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README > CUTLASS Profiler

CUTLASS Profiler

The CUTLASS Profiler is a command-line driven test and profiling environment for CUTLASS computations defined in the CUTLASS Instance Library. The CUTLASS Profiler is capable of executing each GEMM, Sparse Gemm, Conv2d, and Conv3d kernel.

The CUTLASS Profiler may be compiled with:

$ make cutlass_profiler -j

To limit compilation time, only one tile size (typically 128x128) and threadblock cluster size (typically 2x1x1) is instantiated for each data type, math instruction, and layout. To instantiate all sizes, set the following environment variable when running CMake from an empty build/ directory.

$ cmake .. -DCUTLASS_NVCC_ARCHS="70;75;80" -DCUTLASS_LIBRARY_KERNELS=all  -DCUTLASS_UNITY_BUILD_ENABLED=ON
...
$ make cutlass_profiler -j

Enabling the unity build places multiple kernel instances in one compilation unit, thereby reducing size of the compiled binary and avoiding linker limitations on some platforms.

Instantiating more kernels with Hopper

With Hopper (SM90), you will need to use an additional flag, CUTLASS_LIBRARY_INSTANTIATION_LEVEL, in order to instantiate all possible combinations, which unlike previous architectures, will be in the order of millions of kernels. Due to this, CUTLASS_LIBRARY_KERNELS must be non-empty, since generating and filtering these kernels alone can take hours. You must also exercise caution, because not all of these configs are tested, and some may fail to compile or fail to launch at runtime.

$ cmake .. \
  -DCUTLASS_NVCC_ARCHS="90a" \
  -DCUTLASS_LIBRARY_KERNELS="cutlass3x_sm90_tensorop_s64x64x16gemm_f16_f16_f32_void_f32_*" \
  -DCUTLASS_LIBRARY_INSTANTIATION_LEVEL="max" \
  -DCUTLASS_UNITY_BUILD_ENABLED=ON

The CUTLASS profiler employs a four-digit integer level (global instantiation level) mechanism to manage the generation of kernel configurations. This global instantiation level decides the behavior of multiple "generators" by defining how many and which combinations of configurations are produced. If a global instantiation level contains fewer than four digits, it can be padded with leading zeros to ensure it is four digits long. Each of the four digits in the global level corresponds to a specific category that influences kernel generation, from right to left:

  1. Instruction Shape
  2. MMA Shape Multiplier
  3. Cluster Shape
  4. Schedule Pruning

Cluster shape levels define the number of CTAs (Cooperative Thread Arrays) included in the kernel generation:

  • Level 0: Only (1, 2, 1) cluster shape.
  • Level 1: Clusters with 2 CTAs.
  • Level 2: Clusters with 1 or 2 CTAs.
  • Level 3: Clusters with 1, 2, or 4 CTAs.
  • Level 4: Clusters with 1, 2, 4, or 8 CTAs.
  • Level 5: Clusters with 1, 2, 4, 8, or 16 CTAs.

The MMA multipliers are combined with MMA instruction shapes (WGMMA shapes) to form CTA shapes. The levels for MMA multipliers determine the configurations generated for different data types.

  • Levels [0, 3]: Control the specific configurations generated for various data types.
  • Level 9: Activates exhaustive mode, generating all possible configurations.

Higher levels encompass a broader range of CTA configurations, resulting in more comprehensive kernel generation.

Instruction shape levels control the selection of WGMMA shapes used in kernel generation:

  • Level 0: Generates the "default" shape only.
  • Level 1: Includes additional shapes for unpruned cases, specifically for TF32 data type.
  • Level 2: Includes shapes that are powers of 2.
  • Level 3: Includes all other shapes.

The detailed defination of the three instantiation levels controlling cluster shape, MMA shape multiplier, and instruction shape can be found in sm90_shapes.py.

Schedule pruning levels decide the epilogue schedule and mainloop schedule to stamp out a kernel instance. As defined in get_valid_schedules in sm90_utils.py,

  • Level >= 1: Indicates that no pruning is being applied.
  • Level 0: Indicates pruning according to existing generator.py behavior.

An instantiation level 500, which is padded to 0500, thus indicates:

  • Instruction Shapes: At level 0, generating only the "default" shape.
  • MMA Multipliers: At level 0, generating only one multiplier, (2, 1, 4).
  • Cluster Sizes: At level 5, allowing for clusters with 1, 2, 4, 8, or 16 CTAs.
  • Schedule Pruning: At level 0, where pruning is applied according to the existing generator.py behavior.

The CUTLASS Profiler sources are stored in:

tools/
  profiler/

The CUTLASS Profiler usage statement may be obtained by executing cutlass_profiler --help and appears as follows.

CUTLASS Performance Tool
usage:

    cutlass_profiler [options]

  --help

  --mode=<string>                                  Cutlass profiler execution mode.
                                                    --mode=profile    regular verification and profiling (default)
                                                    --mode=dry_run    no kernels are launched or workspaces allocated
                                                    --mode=enumerate  lists all operation kind and operations
                                                    --mode=trace      executes a single device-side computation with
                                                                       no other kernel launches

  --device-info                                    Prints information on all GPUs present in the system

  --operation=<operation_kind>                     CUTLASS operation to profile.

  --kernels=<string_list>                          Filter operations by kernel names. For example, call all kernels with
                                                   ("s1688" and "nt") or ("s844" and "tn" and "align8") in their
                                                   operation name using --kernels="s1688*nt, s884*tn*align8"

  --ignore-kernels=<string_list>                   Excludes kernels whose names match anything in this list.

Device:
  --device=<int>                                   CUDA Device ID

  --compute-capability=<int>                       Override the compute capability.

  --llc-capacity=<capacity in KiB>                 Capacity of last-level cache in kilobytes. If this is non-zero,
                                                   profiling phases cycle through different input tensors to induce
                                                   capacity misses in the L2.

  --allocations=<name>:<device>,<name>:<device>    Pairs of allocation names to devices. If <device> is negative,
                                                   the execution device is used


Initialization:
  --initialization=<bool>                          Enables initialization (default: true). If false, device memory is
                                                   not initialized after allocation.

  --initialization-provider=<provider>             Selects initialization provider {host, device*}. (default: '*')

  --dist=<distribution>                            Data distribution of input tensors {uniform*, gaussian, identity, sequential}
                                                    --dist=uniform,min:<double>,max:<double>,scale:<integer>
                                                    --dist=gaussian,mean:<double>,stddev:<double>,scale:<integer>
                                                    --dist=sequential,start:<double>,delta:<double>,scale:<integer>
                                                    --dist=identity

  --seed=<int>                                     Random number generator seed. Used to enforce deterministic
                                                   initialization.


Library:
  --library-algo-mode=<mode>                       Indicates algorithm mode used to call libraries such as cuBLAS and cuDNN.
                                                   mode={default*,matching,best}

  --library-algos=<range-list>                     If --algorithm-mode=best, permits specifying a selection of algorithms.


Profiling:
  --workspace-count=<workspace count>              Number of discrete workspaces maintained to avoid cache-resident
                                                 If zero (default), the amount is chosen for each workload based on
                                                 capacity of the last-level cache.

  --profiling-iterations=<iterations>              Number of iterations to profile each kernel. If zero, kernels
                                                   are launched up to the profiling duration.

  --warmup-iterations=<iterations>                 Number of iterations to execute each kernel prior to profiling.

  --sleep-duration=<duration>                      Number of ms to sleep between profiling periods (ms).

  --profiling-enabled=<bool>                       If true, profiling is actually conducted.

Verification:
  --verification-enabled=<bool>                    Whether to perform verification checks.

  --epsilon=<error>                                Error threshold. Setting to zero (default) requires
                                                   bit-level equivalence.

  --nonzero-floor=<floor>                          Results whose absolute value is less than this quantity
                                                   are treated as zero for comparisons.

  --save-workspace=<string>                        Specifies when to save the GEMM inputs and results to the filesystem.
                                                    --save-workspace=never      never save workspace (default)
                                                    --save-workspace=incorrect  save workspace for incorrect results
                                                    --save-workspace=always     always save workspace

  --verification-providers=<providers>             List of providers used to verify result. (default: '*')
                                                   Gemm verification-providers {cublas*}
                                                   Conv2d verification-providers {cudnn*, device*, host}


Report:
  --append=<bool>                                  If true, result is appended to possibly existing file. Otherwise,
                                                   any existing file is overwritten.

  --output=<path>                                  Path to output file for machine readable results. Operation kind and '.csv' is appended.

  --junit-output=<path>                            Path to junit output file for result reporting. Operation kind and '.junit.xml' is appended.

  --report-not-run=<bool>                          If true, reports the status of all kernels including those that
                                                   do not satisfy the given arguments.

  --tags=<column:tag,...>                          Inserts leading columns in output table and uniform values for each
                                                   column. Useful for generating pivot tables.

  --verbose=<bool>                                 Prints human-readable text to stdout. If false, nothing is written to stdout.


About:
  --version                                        CUTLASS 2.4.0 built on Nov 19 2020 at 11:59:00


Operations:

     gemm                                          General matrix-matrix product. D = alpha * A*B + beta * C
     spgemm                                        Structured sparse GEMM. D = alpha * A*B + beta * C
     conv2d                                        Conv2d operation. Output(Tensor4D) = alpha * Input(Tensor4D) * Filter(Tensor4D) + beta * Input(Tensor4D)
     conv3d                                        Conv3d operation. Output(Tensor5D) = alpha * Input(Tensor5D) * Filter(Tensor5D) + beta * Input(Tensor5D)


For details about a particular function, specify the function name with --help.

Example:

  $ cutlass_profiler --operation=Gemm --help

  $ cutlass_profiler --operation=Conv3d --help

  $ cutlass_profiler --operation=Conv2d --help

GEMM

The CUTLASS Profiler is capable of executing GEMM and Sparse GEMM problems.

The CUTLASS Profiler can be built with cuBLAS enabled to use as a reference implementation. If CMake detects the cuBLAS library available in the system, it is included as a dependency. This may be explicitly overridden with CMake flag CUTLASS_ENABLE_CUBLAS.

GEMM Arguments

The complete set of arguments available to each operation may be viewed by specifying the operation name in addition to --help. The argument flags and their aliases usable for GEMM appear as follows.

$ ./tools/profiler/cutlass_profiler --operation=gemm --help

GEMM

  [enum]      --gemm_kind                                       Variant of GEMM (e.g. universal, gemm, planar_complex, planar_complex_array)
  [int]       --m,--problem-size::m                             M dimension of the GEMM problem space
  [int]       --n,--problem-size::n                             N dimension of the GEMM problem space
  [int]       --k,--problem-size::k                             K dimension of the GEMM problem space
  [tensor]    --A                                               Tensor storing the A operand
  [tensor]    --B                                               Tensor storing the B operand
  [tensor]    --C                                               Tensor storing the C operand
  [scalar]    --alpha,--epilogue::alpha                         Epilogue scalar alpha
  [scalar]    --beta,--epilogue::beta                           Epilogue scalar beta
  [enum]      --split_k_mode,--split-k-mode                     Variant of split K mode(serial, parallel)
  [int]       --split_k_slices,--split-k-slices                 Number of partitions of K dimension
  [int]       --batch_count,--batch-count                       Number of GEMMs computed in one batch
  [enum]      --op_class,--opcode-class                         Class of math instruction (simt, tensorop, wmmatensorop, wmma).
  [enum]      --accum,--accumulator-type                        Math instruction accumulator data type
  [int]       --cta_m,--threadblock-shape::m                    Threadblock shape in the M dimension
  [int]       --cta_n,--threadblock-shape::n                    Threadblock shape in the N dimension
  [int]       --cta_k,--threadblock-shape::k                    Threadblock shape in the K dimension
  [int]       --cluster_m,--cluster-shape::m                    Cluster shape in the M dimension
  [int]       --cluster_n,--cluster-shape::n                    Cluster shape in the N dimension
  [int]       --cluster_k,--cluster-shape::k                    Cluster shape in the K dimension
  [int]       --stages,--threadblock-stages                     Number of stages of threadblock-scoped matrix multiply
  [int]       --warps_m,--warp-count::m                         Number of warps within threadblock along the M dimension
  [int]       --warps_n,--warp-count::n                         Number of warps within threadblock along the N dimension
  [int]       --warps_k,--warp-count::k                         Number of warps within threadblock along the K dimension
  [int]       --inst_m,--instruction-shape::m                   Math instruction shape in the M dimension
  [int]       --inst_n,--instruction-shape::n                   Math instruction shape in the N dimension
  [int]       --inst_k,--instruction-shape::k                   Math instruction shape in the K dimension
  [int]       --min_cc,--minimum-compute-capability             Minimum device compute capability
  [int]       --max_cc,--maximum-compute-capability             Maximum device compute capability
  [enum]      --raster_order={heuristic|H|along_m|M|along_n|N}  If supported by kernel, sets the tile raster direction
  [int]       --swizzle_size={1,2,4,8}                          If supported by kernel, sets the 2D tile swizzle extent (In Hopper, other values will be rounded down to the nearest supported value)
Examples:

Profile a particular problem size:
  $ cutlass_profiler --operation=Gemm --m=1024 --n=1024 --k=128

Schmoo over problem size and beta:
  $ cutlass_profiler --operation=Gemm --m=1024:4096:256 --n=1024:4096:256 --k=128:8192:128 --beta=0,1,2.5

Schmoo over accumulator types:
  $ cutlass_profiler --operation=Gemm --accumulator-type=f16,f32

Run when A is f16 with column-major and B is any datatype with row-major (For column major, use column, col, or n. For row major use, row or t):
  $ cutlass_profiler --operation=Gemm --A=f16:column --B=*:row

Using various input value distribution:
  $ cutlass_profiler --operation=Gemm --dist=uniform,min:0,max:3
  $ cutlass_profiler --operation=Gemm --dist=gaussian,mean:0,stddev:3
  $ cutlass_profiler --operation=Gemm --dist=sequential,start:0,delta:1

Using CUTLASS 3.x GEMM kernel with a tile scheduler that supports runtime tile remapping and raster mode order:
  $ cutlass_profiler --operation=Gemm --m=2048 --n=2048 --k=2048 --raster_order=M --swizzle_size=2

Run a kernel with cta tile size of 256x128x32 and save workspace if results are incorrect (note that --cta-tile::k=32 is default cta-tile size):
 $ cutlass_profiler --operation=Gemm --cta_m=256 --cta_n=128  --cta_k=32 --save-workspace=incorrect

Test your changes to gemm kernels with a quick functional test and save results in functional-test.csv:
 $ cutlass_profiler  --operation=Gemm \
   --m=8,56,120,136,256,264,512,520,1024,1032,4096,8192,16384 \
   --n=8,56,120,136,256,264,512,520,1024,1032,4096,8192,16384 \
   --k=8,16,32,64,128,256,288,384,504,512,520 \
   --beta=0,1,2 --profiling-iterations=1 \
   --providers=cutlass --output=functional-test.csv

Profile when execution is performed on device 0 and the C tensor is located on a device 1 and D on device 2:
  $ cutlass_profiler --device=0 --allocations=C:1,D:2 --operation=Gemm --m=1024 --n=1024 --k=128

The format of tensor argument is followed by <type>:<layout>. The type could be f32 as 32-bit floating point, s8 as 8-bit signed integer, etc. The available types can be referred to the NumericTypeID_enumerants in util.cu. The layout could be row or column.

Example CUDA Core GEMM Operation

Example command line for profiling SGEMM kernels is as follows:

$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096



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

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1

          Status: Success
    Verification: ON
     Disposition: Passed

          cuBLAS: Passed

       Arguments: --m=3456 --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=4  \
                  --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024

           Bytes: 180355072  bytes
           FLOPs: 115992428544  flops

         Runtime: 6.73655  ms
          Memory: 24.934 GiB/s

            Math: 17218.4 GFLOP/s

Note, the arguments which appear in the output may be used as command line parameters for subsequent invocations.

Example Tensor Core GEMM Operations

To execute kernels targeting Tensor Core operations, supply the flag --op_class=tensorop in the command line.

$ ./tools/profiler/cutlass_profiler --op_class=tensorop --m=3456 --n=4096 --k=8192



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

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass_tensorop_s16816gemm_f16_256x128_32x3_nn_align8

          Status: Success
    Verification: ON
     Disposition: Passed

          cuBLAS: Passed

       Arguments: --m=3456 --n=4096 --k=8192 --A=f16:column --B=f16:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1  \
                  --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128 --cta_k=32 --stages=3 --warps_m=4  \
                  --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024

           Bytes: 180355072  bytes
           FLOPs: 231956545536  flops

         Runtime: 0.98647  ms
          Memory: 170.272 GiB/s

            Math: 235138 GFLOP/s

Covering the problem space

All arguments may have single values or comma-delimited set of values. Integers may also be specified as an inclusive range with the following syntax start:end:increment or simply start:end.

For example, the following sweeps over the range of the GEMM K dimension from 8 to 4096 in increments of 8 elements.

$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn --m=4352 --n=4096 --k=8:4096:8

Output

By default, runtime and computed GFLOP/s are reported for each operation and problem size. Additionally, a table of comma separated values are reported at the end of the execution. This may be output to a file with the --output=<filename.csv> command line option as shown:

$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn            \
                                    --m=3456 --n=4096 --k=8:4096:8 --output=report.csv

To faclitate generation of pivot tables and charts, additional columns may be prepended with the --tags=<column>:<value> option. One or more tags may be specified using a comma-delimited list.

$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn            \
                                    --m=3456 --n=4096 --k=8:4096:8 --output=report.csv \
                                    --tags=cutlass:2.2,date:2020-06-08

CUTLASS 3.0 GEMM procedural names

CUTLASS 3.0 introduces a new naming convention for GEMMs used by the profiler targeting the NVIDIA Hopper architecture and beyond so as to indicate new features of the kernel within the name (e.g., the cluster shape).

To best illustrate this naming convention, we will walk through the meaning of each of the components in a GEMM kernel used by the profiler:

cutlass3x_sm90_tensorop_s64x128x16gemm_f16_f16_f32_f16_f32_128x128x64_2x1x1_0_ntn_align8

The components within this name are as follows:

  • cutlass3x: indicates that the kernel was generated through the CUTLASS 3.0 API
  • sm90: indicates that the kernel targets NVIDIA GPUs with compute capability 90
  • tensorop: indicates that the kernel makes use of NVIDIA Tensor Cores (as opposed to simt, which indicates the use of "CUDA cores")
  • s: indicates that the Tensor Core instruction being used accumulates in single precision (as opposed to h, which indicates half precision)
  • 64x128x16gemm: indicates that the shape of the Tensor Core instruction being used (MxNxK) is 64x128x16
  • f16_f16_f32_f16_f16: indicates that the data types for operands A, B, Accumulator, C and D (in that order).
  • 128x128x64: indicates that the thread block shape used in the GEMM (MxNxK) is 128x128x64
  • 2x1x1: indicates that the cluster shape being used is 2x1x1
  • 0: indicates that the kernel uses the CollectiveBuilder's automatic stage calculation to determine the number of pipeline stages in the kernel. Note that 0 does not mean that no stages are used. A nonzero value indicates that automatic stage calculation is not performed and indicates the number of pipeline stages to be used. This 0 is only added to the kernel's procedural name, the profiler will still report the actual stage count when printing the kernel argument details (--stages=N) and kernel discovery will still support filtering through the --stages argument.
  • ntn: indicates that the layouts for operands A, B, and C are column major ("n"; non-transposed), row major ("t"; transposed), and column major, respectively.
  • align8: indicates that the maximum alignment between operands A and B is 8.

Note that in some special cases where the input A/B types do not match that of the MMA instruction's, the MMA facing input type is added to the instruction string as well.

cutlass3x_sm90_tensorop_s64x128x8tf32gemm_f32_f32_f32_f32_f32_128x128x32_2x1x1_0_tnn_align4
  • s64x128x8tf32gemm: indicates that the MMA consumes inputs in tf32 format, and therefore the kernel performs rounding of the f32 values in global memory while loading them into shared memory.

For custom mainloop or epilogue schedules, details of the opted-in schedule are appended to the end of the kernel name. For example,

cutlass3x_sm90_tensorop_h64x128x16gemm_f16_f16_f16_void_f16_128x128x64_1x1x1_0_nnn_align8_warpspecialized_cooperative_epi_tma
  • warpspecialized_cooperative: Mainloop employs a persistent warp-specialized mainloop and kernel schedule.
  • epi_tma: Kernel epilogue employs TMA based vectorization.
  • f16_f16_f16_void_f16: In this case, C type is set to void, indicating that residual matrix support is disabled.

Convolution

The CUTLASS Profiler is capable of executing 2-D and 3-D convolution problems for forwards and backwards operator variants.

The CUTLASS Profiler can be built with cuDNN enabled to use as a reference implementation. If CMake detects the cuDNN library available in the system, it is included as a dependency. This may be explicitly overridden with CMake flag CUTLASS_ENABLE_CUDNN.

$ cmake .. -DCUTLASS_LIBRARY_OPERATIONS=conv2d -DCUTLASS_ENABLE_CUDNN=OFF
...
$ make -j16 cutlass_profiler

Convolution Arguments

$ ./tools/profiler/cutlass_profiler --help --operation=Conv2d

Conv2d

  [enum]      --conv_kind                                       Convolutional operator (fprop, dgrad, wgrad)
  [int]       --n,--input_n                                     Input N dimension of the Conv2d problem space
  [int]       --h,--input_h                                     Input H dimension of the Conv2d problem space
  [int]       --w,--input_w                                     Input W dimension of the Conv2d problem space
  [int]       --c,--input_c                                     Input C dimension of the Conv2d problem space
  [int]       --k,--filter_k                                    Filter K dimension of the Conv2d problem space
  [int]       --r,--filter_r                                    Filter R dimension of the Conv2d problem space
  [int]       --s,--filter_s                                    Filter S dimension of the Conv2d problem space
  [int]       --p,--output_p                                    Output P dimension of the Conv2d problem space
  [int]       --q,--output_q                                    Output Q dimension of the Conv2d problem space
  [int]       --g,--groups                                      Number of convolution groups
  [int]       --pad_h                                           Padding in H direction
  [int]       --pad_w                                           Padding in W direction
  [int]       --stride_h                                        Stride in H direction
  [int]       --stride_w                                        Stride in W direction
  [int]       --dilation_h                                      Dilation in H direction
  [int]       --dilation_w                                      Dilation in W direction
  [tensor]    --Activation                                      Tensor storing the Activation operand
  [tensor]    --Filter                                          Tensor storing the Filter operand
  [tensor]    --Output                                          Tensor storing the Output operand
  [enum]      --conv_mode                                       Convolution filter mode (conv, cross)
  [enum]      --iterator_algorithm,--iterator_algo              Convolution iterator algorithm (analytic, optimized)
  [scalar]    --alpha,--epilogue::alpha                         Epilogue scalar alpha
  [scalar]    --beta,--epilogue::beta                           Epilogue scalar beta
  [enum]      --split_k_mode,--split-k-mode                     SplitK mode for serial or parallel reduction (serial, parallel)
  [int]       --split_k_slices,--split-k-slices                 Number of partitions of K dimension
  [enum]      --eq_gemm_provider,--eq-gemm-provider             Enable profiling equivalent gemm by the following providers (cutlass)
  [enum]      --op_class,--opcode-class                         Class of math instruction (simt, tensorop, wmmatensorop, wmma)
  [enum]      --accum,--accumulator-type                        Math instruction accumulator data type
  [int]       --cta_m,--threadblock-shape::m                    Threadblock shape in the M dimension
  [int]       --cta_n,--threadblock-shape::n                    Threadblock shape in the N dimension
  [int]       --cta_k,--threadblock-shape::k                    Threadblock shape in the K dimension
  [int]       --cluster_m,--cluster-shape::m                    Cluster shape in the M dimension
  [int]       --cluster_n,--cluster-shape::n                    Cluster shape in the N dimension
  [int]       --cluster_k,--cluster-shape::k                    Cluster shape in the K dimension
  [int]       --stages,--threadblock-stages                     Number of stages of threadblock-scoped matrix multiply
  [int]       --warps_m,--warp-count::m                         Number of warps within threadblock along the M dimension
  [int]       --warps_n,--warp-count::n                         Number of warps within threadblock along the N dimension
  [int]       --warps_k,--warp-count::k                         Number of warps within threadblock along the K dimension
  [int]       --inst_m,--instruction-shape::m                   Math instruction shape in the M dimension
  [int]       --inst_n,--instruction-shape::n                   Math instruction shape in the N dimension
  [int]       --inst_k,--instruction-shape::k                   Math instruction shape in the K dimension
  [int]       --min_cc,--minimum-compute-capability             Minimum device compute capability
  [int]       --max_cc,--maximum-compute-capability             Maximum device compute capability

Examples:

Profile a particular convolution (specify all the convolution parameters):
 $ cutlass_profiler --operation=Conv2d --Activation=f16:nhwc --Filter=f16:nhwc --Output=f16 --accumulator-type=f32 --n=32 --h=14 --w=14 --c=8 --k=64 --r=3 --s=3 --pad_h=1 --pad_w=1 --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1

Example CUDA Core Convolution Operation

Example command line for profiling forward propagation convolution kernels on CUDA cores is as follows:

$ ./tools/profiler/cutlass_profiler --kernels=simt_sfprop  --verification-providers=device --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3


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

        Provider: CUTLASS
   OperationKind: conv2d
       Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc

          Status: Success
    Verification: ON
     Disposition: Passed

reference_device: Passed

       Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1  \
                  --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc  \
                  --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1  \
                  --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4  \
                  --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024

           Bytes: 2055798784  bytes
           FLOPs: 118482796544  flops

         Runtime: 8.13237  ms
          Memory: 235.431 GiB/s

            Math: 14569.3 GFLOP/s

Example Tensor Core Convolution Operation

Example command line for profiling forward propagation convolution kernels runing on Tensor Cores is as follows:

$ ./tools/profiler/cutlass_profiler --kernels=tensorop*fprop  --verification-providers=device --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3



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

        Provider: CUTLASS
   OperationKind: conv2d
       Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_64x4_nhwc

          Status: Success
    Verification: ON
     Disposition: Passed

reference_device: Passed

       Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1  \
                  --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc  \
                  --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1  \
                  --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=64 --stages=4  \
                  --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024

           Bytes: 1130659840  bytes
           FLOPs: 118482796544  flops

         Runtime: 0.945071  ms
          Memory: 1114.21 GiB/s

            Math: 125369 GFLOP/s

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