  Graphics Processing Units (GPUs) were once used solely for graphical
computation tasks but with the increase in the use of machine learning
applications, the use of GPUs to perform general-purpose computing has
increased in the last few years. GPUs employ a massive amount of threads, that
in turn achieve a high amount of parallelism, to perform tasks. Though GPUs
have a high amount of computation power, they face the problem of cache
contention due to the SIMT model that they use. A solution to this problem is
called "cache bypassing". This paper presents a predictive model that analyzes
the access patterns of various machine learning algorithms and determines
whether certain data should be stored in the cache or not. It presents insights
on how well each model performs on different datasets and also shows how
minimizing the size of each model will affect its performance The performance
of most of the models were found to be around 90% with KNN performing the best
but not with the smallest size. We further increase the features by splitting
the addresses into chunks of 4 bytes. We observe that this increased the
performance of the neural network substantially and increased the accuracy to
99.9% with three neurons.
