problem ID: 1

    Provider: CUTLASS
   Operation: cutlass_simt_sgemm_128x128_nn

 Disposition: Passed
      Status: Success

   Arguments:  --m=4352 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0  \
               --split_k_slices=1 --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8  \
               --stages=2 --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50  \
               --max_cc=1024

       Bytes: 52428800  bytes
       FLOPs: 146064539648  flops

     Runtime: 10.5424  ms
      Memory: 4.63158 GiB/s

        Math: 13854.9 GFLOP/s
```

See [documentation for the CUTLASS Profiler](profiler.md) for more details.

## Build and run CUTLASS Unit Tests

From the `build/` directory created above, simply build the target `test_unit` to compile and run
all unit tests.

```bash
$ make test_unit -j
...
...
...
[----------] Global test environment tear-down
[==========] 946 tests from 57 test cases ran. (10812 ms total)
[  PASSED  ] 946 tests.
$
```

No tests should fail.

## Using CUTLASS within other applications

Applications should list [`/include`](/include) within their include paths. They must be
compiled as C++11 or greater.

**Example:** print the contents of a variable storing half-precision data.
```c++
#include <iostream>
#include <cutlass/cutlass.h>
#include <cutlass/numeric_types.h>

int main() {

  cutlass::half_t x = 2.25_hf;

  std::cout << x << std::endl;

  return 0;
}
```

## Launching a GEMM kernel

**Example:** launch a mixed-precision GEMM targeting Volta Tensor Cores.
```c++
#include <cutlass/numeric_types.h>
#include <cutlas/gemm/device/gemm.h>
#include <cutlass/util/host_tensor.h>

int main() {

  // Define the GEMM operation
  using Gemm = cutlass::gemm::device::Gemm<
    cutlass::half_t,                           // ElementA
    cutlass::layout::ColumnMajor,              // LayoutA
    cutlass::half_t,                           // ElementB
    cutlass::layout::ColumnMajor,              // LayoutB
    cutlass::half_t,                           // ElementOutput
    cutlass::layout::ColumnMajor,              // LayoutOutput
    float,                                     // ElementAccumulator
    cutlass::arch::OpClassTensorOp,            // tag indicating Tensor Cores
    cutlass::arch::Sm70                        // tag indicating target GPU compute architecture
  >;

  Gemm gemm_op;
  cutlass::Status status;

  //
  // Define the problem size
  //
  int M = 512;
  int N = 256;
  int K = 128;

  float alpha = 1.25f;
  float beta = -1.25f;

  //
  // Allocate device memory
  //

  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> A({M, K});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> B({K, N});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> C({M, N});

  cutlass::half_t const *ptrA = A.device_data();
  cutlass::half_t const *ptrB = B.device_data();
  cutlass::half_t const *ptrC = C.device_data();
  cutlass::half_t       *ptrD = C.device_data();

  int lda = A.device_ref().stride(0);
  int ldb = B.device_ref().stride(0);
  int ldc = C.device_ref().stride(0);
  int ldd = D.device_ref().stride(0);
  //
  // Launch GEMM on the device
  //
 
  status = gemm_op({
    {M, N, K},
    {ptrA, lda},            // TensorRef to A device tensor
    {ptrB, ldb},            // TensorRef to B device tensor
    {ptrC, ldc},            // TensorRef to C device tensor
    {ptrD, ldd},            // TensorRef to D device tensor - may be the same as C
    {alpha, beta}           // epilogue operation arguments
  });

  if (status != cutlass::Status::kSuccess) {
    return -1;
  }

  return 0;
}
```

Note, the above could be simplified as follows using helper methods defined in `HostTensor`.
```c++
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> A({M, K});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> B({K, N});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> C({M, N});

  //
  // Use the TensorRef returned by HostTensor::device_ref().
  // 

  status = gemm_op({
    {M, N, K},
    A.device_ref(),            // TensorRef to A device tensor
    B.device_ref(),            // TensorRef to B device tensor
    C.device_ref(),            // TensorRef to C device tensor
    C.device_ref(),            // TensorRef to D device tensor - may be the same as C
    {alpha, beta}              // epilogue operation arguments
  });
```

# Copyright

Copyright (c) 2017-2019, NVIDIA CORPORATION.  All rights reserved.

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        provided with the distribution.
      * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
        to endorse or promote products derived from this software without specific prior written
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  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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