  Understanding the bottlenecks in implementing stochastic gradient descent
(SGD)-based distributed support vector machines (SVM) algorithm is important in
training larger data sets. The communication time to do the model
synchronization across the parallel processes is the main bottleneck that
causes inefficiency in the training process. The model synchronization is
directly affected by the mini-batch size of data processed before the global
synchronization. In producing an efficient distributed model, the communication
time in training model synchronization has to be as minimum as possible while
retaining a high testing accuracy. The effect from model synchronization
frequency over the convergence of the algorithm and accuracy of the generated
model must be well understood to design an efficient distributed model. In this
research, we identify the bottlenecks in model synchronization in parallel
stochastic gradient descent (PSGD)-based SVM algorithm with respect to the
training model synchronization frequency (MSF). Our research shows that by
optimizing the MSF in the data sets that we used, a reduction of 98\% in
communication time can be gained (16x - 24x speed up) with respect to
high-frequency model synchronization. The training model optimization discussed
in this paper guarantees a higher accuracy than the sequential algorithm along
with faster convergence.
