From 57f30a9309ff2e5e3b32731a785bf38b01d1fd69 Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Tue, 8 Sep 2020 14:03:51 +0100 Subject: COMPMID-3767: Align documentation with trademark rules Change-Id: Id2794f2142e21522283a423f0208dc1022036c79 Signed-off-by: Michele Di Giorgio Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3942 Reviewed-by: Georgios Pinitas Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins --- examples/gemm_tuner/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) (limited to 'examples/gemm_tuner') diff --git a/examples/gemm_tuner/README.md b/examples/gemm_tuner/README.md index 1effd2f7e1..73bddc9239 100644 --- a/examples/gemm_tuner/README.md +++ b/examples/gemm_tuner/README.md @@ -34,7 +34,7 @@ what kernel and subsequently what configurations for that kernels are the most p ### Step1: Prepare the shape and configs files 1. We first need to identify the shapes that we are interested in and store them in a csv file, say *gemm_shapes.csv*. 2. Then we need to specify a set of good GEMMConfig candidates for each kernel in 3 separate csv files (this requires - some prior heuristics, but can be provided by the ACL developers upon requests, based on your target device). + some prior heuristics, but can be provided by the Compute Library developers upon requests, based on your target device). Say we have *gemm_configs_native.csv", "gemm_configs_reshaped.csv" and "gemm_configs_reshaped_only_rhs.csv". @@ -42,9 +42,9 @@ what kernel and subsequently what configurations for that kernels are the most p ### Step2: Push relevant files to the target device All the files that need to be present on the target device are: -* benchmark script: \/examples/gemm_tuner/benchmark_gemm_examples.sh +* benchmark script: \/examples/gemm_tuner/benchmark_gemm_examples.sh * shapes and configs csv files: gemm_shapes.csv, gemm_configs_native.csv, gemm_configs_reshaped_only_rhs.csv, gemm_configs_reshaped.csv -* Example benchmark binaries: \/build/tests/gemm_tuner/benchmark_cl_gemm* +* Example benchmark binaries: \/build/tests/gemm_tuner/benchmark_cl_gemm* ### Step3: Collect benchmark data With these files on device, we can collect benchmark data using the script. Assume all the example binaries are pushed @@ -64,7 +64,7 @@ but you may need to change the output folder for each repeat 1. After benchmarking, we pull the benchmark data, the *results* folder, from the target device to our host machine 2. We use the GemmTuner.py script to give us the heuristics ``` - python3 /examples/gemm_tuner/GemmTuner.py -b ./results -o heuristics + python3 /examples/gemm_tuner/GemmTuner.py -b ./results -o heuristics ``` When it's finished, there should be 4 json files in the *heuristics* folder @@ -76,12 +76,12 @@ passing a lower value to *-t \* to the GemmTuner.py script. * A target device to be tuned, plus the following on the device: * Android or Linux OS * Bash shell - * Built ACL with benchmark examples binaries + * Built Compute Library with benchmark examples binaries * benchmark_gemm_examples.sh script * gemm shape file A csv file containing the **GEMMParam search list**. This is the list of GEMMParams/gemm shapes that we're - interested in (For more details see Approach section). The default list is prepared by ACL developers in advance + interested in (For more details see Approach section). The default list is prepared by Compute Library developers in advance and can be provided on request. The format is described as: @@ -105,7 +105,7 @@ passing a lower value to *-t \* to the GemmTuner.py script. * gemm config file A csv file containing the **GEMMConfig search list**. This is the list of candidate GEMMConfigs among which we search for the optimal one. **Note that we have a different list for each strategy.** - The default lists are prepared by ACL developers in advance and can be provided on request. + The default lists are prepared by Compute Library developers in advance and can be provided on request. The format of the file for each strategy is the same: -- cgit v1.2.1