  Machine learning algorithms, such as Support Vector Machine (SVM) and Deep
Neural Network (DNN), have gained a lot of interests recently. When training a
machine learning algorithm, randomly shuffle all the training data can improve
the testing accuracy and boost the convergence rate. Nevertheless, realizing
training data random shuffling in a real system is not a straightforward
process due to the slow random accesses in hard disk drive (HDD). To avoid
frequent random disk access, the effect of random shuffling is often limited in
existing approaches. With the emerging non-volatile memory-based storage
device, such as Intel Optane SSD, which provides fast random accesses, we
propose a lightweight implementation of random shuffling (LIRS) to randomly
shuffle the indexes of the entire training dataset, and the selected training
instances are directly accessed from the storage and packed into batches.
Experimental results show that LIRS can reduce the total training time of SVM
and DNN by 49.9% and 43.5% on average, and improve the final testing accuracy
on DNN by 1.01%.
