Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data

Identifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data that can have a low signal-to-noise ratio for various reasons including low coverage or low cell count. To denoise and identify accessible chromatin regions from noisy ATAC-seq data, use of deep learning on 1D data - using large filter sizes, long tensor widths, and/or dilation - has recently been proposed. Convolutions over 1D data consume a majority of the runtime in these methods. However, existing implementations of the 1D convolution layer for CPUs and GPUs fail to efficiently use the underlying architecture especially in the case of large filter sizes, long tensor widths, and dilation. Here, we present ways to accelerate the end-to-end training performance of these deep learning based methods. We evaluate our approach on the recently released AtacWorks toolkit using modern CPUs. Compared to AtacWorks running on an Nvidia DGX-1 box with 8 V100 GPUs, we get up to 2.27× speedup using just 16 CPU sockets. To achieve this, we build an efficient 1D dilated convolution layer and demonstrate reduced precision (BFloat16) training and nearly linear scaling from 1 to 16 sockets. Code Availability: https://github.com/IntelLabs/Trans-Omics-Acceleration-Library/tree/ATAC-Seq/applications/ATAC-Seq