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Cache misses analysis by means of data mining methods
Pavel Kordík, Ivan Šimeček
kordikp, [email protected]
Department of Computer Science, Faculty of Electrical Engineering, Czech Technical
University, Technická 2, 166 27 Prague 6, Czech Republic
It is really difficult to predict the cache behavior even for a simple program (for details see [1,
2]) because every modern CPU use a complex memory hierarchy, which consists of levels of
cache memories. One challenging task is to predict the exact number of cache misses during
the sparse matrix-vector multiplication (shortly SpMV). Due to matrix sparsity, the memory
access patterns are irregular and the utilization of a cache suffers from low spatial and
temporal locality. It is really difficult to predict the cache behavior for all cases of input
parameters. The cache misses data were also analyzed by means of data mining methods. This
is the main topic of this paper and we will discuss the data mining analysis bellow in the more
detailed form.
At first the data had to be preprocessed. It was transformed into the native format of
the data mining application WEKA [3] where almost all experiments have been performed.
We tried to predict the number of cache misses from input variables (read operations,
size of matrices, bandwidth etc.). The data mining methods from the category of decision
trees, bayes classifiers and neural networks were used. The detailed description of these
methods can be found in [4].
Because WEKA is designed to solve mainly classification problems, we had divided
the output attribute “number of cache misses” into 10 intervals (classes). We achieved just
62% classification accuracy by Bayes based methods (Bayes Net, Naive Bayes Simple, etc.).
Other methods were unable to give any results because of memory demands.
When we studied why the performance is so low, we found out that the data should be further
preprocessed. New data set were created from the original one by leaving out redundant
measurements with low additional information. It consisted from 1500 records that
representatively described number of cache misses (almost uniformly distributed). With this
new data set we achieved for data mining methods following classification accuracies:
 Multi Layer Perceptron MLP (92%),
 Radial Basis Function network RBF (93%),
 Decision tree C4.5 (95%),
 etc.
This accuracy is perfect so we can conclude that using data mining methods, we can estimate
the number of cache missed with relatively low error.
Data mining also allows us to find out which input variables (features) are most
important in estimating the number of cache misses (feature ranking).
Again, we performed several experiments with method for feature ranking available in
WEKA. The results show that the most important feature is
 readCount (number of read operations),
 nonZero (number of nonzero elements in the matrix),

width (the bandwidth) feature.
Surprisingly the size (size of the matrix) feature was on the fourth position in average –
gaining much lower significance than we expected.
We have implemented a simple cache analyzer, collected data about cache misses and
along with analytical estimation of cache misses we analyzed data by means of data mining
methods. The results of data mining analysis are very promising for our further research
leading to models reducing the number of cache misses.
We would like to thank to students Miloš Klíma and Michal Chalupník for performing
some of the above described experiments as their semester projects from the course Neural
networks and neurocomputers.
References:
[1] K. BEYLS, E. D'HOLLANDER: Exact compile-time calculation of data cache behavior,
Proceedings of PDCS'01, 2001, pp. 617-662.
[2] X. VERA, J. XUE: Efficient Compile-Time Analysis of Cache Behavior for Programs
with IF Statements, International Conference on Algorithms And Architectures for
Parallel Processing, Beijing, 2002.
[3] THE
UNIVERSITY
OF
WAIKOTO:
WEKA
data
mining
software,
http://www.cs.waikato.ac.nz/ml/weka/.
[4] I. H. WITTEN, F. EIBE: Data mining: practical machine learning tools and techniques,
2nd ed. Morgan Kaufmann series in data management systems, 2005, ISBN: 0-12088407-0.
This research has been supported by GA AV ČR grant No. IBS 3086102, by IGA CTU FEE
grant No. CTU0409313, and by MŠMT under research program MSM6840770014.