Efficient large-scale neural network training and inference on commodity CPU hardware is of immense practical significance in democratizing deep learning (DL) capabilities. Presently, the process of training massive models consisting of hundreds of millions to billions of parameters requires the extensive use of specialized hardware accelerators, such as GPUs, which are only accessible to a limited number of institutions with considerable financial resources. Moreover, there is often an alarming carbon footprint associated with training and deploying these models. In this paper, we take a step towards addressing these challenges by introducing BOLT, a sparse deep learning library for training large-scale search and recommendation models on standard CPU hardware. BOLT provides a flexible, high-level API for constructing models that will be familiar to users of existing popular DL frameworks. By automatically tuning specialized hyperparameters, BOLT also abstracts away the algorithmic details of sparse network training. We evaluate BOLT on a number of information retrieval tasks including product recommendations, text classification, graph neural networks, and personalization. We find that our proposed system achieves competitive performance with state-of-the-art techniques at a fraction of the cost and energy consumption and an order-of-magnitude faster inference time. BOLT has also been successfully deployed by multiple businesses to address critical problems, and we highlight one customer case study in the field of e-commerce.

In the last decade, large-scale deep learning has fundamentally transformed industrial recommendation systems. However, this revolutionary technology remains prohibitively expensive due to the need for costly and scarce specialized hardware, such as Graphics Processing Units (GPUs), to train and serve models. In this talk, we share our multi-year journey at ThirdAI in developing efficient neural recommendation models that can be trained and deployed on commodity CPU machines without the need for costly accelerators like GPUs. In particular, we discuss the limitations of the current GPU-based ecosystem in machine learning, why recommendation systems are amenable to the strengths of CPU devices, and present results from our efforts to translate years of academic research into a deployable system that fundamentally shifts the economics of training and operating large-scale machine learning models.

A popular approach to reduce the size of a massive dataset is to apply efficient online sampling to the stream of data as it is read or generated. Online sampling routines are currently restricted to variations of reservoir sampling, where each sample is selected uniformly and independently of other samples. This renders them unsuitable for large-scale applications in computational biology, such as metagenomic community profiling and protein function annotation, which suffer from severe class imbalance. To maintain a representative and diverse sample, we must identify and preferentially select data that are likely to belong to rare classes. We argue that existing schemes for diversity sampling have prohibitive overhead for large-scale problems and high-throughput streams. We propose an efficient sampling routine that uses an online representation of the data distribution as a prefilter to retain elements from rare groups. We apply this method to several genomic data analysis tasks and demonstrate significant speedup in downstream analysis without sacrificing the quality of the results. Because our algorithm is 2x faster and uses 1000x less memory than coreset, reservoir and sketch-based alternatives, we anticipate that it will become a useful preprocessing step for applications with large-scale streaming data.