README > 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 may be compiled with:
$ make cutlass_profiler -jTo limit compilation time, only one tile size (128x128) 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 -jEnabling 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.
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={profile*,single,dry,enumerate} Regular profiling, single kernel mode only, or no profiling.
--device-info Prints information on all GPUs present in the system
--operation=<operation_kind> CUTLASS operation to profile.
--kernels=<kernel names> Names of individual kernels to execute. All are executed if not specified.
Device:
--device=<int> CUDA Device ID
Initialization:
--initialization=<bool> Enables initialization (default: true). If false, device memory is
not initialized after allocation.
--initialization-provider=<provider> Selects 'device' or 'host' initialization.
--dist=<distribution> Data distribution of input tensors
--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:
--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.
--providers=<providers> List of providers to be profiled for performance
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={*never,incorrect,always} Specifies when to save the GEMM inputs and results to the filesystem.
--verification-providers=<providers> List of providers used to verify result. (default: cublas)
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.
--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> If true (default), prints human-readable text to stdout.
About:
--version CUTLASS 2.2.0 built on Jun 8 2020 at 07:59:33
Operations:
--operation=<operation_name> Specifies a particular operation to run or print the usage statement.
gemm General matrix-matrix product. D = alpha * A*B + beta * C
For more details about a particular operation, specify the operation name with --help.
Example:
$ ./tools/profiler/cutlass_profiler --operation=Gemm --help
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. gemm, batched, ...)
[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
[int] --split_k_slices Number of partitions of K dimension
[int] --batch_count Number of GEMMs computed in one batch
[enum] --op_class,--opcode-class Class of math instruction (SIMT or TensorOp).
[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] --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 problem size:
$ ./tools/profiler/cutlass_profiler --operation=Gemm --m=1024 --n=1024 --k=128
Schmoo over problem size and beta:
$ ./tools/profiler/cutlass_profiler --operation=Gemm --m=1024:4096:256 --n=1024:4096:256 --k=128:8192:128 --beta=0,1,2
Schmoo over accumulator types:
$ ./tools/profiler/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):
$ ./tools/profiler/cutlass_profiler --operation=Gemm --A=f16:column --B=*:row
Using various input value distribution:
$ ./tools/profiler/cutlass_profiler --operation=Gemm --dist=uniform,min:0,max:3
$ ./tools/profiler/cutlass_profiler --operation=Gemm --dist=gaussian,mean:0,stddev:3
$ ./tools/profiler/cutlass_profiler --operation=Gemm --dist=sequential,start:0,delta:1
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):
$ ./tools/profiler/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:
$ ./tools/profiler/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.csvExample 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/sNote, the arguments which appear in the output may be used as command line parameters for subsequent invocations.
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/sAll 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=8:4096:8By 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.csvTo 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:2020-06-08Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
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