148 citations · 428 across the 18 of their papers we have counts for
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Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
Evangelos Georganas, Dhiraj Kalamkar, Kirill Voronin +5
During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the pro…
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
Dhiraj Kalamkar, Evangelos Georganas, Sudarshan Srinivasan +3
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks. Often this discussion is held in the context…
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee +4
Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image…
On Scale-out Deep Learning Training for Cloud and HPC
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar +8
The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes. This can only be achieved thr…
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere +5
We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior. We perform a de…