Meta Research

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize...

This work proposes ACT, an architectural carbon footprint modeling framework, to enable carbon characterization and sustainability-driven early design space exploration.

This paper presents Meta’s end-to-end DSI pipeline, composed of a central data warehouse built on distributed storage and a Data PreProcessing Service that scales to eliminate data stalls.

In this paper, we describe a schema-first approach to application telemetry that is being implemented at Meta. It allows the observability platforms to capture metadata about...

This paper extends the bitcode summary to perform a global inlining analysis to find inline candidates for saving the code size. Using this summary information, a pre-inliner...

In this work, we examine ways in which machine learning can be used to accelerate enumerative program synthesis. We present a deep-learning-based model to predict the sequence of...

In this paper, we present Neo, a software-hardware co-designed system for high-performance distributed training of large-scale DLRMs. Neo employs a novel 4D parallelism strategy...

In this paper, we report on some of the unique challenges when analyzing CMake files at Facebook.

Machine learning (ML) models play an increasingly prevalent role in many software engineering tasks. However, because most models are now powered by opaque deep neural networks...

In this work, we provide an in-depth characterization study of the performance overhead for running Transformer models with secure multi-party computation (MPC). MPC is a...