179 citations · 267 across the 5 of their papers we have counts for
6 papers
OpenZL: Using Graphs to Compress Smaller and Faster
Yann Collet, Nick Terrell, W. Felix Handte +10
In the last few decades, research techniques have improved lossless compression ratios by significantly increasing processing time. However, these techniques have not gained popula…
OpenZL: A Graph-Based Model for Compression
Yann Collet, Nick Terrell, W. Felix Handte +10
Research techniques in the last decade have improved lossless compression ratios by significantly increasing processing time. These techniques have remained obscure because product…
Profile Guided Optimization without Profiles: A Machine Learning Approach
Nadav Rotem, Chris Cummins
Profile guided optimization is an effective technique for improving the optimization ability of compilers based on dynamic behavior, but collecting profile data is expensive, cumbe…
Warrior1: A Performance Sanitizer for C++
Nadav Rotem, Lee Howes, David Goldblatt
This paper presents Warrior1, a tool that detects performance anti-patterns in C++ libraries. Many programs are slowed down by many small inefficiencies. Large-scale C++ applicatio…
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
Jongsoo Park, Maxim Naumov, Protonu Basu +25
The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models…
Glow: Graph Lowering Compiler Techniques for Neural Networks
Nadav Rotem, Jordan Fix, Saleem Abdulrasool +15
This paper presents the design of Glow, a machine learning compiler for heterogeneous hardware. It is a pragmatic approach to compilation that enables the generation of highly opti…