Update README.md

README.md

@@ -45,3 +45,22 @@ configs:
   - split: train
     path: data/train-*
 ---
+# Information
+We performed some filtering on this dataset.
+Here is the original information of this dataset:
+
+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*.
+
+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.
+
+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).
+
+* 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).
+
+# Reference
+- Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola.
+- Sobol Tensor Trains for Global Sensitivity Analysis.
+- In arXiv Computer Science / Numerical Analysis e-prints, 2017
+- Cedric Nugteren and Valeriu Codreanu.
+- CLTune: A Generic Auto-Tuner for OpenCL Kernels.
+- In: MCSoC: 9th International Symposium on Embedded Multicore/Many-core Systems-on-Chip. IEEE, 2015