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Deep learning is an important component of big-data analytic tools andintelligent applications, such as, self-driving cars, computer vision, speechrecognition, or precision medicine. However, the training process iscomputationally intensive, and often requires a large amount of time ifperformed sequentially. Modern parallel computing systems provide thecapability to reduce the required training time of deep neural networks. Inthis paper, we present our parallelization scheme for training convolutionalneural networks (CNN) named Controlled Hogwild with Arbitrary Order ofSynchronization (CHAOS). Major features of CHAOS include the support for threadand vector parallelism, non-instant updates of weight parameters duringback-propagation without a significant delay, and implicit synchronization inarbitrary order. CHAOS is tailored for parallel computing systems that areaccelerated with the Intel Xeon Phi. We evaluate our parallelization approachempirically using measurement techniques and performance modeling for variousnumbers of threads and CNN architectures. Experimental results for the MNISTdataset of handwritten digits using the total number of threads on the Xeon Phishow speedups of up to 103x compared to the execution on one thread of the XeonPhi, 14x compared to the sequential execution on Intel Xeon E5, and 58xcompared to the sequential execution on Intel Core i5.

CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi