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--- |
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dataset_info: |
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features: |
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- name: MWG |
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dtype: int64 |
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- name: NWG |
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dtype: int64 |
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- name: KWG |
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dtype: int64 |
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- name: MDIMC |
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dtype: int64 |
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- name: NDIMC |
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dtype: int64 |
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- name: MDIMA |
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dtype: int64 |
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- name: NDIMB |
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dtype: int64 |
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- name: KWI |
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dtype: int64 |
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|
- name: VWM |
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dtype: int64 |
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|
- name: VWN |
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dtype: int64 |
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- name: STRM |
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dtype: int64 |
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|
- name: STRN |
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dtype: int64 |
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- name: SA |
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dtype: int64 |
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- name: SB |
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dtype: int64 |
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- name: Run_time |
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dtype: float64 |
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- name: __index_level_0__ |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 24748672 |
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num_examples: 193349 |
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download_size: 3916323 |
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dataset_size: 24748672 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Information |
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SGEMM GPU kernel performance Data Set available for download at https://archive.ics.uci.edu/ml/datasets/SGEMM+GPU+kernel+performance |
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We performed some filtering on this dataset. |
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Here is the original information of this dataset: |
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This data set measures the running time of a matrix-matrix product A*B = C, where all matrices have size 2048 x 2048, using a parameterizable SGEMM GPU kernel with 241600 possible parameter combinations. For each tested combination, 4 runs were performed and their results are reported as the 4 last columns. All times are measured in milliseconds*. |
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There are 14 parameter, the first 10 are ordinal and can only take up to 4 different powers of two values, and the 4 last variables are binary. Out of 1327104 total parameter combinations, only 241600 are feasible (due to various kernel constraints). This data set contains the results for all these feasible combinations. |
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The experiment was run on a desktop workstation running Ubuntu 16.04 Linux with an Intel Core i5 (3.5GHz), 16GB RAM, and a NVidia Geforce GTX 680 4GB GF580 GTX-1.5GB GPU. We use the 'gemm_fast' kernel from the automatic OpenCL kernel tuning library 'CLTune' (https://github.com/CNugteren/CLTune). |
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* Note: for this kind of data sets it is usually better to work with the logarithm of the running times (see e.g. Falch and Elster, 'Machine learning-based auto-tuning for enhanced performance portability of OpenCL applications', 2015). |
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# Download |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("Rosykunai/SGEMM_GPU_performance") |
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``` |
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# Reference |
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- Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola. |
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- Sobol Tensor Trains for Global Sensitivity Analysis. |
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- In arXiv Computer Science / Numerical Analysis e-prints, 2017 |
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- Cedric Nugteren and Valeriu Codreanu. |
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- CLTune: A Generic Auto-Tuner for OpenCL Kernels. |
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- In: MCSoC: 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip. IEEE, 2015 |
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