![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "CUTLASS Profiler") [README](/README.md#documentation) > **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 sources are stored in ``` tools/ profiler/ ``` and may be compiled as follows. ``` $ make cutlass_profiler -j ``` To 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=75 -DCUTLASS_LIBRARY_KERNELS=all ... $ make cutlass_profiler -j ``` 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= CUTLASS operation to profile. --kernels= Names of individual kernels to execute. All are executed if not specified. Device: --device= CUDA Device ID Initialization: --initialization= Enables initialization (default: true). If false, device memory is not initialized after allocation. --initialization-provider= Selects 'device' or 'host' initialization. --dist= Data distribution of input tensors --seed= Random number generator seed. Used to enforce deterministic initialization. Library: --library-algo-mode= Indicates algorithm mode used to call libraries such as cuBLAS and cuDNN. mode={default*,matching,best} --library-algos= If --algorithm-mode=best, permits specifying a selection of algorithms. Profiling: --profiling-iterations= Number of iterations to profile each kernel. If zero, kernels are launched up to the profiling duration. --warmup-iterations= Number of iterations to execute each kernel prior to profiling. --sleep-duration= Number of ms to sleep between profiling periods (ms) --profiling-enabled= If true, profiling is actually conducted. --providers= List of providers to be profiled for performance Verification: --verification-enabled= Whether to perform verification checks. --epsilon= Error threshold. Setting to zero (default) requires bit-level equivalence. --nonzero-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= List of providers used to verify result. (default: cublas) Report: --append= If true, result is appended to possibly existing file. Otherwise, any existing file is overwritten. --output= Path to output file for machine readable results. --report-not-run= If true, reports the status of all kernels including those that do not satisfy the given arguments. --tags= Inserts leading columns in output table and uniform values for each column. Useful for generating pivot tables. --verbose= If true (default), prints human-readable text to stdout. About: --version CUTLASS 2.0.0 built on Nov 19 2019 at 13:01:00 Operations: --operation= 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 ``` ## 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. 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.csv ``` ## Example SGEMM Example command line for profiling SGEMM kernels is as follows: ``` $ ./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 ``` Note, the arguments which appear in the output may be used as command line parameters for subsequent invocations. ## Example Tensor Core 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 ============================= Problem ID: 1 Provider: CUTLASS Operation: cutlass_turing_h1688gemm_128x128_nt Disposition: Passed Status: Success Arguments: --m=4352 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f16:column --alpha=1 --beta=0 --split_k_slices=1 \ --batch_count=1 --op_class=tensorop --accum=f16 --cta_m=128 --cta_n=128 --cta_k=32 --stages=2 \ --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75 --max_cc=1024 \ Bytes: 52428800 bytes FLOPs: 146064539648 flops Runtime: 1.51255 ms Memory: 32.2821 GiB/s Math: 96568.7 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=` command line option as shown: ``` $ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn --m=4352 --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=:` option. One or more tags may be specified using a comma-delimited list. ``` $ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sgemm_128x128_nn \ --m=4352 --n=4096 --k=8:4096:8 --output=report.csv \ --tags=cutlass:2.0,date:2019-11-19 ``` # Copyright Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved. ``` Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials 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 permission. 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